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Machine learning is a branch of artificial intelligence (AI). It uses statistical models and algorithms to get computers to imitate the way humans learn and improve their learning automatically.
Students who study machine learning in college focus on advanced data science, math and computer science topics. You'll have to master many complicated subjects to succeed in your machine learning classes.
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Machine Learning Topics
You can get help on any aspect of machine learning, including:
 Artificial intelligence : Artificial intelligence deals with developing computers to perform tasks that typically require human intelligence, such as decision making, speech recognition and visual perception.
 Computer science : Computer science is the study of computers, including their software and hardware.
 Algorithms : An algorithm is a sequence of specific programming steps used to solve problems, particularly computer problems.
 Python : Python is a highlevel computer programming language used for machine learning.
 Supervised learning: Supervised learning is a subcategory of machine learning that uses labeled datasets to teach algorithms to classify data or predict outcomes.
 Unsupervised learning: Unsupervised learning is a machine learning subcategory that uses algorithms to learn patterns from untagged data.
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Over the last ten years, machine learning has evolved as one of the most indemand subjects in computer science. Increasingly, students are trying to learn and get hold of this new subject. As you dive deeper into machine learning, you might encounter certain challenges in understanding the subject or get stuck while working on university assignments. Machine learning assignments require a large amount of data collection and analysis. If you cannot take this pressure, you can take the expert machine learning help online.
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What is Machine Learning?
Machine learning is an associate degree application of computer science that gives systems the flexibility to mechanically learn and improve from expertise while not being expressly programmed. The process of learning begins with observations or information, like examples, direct expertise, or instruction, to appear for patterns in the information and build higher selections within the future supported by the examples that we offer. The first aim is to permit the computers to learn mechanically while not human intervention or help and alter actions consequently.
Key Topics in ML
The most difficult concept to learn in Machine Learning is the classification of ML. ML is classified into 3 categories, let us understand them below:
 Supervised Learning: Supervised learning is a variety of machine learning methodologies during which we offer sample tagged information to the machine learning system to coach it, and thereon basis, it predicts the output. Supervised learning relies on supervision, and it's similar to once a student learns things within the supervision of the teacher.
 Unsupervised Learning: Unsupervised learning is a learning methodology during which a machine learns with none supervising. The coaching is provided to the machine with the set of knowledge that has not been tagged or classified, and also the formula must act thereon knowledge with none supervising.
 Reinforcement Learning: Reinforcement learning is a feedbackbased learning technique, during which a learning agent gets a gift for every right action and gets a penalty for every wrong action.
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Foundations of Machine Learning
Instructor  , Office of the CTO at Bloomberg 

Understand the Concepts, Techniques and Mathematical Frameworks Used by Experts in Machine Learning
About This Course
Bloomberg presents "Foundations of Machine Learning," a training course that was initially delivered internally to the company's software engineers as part of its "Machine Learning EDU" initiative. This course covers a wide variety of topics in machine learning and statistical modeling. The primary goal of the class is to help participants gain a deep understanding of the concepts, techniques and mathematical frameworks used by experts in machine learning. It is designed to make valuable machine learning skills more accessible to individuals with a strong math background, including software developers, experimental scientists, engineers and financial professionals.
The 30 lectures in the course are embedded below, but may also be viewed in this YouTube playlist . The course includes a complete set of homework assignments, each containing a theoretical element and implementation challenge with support code in Python, which is rapidly becoming the prevailing programming language for data science and machine learning in both academia and industry. This course also serves as a foundation on which more specialized courses and further independent study can build.
Please fill out this short online form to register for access to our course's Piazza discussion board. Applications are processed manually, so please be patient. You should receive an email directly from Piazza when you are registered. Common questions from this and previous editions of the course are posted in our FAQ .
The first lecture, Black Box Machine Learning , gives a quick start introduction to practical machine learning and only requires familiarity with basic programming concepts.
Highlights and Distinctive Features of the Course Lectures, Notes, and Assignments
 Geometric explanation for what happens with ridge, lasso, and elastic net regression in the case of correlated random variables.
 Investigation of when the penalty (Tikhonov) and constraint (Ivanov) forms of regularization are equivalent.
 Concise summary of what we really learn about SVMs from Lagrangian duality.
 Proof of representer theorem with simple linear algebra, emphasizing it as a way to reparametrize certain objective functions.
 Guided derivation of the math behind the classic diamond/circle/ellipsoids picture that "explains" why L1 regularization gives sparsity (Homework 2, Problem 5)
 From scrach (in numpy) implementation of almost all major ML algorithms we discuss: ridge regression with SGD and GD (Homework 1, Problems 2.5, 2.6 page 4), lasso regression with the shooting algorithm (Homework 2, Problem 3, page 4), kernel ridge regression (Homework 4, Problem 3, page 2), kernelized SVM with Kernelized Pegasos (Homework 4, 6.4, page 9), L2regularized logistic regression (Homework 5, Problem 3.3, page 4),Bayesian Linear Regession (Homework 5, problem 5, page 6), multiclass SVM (Homework 6, Problem 4.2, p. 3), classification and regression trees (without pruning) (Homework 6, Problem 6), gradient boosting with trees for classification and regression (Homework 6, Problem 8), multilayer perceptron for regression (Homework 7, Problem 4, page 3)
 Repeated use of a simple 1dimensional regression dataset, so it's easy to visualize the effect of various hypothesis spaces and regularizations that we investigate throughout the course.
 Investigation of how to derive a conditional probability estimate from a predicted score for various loss functions, and why it's not so straightforward for the hinge loss (i.e. the SVM) (Homework 5, Problem 2, page 1)
 Discussion of numerical overflow issues and the logsumexp trick (Homework 5, Problem 3.2)
 Selfcontained introduction to the expectation maximization (EM) algorithm for latent variable models.
 Develop a general computation graph framework from scratch, using numpy, and implement your neural networks in it.
Prerequisites
The quickest way to see if the mathematics level of the course is for you is to take a look at this mathematics assessment , which is a preview of some of the math concepts that show up in the first part of the course.
 Solid mathematical background , equivalent to a 1semester undergraduate course in each of the following: linear algebra, multivariate differential calculus, probability theory, and statistics. The content of NYU's DSGA1002: Statistical and Mathematical Methods would be more than sufficient, for example.
 Python programming required for most homework assignments.
 Recommended: At least one advanced, proofbased mathematics course
 Recommended: Computer science background up to a "data structures and algorithms" course
 (HTF) refers to Hastie, Tibshirani, and Friedman's book The Elements of Statistical Learning
 (SSBD) refers to ShalevShwartz and BenDavid's book Understanding Machine Learning: From Theory to Algorithms
 (JWHT) refers to James, Witten, Hastie, and Tibshirani's book An Introduction to Statistical Learning
With the abundance of welldocumented machine learning (ML) libraries, programmers can now "do" some ML, without any understanding of how things are working. And we'll encourage such "black box" machine learning... just so long as you follow the procedures described in this lecture. To make proper use of ML libraries, you need to be conversant in the basic vocabulary, concepts, and workflows that underlie ML. We'll introduce the standard ML problem types (classification and regression) and discuss prediction functions, feature extraction, learning algorithms, performance evaluation, crossvalidation, sample bias, nonstationarity, overfitting, and hyperparameter tuning. If you're already familiar with standard machine learning practice, you can skip this lecture.  
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We have an interactive discussion about how to reformulate a real and subtly complicated business problem as a formal machine learning problem. The real goal isn't so much to solve the problem, as to convey the point that properly mapping your business problem to a machine learning problem is both extremely important and often quite challenging. This course doesn't dwell on how to do this mapping, though see Provost and Fawcett's book in the references.  
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This is where our "deep study" of machine learning begins. We introduce some of the core building blocks and concepts that we will use throughout the remainder of this course: input space, action space, outcome space, prediction functions, loss functions, and hypothesis spaces. We present our first machine learning method: empirical risk minimization. We also highlight the issue of overfitting, which may occur when we find the empirical risk minimizer over too large a hypothesis space.  
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A recurring theme in machine learning is that we formulate learning problems as optimization problems. Empirical risk minimization was our first example of this. To do learning, we need to do optimization. In this lecture we cover stochastic gradient descent, which is today's standard optimization method for largescale machine learning problems.  
We introduce the notions of approximation error, estimation error, and optimization error. While these concepts usually show up in more advanced courses, they will help us frame our understanding of the tradeoffs between hypothesis space choice, data set size, and optimization run times. In particular, these concepts will help us understand why "better" optimization methods (such as quasiNewton methods) may not find prediction functions that generalize better, despite finding better optima.  
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We introduce "regularization", our main defense against overfitting. We discuss the equivalence of the penalization and constraint forms of regularization (see ), and we introduce L1 and L2 regularization, the two most important forms of regularization for linear models. When L1 and L2 regularization are applied to linear least squares, we get "lasso" and "ridge" regression, respectively. We compare the "regularization paths" for lasso and ridge regression, and give a geometric argument for why lasso often gives "sparse" solutions. Finally, we present "coordinate descent", our second major approach to optimization. When applied to the lasso objective function, coordinate descent takes a particularly clean form and is known as the "shooting algorithm".  
We continue our discussion of ridge and lasso regression by focusing on the case of correlated features, which is a common occurrence in machine learning practice. We will see that ridge solutions tend to spread weight equally among highly correlated features, while lasso solutions may be unstable in the case of highly correlated features. Finally, we introduce the "elastic net", a combination of L1 and L2 regularization, which ameliorates the instability of L1 while still allowing for sparsity in the solution. (Credit to Brett Bernstein for the excellent graphics.)  
We start by discussing absolute loss and Huber loss. We consider them as alternatives to the square loss that are more robust to outliers. Next, we introduce our approach to the classification setting, introducing the notions of score, margin, and marginbased loss functions. We discuss basic properties of the hinge loss (i.e SVM loss), logistic loss, and the square loss, considered as marginbased losses. The interplay between the loss function we use for training and the properties of the prediction function we end up with is a theme we will return to several times during the course.  
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We introduce the basics of convex optimization and Lagrangian duality. We discuss weak and strong duality, Slater's constraint qualifications, and we derive the complementary slackness conditions. As far as this course is concerned, there are really only two reasons for discussing Lagrangian duality: 1) The complementary slackness conditions will imply that SVM solutions are "sparse in the data" ( ), which has important practical implications for the kernelized SVMs (see the ). 2) Strong duality is a sufficient condition for the equivalence between the penalty and constraint forms of regularization (see ). This mathematically intense lecture may be safely skipped.  
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We define the softmargin support vector machine (SVM) directly in terms of its objective function (L2regularized, hinge loss minimization over a linear hypothesis space). Using our knowledge of Lagrangian duality, we find a dual form of the SVM problem, apply the complementary slackness conditions, and derive some interesting insights into the connection between "support vectors" and margin. Read the "SVM Insights from Duality" in the Notes below for a highlevel view of this mathematically dense lecture. Notably absent from the lecture is the hardmargin SVM and its standard geometric derivation. Although the derivation is fun, since we start from the simple and visually appealing idea of maximizing the "geometric margin", the hardmargin SVM is rarely useful in practice, as it requires separable data, which precludes any datasets with repeated inputs and label noise. One fixes this by introducing "slack" variables, which leads to a formulation equivalent to the softmargin SVM we present. Once we introduce slack variables, I've personally found the interpretation in terms of maximizing the margin to be much hazier, and I find understanding the SVM in terms of "just" a particular loss function and a particular regularization to be much more useful for understanding its properties. That said, Brett Bernstein gives a very nice development of the geometric approach to the SVM, which is linked in the References below. At the very least, it's a great exercise in basic linear algebra.  
Neither the lasso nor the SVM objective function is differentiable, and we had to do some work for each to optimize with gradientbased methods. It turns out, however, that gradient descent will essentially work in these situations, so long as you're careful about handling the nondifferentiable points. To this end, we introduce "subgradient descent", and we show the surprising result that, even though the objective value may not decrease with each step, every step brings us closer to the minimizer. This mathematically intense lecture may be safely skipped.  
When using linear hypothesis spaces, one needs to encode explicitly any nonlinear dependencies on the input as features. In this lecture we discuss various strategies for creating features. Much of this material is taken, with permission, from Percy Liang's CS221 course at Stanford.  
With linear methods, we may need a whole lot of features to get a hypothesis space that's expressive enough to fit our data  there can be orders of magnitude more features than training examples. While regularization can control overfitting, having a huge number of features can make things computationally very difficult, if handled naively. For objective functions of a particular general form, which includes ridge regression and SVMs but not lasso regression, we can "kernelize", which can allow significant speedups in certain situations. In fact, with the "kernel trick", we can even use an infinitedimensional feature space at a computational cost that depends primarily on the training set size. In more detail, it turns out that even when the optimal parameter vector we're searching for lives in a very highdimensional vector space (dimension being the number of features), a basic linear algebra argument shows that for certain objective functions, the optimal parameter vector lives in a subspace spanned by the training input vectors. Thus, when we have more features than training points, we may be better off restricting our search to the lowerdimensional subspace spanned by training inputs. We can do this by an easy reparameterization of the objective function. This result is referred to as the "representer theorem", and its proof can be given on one slide.After reparameterization, we'll find that the objective function depends on the data only through the Gram matrix, or "kernel matrix", which contains the dot products between all pairs of training feature vectors. This is where things get interesting a second time: Suppose f is our featurization function. Sometimes the dot product between two feature vectors f(x) and f(x') can be computed much more efficiently than multiplying together corresponding features and summing. In such a situation, we write the dot products in terms of the "kernel function": k(x,x')=〈f(x),f(x')〉, which we hope to compute much more quickly than O(d), where d is the dimension of the feature space. The essence of a "kernel method" is to use this "kernel trick" together with the reparameterization described above. This allows one to use huge (even infinitedimensional) feature spaces with a computational burden that depends primarily on the size of your training set. In practice, it's useful for small and mediumsized datasets for which computing the kernel matrix is tractable. Scaling kernel methods to large data sets is still an active area of research.
 
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This is our second "blackbox" machine learning lecture. We start by discussing various models that you should almost always build for your data, to use as baselines and performance sanity checks. From there we focus primarily on evaluating classifier performance. We define a whole slew of performance statistics used in practice (precision, recall, F1, etc.). We also discuss the fact that most classifiers provide a numeric score, and if you need to make a hard classification, you should tune your threshold to optimize the performance metric of importance to you, rather than just using the default (typically 0 or 0.5). We also discuss the various performance curves you'll see in practice: precision/recall, ROC, and (my personal favorite) lift curves.  
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So far we have studied the regression setting, for which our predictions (i.e. "actions") are realvalued, as well as the classification setting, for which our score functions also produce real values. With this lecture, we begin our consideration of "conditional probability models", in which the predictions are probability distributions over possible outcomes. We motivate these models by discussion of the "CitySense" problem, in which we want to predict the probability distribution for the number of taxicab dropoffs at each street corner, at different times of the week. Given this model, we can then determine, in realtime, how "unusual" the amount of behavior is at various parts of the city, and thereby help you find the secret parties, which is of course the ultimate goal of machine learning.  
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In empirical risk minimization, we minimize the average loss on a training set. If our prediction functions are producing probability distributions, what loss functions will give reasonable performance measures? In this lecture, we discuss "likelihood", one of the most popular performance measures for distributions. We temporarily leave aside the conditional probability modeling problem, and focus on the simpler problem of fitting an unconditional probability model to data. We can use "maximum likelihood" to fit both parametric and nonparametric models. Once we have developed a collection of candidate probability distributions on training data, we select the best one by choosing the model that has highest "holdout likelihood", i.e. likelihood on validation data.  
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In this lecture we consider prediction functions that produce distributions from a parametric family of distributions. We restrict to the case of linear models, though later in the course we will show how to make nonlinear versions using gradient boosting and neural networks. We develop the technique through four examples: Bernoulli regression (logistic regression being a special case), Poisson regression, Gaussian regression, and multinomial logistic regression (our first multiclass method). We conclude by connecting this maximum likelihood framework back to our empirical risk minimization framework.  
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We review some basics of classical and Bayesian statistics. For classical "frequentist" statistics, we define statistics and point estimators, and discuss various desirable properties of point estimators. For Bayesian statistics, we introduce the "prior distribution", which is a distribution on the parameter space that you declare before seeing any data. We compare the two approaches for the simple problem of learning about a coin's probability of heads. Along the way, we discuss conjugate priors, posterior distributions, and credible sets. Finally, we give the basic setup for Bayesian decision theory, which is how a Bayesian would go from a posterior distribution to choosing an action.  
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In our earlier discussion of conditional probability modeling, we started with a hypothesis space of conditional probability models, and we selected a single conditional probability model using maximum likelihood or regularized maximum likelihood. In the Bayesian approach, we start with a prior distribution on this hypothesis space, and after observing some training data, we end up with a posterior distribution on the hypothesis space. For making conditional probability predictions, we can derive a predictive distribution from the posterior distribution. We explore these concepts by working through the case of Bayesian Gaussian linear regression. We also make a precise connection between MAP estimation in this model and ridge regression.  
We begin our discussion of nonlinear models with tree models. We first describe the hypothesis space of decision trees, and we discuss some complexity measures we can use for regularization, including tree depth and the number of leaf nodes. The challenge starts when we try to find the regularized empirical risk minimizer (ERM) over this space for some loss function. It turns out finding this ERM is computationally intractable. We discuss a standard greedy approach to tree building, both for classification and regression, in the case that features take values in any ordered set. We also describe an approach for handling categorical variables (in the binary classification case) and missing values.  
In this lecture, we define bootstrap sampling and show how it is typically applied in statistics to do things such as estimating variances of statistics and making confidence intervals. It can be used in a machine learning context for assessing model performance.  
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We motivate bagging as follows: Consider the regression case, and suppose we could create a bunch of prediction functions, say B of them, based on B independent training samples of size n. If we average together these prediction functions, the expected value of the average is the same as any one of the functions, but the variance would have decreased by a factor of 1/B  a clear win! Of course, this would require an overall sample of size nB. The idea of bagging is to replace independent samples with bootstrap samples from a single data set of size n. Of course, the bootstrap samples are not independent, so much of our discussion is about when bagging does and does not lead to improved performance. Random forests were invented as a way to create conditions in which bagging works better. Although it's hard to find crisp theoretical results describing when bagging helps, conventional wisdom says that it helps most for models that are "high variance", which in this context means the prediction function may change a lot when you train with a new random sample from the same distribution, and "low bias", which basically means fitting the training data well. Large decision trees have these characteristics and are usually the model of choice for bagging. Random forests are just bagged trees with one additional twist: only a random subset of features are considered when splitting a node of a tree. The hope, very roughly speaking, is that by injecting this randomness, the resulting prediction functions are less dependent, and thus we'll get a larger reduction in variance. In practice, random forests are one of the most effective machine learning models in many domains.  
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Gradient boosting is an approach to "adaptive basis function modeling", in which we learn a linear combination of M basis functions, which are themselves learned from a base hypothesis space H. Gradient boosting may be used with any subdifferentiable loss function and over any base hypothesis space on which we can do regression. Regression trees are the most commonly used base hypothesis space. It is important to note that the "regression" in "gradient boosted regression trees" (GBRTs) refers to how we fit the basis functions, not the overall loss function. GBRTs are routinely used for classification and conditional probability modeling. They are among the most dominant methods in competitive machine learning (e.g. Kaggle competitions). If the base hypothesis space H has a nice parameterization (say differentiable, in a certain sense), then we may be able to use standard gradientbased optimization methods directly. In fact, neural networks may be considered in this category. However, if the base hypothesis space H consists of trees, then no such parameterization exists. This is where gradient boosting is really needed.For practical applications, it would be worth checking out the GBRT implementations in and . See the Notes below for fully worked examples of doing gradient boosting for classification, using the hinge loss, and for conditional probability modeling using both exponential and Poisson distributions. The code gbm.py illustrates L2boosting and L1boosting with decision stumps, for a onedimensional regression dataset.  
Here we consider how to generalize the scoreproducing binary classification methods we've discussed (e.g. SVM and logistic regression) to multiclass settings. We start by discussing "OnevsAll", a simple reduction of multiclass to binary classification. This usually works just fine in practice, despite the interesting failure case we illustrate. However, OnevsAll doesn't scale to a very large number of classes, since we have to train a separate model for each class. This is the real motivation for presenting the "compatibility function" approach described in this lecture. The approach presented here extends to structured prediction problems, where the output space may be exponentially large. We didn't have time to define structured prediction in the lecture, but please see the slides and the SSBD book in the references.  
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Here we start our short unit on unsupervised learning. kmeans clustering is presented first as an algorithm and then as an approach to minimizing a particular objective function. One challenge with clustering algorithms is that it's not obvious how to measure success. (See Section 22.5 of the SSBD book for a nice discussion.) When possible, I prefer to take a probabilistic modeling approach, as discussed in the next two lectures.  
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A Gaussian mixture model (GMM) is a family of multimodal probability distributions, which is a plausible generative model for clustered data. We can fit this model using maximum likelihood, and we can assess the quality of fit by evaluating the model likelihood on holdout data. While the "learning" phase of Gaussian mixture modeling is fitting the model to data, in the "inference" phase, we determine for any point drawn from the GMM the probability that it came from each of the k components. To use a GMM for clustering, we simply assign each point to the component that it is most likely to have come from. kmeans clustering can be seen as a limiting case of a restricted form of Gaussian mixture modeling.  
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It turns out, fitting a Gaussian mixture model (GMM) by maximum likelihood is easier said than done: there is no closed form solution, and our usual gradient methods do not work well. The standard approach to maximum likelihood estimation in a Gaussian mixture model is the expectation maximization (EM) algorithm. In this lecture, we present the EM algorithm for a general latent variable model, of which GMM is a special case. We present the EM algorithm as a very basic "variational method" and indicate a few generalizations.  
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In the context of this course, we view neural networks as "just" another nonlinear hypothesis space. On the practical side, unlike trees and treebased ensembles (our other major nonlinear hypothesis spaces), neural networks can be fit using gradientbased optimization methods. On the theoretical side, a large enough neural network can approximate any continuous function. We discuss the specific case of the multilayer perceptron for multiclass classification, which we view as a generalization of multinomial logistic regression from linear to nonlinear score functions.  
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Neural network optimization is amenable to gradientbased methods, but if the actual computation of the gradient is done naively, the computational cost can be prohibitive. Backpropagation is the standard algorithm for computing the gradient efficiently. We present the backpropagation algorithm for a general computation graph. The algorithm we present applies, without change, to models with "parameter tying", which include convolutional networks and recurrent neural networks (RNN's), the workhorses of modern computer vision and natural language processing. We illustrate backpropagation with one of the simplest models with parameter tying: regularized linear regression. Backpropagation for the multilayer perceptron, the standard introductory example, is presented in detail in .  
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We point the direction to many other topics in machine learning that should be accessible to students of this course, but that we did not have time to cover.  
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Assignments
GD, SGD, and Ridge Regression
Lasso Regression
SVM and Sentiment Analysis
Kernel Methods
Probabilistic Modeling
Multiclass, Trees, and Gradient Boosting
Computation Graphs, Backpropagation, and Neural Networks
Other tutorials and references
 Carlos FernandezGranda's lecture notes provide a comprehensive review of the prerequisite material in linear algebra, probability, statistics, and optimization.
 Brian Dalessandro's iPython notebooks from DSGA1001: Intro to Data Science
 The Matrix Cookbook has lots of facts and identities about matrices and certain probability distributions.
 Stanford CS229: "Review of Probability Theory"
 Stanford CS229: "Linear Algebra Review and Reference"
 Math for Machine Learning by Hal Daumé III
David S. Rosenberg
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This repository contains links to machine learning exams, homework assignments, and exercises that can help you test your understanding.
fatosmorina/machinelearningexams
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Carnegie mellon university (cmu).
 The fall 2009 10601 midterm ( midterm and solutions )
 The spring 2009 10601 midterm ( midterm and solutions )
 The fall 2010 10601 midterm ( midterm , solution )
 The 2001 10701 midterm ( midterm , solutions )
 The 2002 10701 midterm ( midterm , solutions )
 The 2003 10701 midterm ( midterm , solutions )
 The 2004 10701 midterm ( midterm , solutions )
 The 2005 spring 10701 midterm ( midterm and solutions )
 The 2005 fall 10701 midterm ( midterm and solutions )
 The 2006 fall 10701 midterm ( midterm , solutions )
 The 2007 spring 10701 midterm ( midterm , solutions )
 The 2008 spring 10701 midterm ( midterm and solutions )
 Additional midterm examples ( questions , solutions )
 The 2001 final ( final , solutions )
 The 2002 final ( final with some figs missing , solutions )
 The 2003 final ( final , solutions )
 The 2004 final ( solutions )
 The 2006 fall final ( final , solutions )
 The 2007 spring final ( final , solutions )
 The 2008 fall final ( final , solutions )
 The 2009 spring 701 midterm , final
 The 2010 fall 601 midterm
 The 2012 fall 601 midterm
 The 2012 spring 701 final
 May 2015 final
 March 2015 midterm
 The 2012 fall midterm
 The 2015 fall 701 midterm , solutions
 The 2011 fall midterm
 The spring 2014 midterm
 The spring 2013 final
 The 2007 10701 spring ( final , solutions )
 The 2008 10701 fall ( final , solutions )
 The 2012 10701 spring final ( final and solutions )
Stanford University
Cs230 deep learning.
 Fall Quarter 2018 Midterm Exam Solution
 Spring Quarter 2018 Midterm Exam Solution
 Winter Quarter 2018 Midterm Exam Solution
 Fall Quarter 2019 Midterm Exam Solution
 Winter Quarter 2019 Midterm Exam Solution
 Fall Quarter 2020 Midterm Exam Solution
 Winter Quarter 2020 Midterm Exam Solution
 Winter Quarter 2021 Midterm Exam Solution
 Spring Quarter 2021 Midterm Exam Solution
CS224N Natural Language Processing with Deep Learning
 Winter 2017 Midterm Exam
Introduction to Machine Learning
 The 2020 final exam , solutions
University of Texas
Machine learning.
 [Midterm] ( https://www.cs.utexas.edu/~dana/MLClass/practicemidterm2.pdf )
University of Toronto
Neural networks and deep learning.
 2019 Midterm
Technical University of Munich
In2346: introduction to deep learning (i2dl).
 2020 Mock exam , solutions
University of Pennsylvania
Cis 520 machine learning.
 2016 Midterm Exam
University of Washington
10701 machine learning.
 2007 Autumn Midterm: [Exam] [Solutions]
 2009 Autumn Midterm: [Exam]
 2013 Spring Midterm (CSE446): [Exam]
 2013 Autumn Midterm: [Exam]
 2013 Autumn Final: [Exam]
 2014 Autumn Midterm: [Exam] [Solutions]
University of Edinburgh
Machine learning and pattern recognition (mlpr) tutorials, autumn 2018.
 Tutorial 1, week 3, html , pdf
 Tutorial 2, week 4, html , pdf
 Tutorial 3, week 5, html , pdf
 Tutorial 4, week 6, html , pdf
 Tutorial 5, week 7, html , pdf
 Tutorial 6, week 8, html , pdf
 Tutorial 7, week 9, html , pdf
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Frequently asked questions
Introduction to Machine Learning
Homework 1  numpy and ml.
Due: Wednesday, February 15, 2023 at 11:00 PM
Welcome to your first homework! Homeworks are designed to be our primary teaching and learning mechanism, with conceptual, math, and coding questions that are designed to highlight the critical ideas in this course. You may choose to tackle the questions in any order, but the homeworks are designed to be followed sequentially. Often, insights from the early problems will help with the later ones.
You have 'free checking'! That means you can check and submit your answer as many times as you want. Your best submission (the one that gives you the most points taking into account correctness and lateness) will be countedyou don't have to worry about it.
After submitting your answers, even if you have gotten a perfect score, we highly encourage you to hit 'View Answer' to look at the staff solution. You may find the staff solutions approached the problems in a different way than you did, which can yield additional insight. Be sure you have gotten your points before hitting 'View Answer', however. You will not be allowed to submit again after viewing the answer.
Each week, we'll provide a Colab notebook for you to use draft and debug your solutions to coding problems (you have better editing and debugging tools there); but you should submit your final solutions here to claim your points.
This week's Colab notebook can be found here: HW01 Colab Notebook (Click Me!)
The homework comes in two parts:
 Learning to use numpy
 Introduction to linear regression
Machine learning algorithms almost always boil down to matrix computations, so we'll need a way to efficiently work with matrices.
numpy is a package for doing a variety of numerical computations in Python that supports writing very compact and efficient code for handling arrays of data. It is used extensively in many fields requiring numerical analysis, so it is worth getting to know.
We will start every code file that uses numpy with import numpy as np , so that we can reference numpy functions with the np. precedent. The fundamental data type in numpy is the multidimensional array, and arrays are usually generated from a nested list of values using the np.array command. Every array has a shape attribute which is a tuple of dimension sizes.
In this class, we will use twodimensional arrays almost exclusively. That is, we will use 2D arrays to represent both matrices and vectors! This is one of several times where we will seem to be unnecessarily fussy about how we construct and manipulate vectors and matrices, but this will make it easier to catch errors in our code. Even if [[1,2,3]] and [1,2,3] may look the same to us, numpy functions can behave differently depending on which format you use. The first has two dimensions (it's a list of lists), while the second has only one (it's a single list). Using only 2D arrays for both matrices and vectors gives us predictable results from numpy operations.
Using 2D arrays for matrices is clear enough, but what about column and row vectors? We will represent a column vector as a d\times 1 array and a row vector as a 1\times d array. So for example, we will represent the threeelement column vector, x = \left[ \begin{array}{c} 1 \\ 5 \\ 3 \\ \end{array} \right], as a 3 \times 1 numpy array. This array can be generated with
~~~ x = np.array([[1],[5],[3]]),
or by using the transpose of a 1 \times 3 array (a row vector) as in,
~~~ x = np.transpose(np.array([[1,5,3]]),
where you should take note of the "double" brackets.
It is often more convenient to use the array attribute .T , as in
~~~ x = np.array([[1,5,3]]).T
to compute the transpose.
Before you begin, we would like to note that in this assignment we will not accept answers that use for or while loops. One reason for avoiding loops is efficiency. For many operations, numpy calls a compiled library written in C, and the library is far faster than interpreted Python (in part due to the lowlevel nature of C, optimizations like vectorization, and in some cases, parallelization). But the more important reason for avoiding loops is that using numpy library calls leads to simpler code that is easier to debug. So, we expect that you should be able to transform loop operations into equivalent operations on numpy arrays, and we will practice this in this assignment.
Of course, there will be more complex algorithms that require loops, but when manipulating matrices you should always look for a solution without loops.
You can find general documentation on numpy here .
Numpy functions and features you should be familiar with for this assignment:
 np.transpose (and the equivalent method a.T )
 np.ndarray.shape
 np.dot (and the equivalent method a.dot(b) )
 np.linalg.inv
 Elementwise operators +, , *, /
Note that in Python, np.dot(a, b) is the matrix product a @ b , not the dot product a^T b .
If you're unfamiliar with numpy and want to see some examples of how to use it, please see this link: Numpy Overview .
Array Basics
Creating Arrays
Provide an expression that sets A to be a 2 \times 3 numpy array ( 2 rows by 3 columns), containing any values you wish.
Write a procedure that takes an array and returns the transpose of the array. You can use np.transpose or the property T , but you may not use loops.
Note: as with other coding problems in 6.390 you do not need to call the procedure; it will be called/tested when submitted.
Shapes Hint: If you get stuck, code and run these expressions (with array values of your choosing), then print out the shape using A.shape
Let A be a 4\times 2 numpy array, B be a 4\times 3 array, and C be a 4\times 1 array. For each of the following expressions, indicate the shape of the result as a tuple of integers ( recall python tuples use parentheses, not square brackets, which are for lists, and a tuple with just one item x in it is written as (x,) with a comma ). Write "none" (as a Python string with quotes) if the expression is illegal.
For example,
 If the result array was [45, 36, 75] , the shape is (3,)
 If the result array was [[1,2,3],[4,5,6]] , the shape is (2,3)
Hint: for more compact and legible code, use @ for matrix multiplication, instead of np.dot . If A and B , are matrices (2D numpy arrays), then A @ B = np.dot(A, B) .
Indexing vs. Slicing The shape of the resulting array is different depending on if you use indexing or slicing. Indexing refers to selecting particular elements of an array by using a single number (the index) to specify a particular row or column. Slicing refers to selecting a subset of the array by specifying a range of indices.
If you're unfamiliar with these terms, and the indexing and slicing rules of arrays, please see the indexing and slicing sections of this link: Numpy Overview (Same as the Numpy Overview link from the introduction). You can also look at the official numpy documentation here .
In the following questions, let A = np.array([[5,7,10,14],[2,4,8,9]]) . Tell us what the output would be for each of the following expressions. Use brackets [] as necessary. If the operation is invalid, write the python string "none" .
Note: Remember that Python uses zeroindexing and thus starts counting from 0, not 1. This is different from R and MATLAB.
Indexing, revisited
Slicing, revisited
Lone Colon Slicing
Combining Indexing and Slicing
Combining Indexing and Slicing, revisited
Combining Indexing and Slicing, revisited again
Coding Practice
Now that we're familiar with numpy arrays, let's practice actually using numpy in our code!
In the following questions, you must get the shapes of the output correct for your answer to be accepted. If your answer contains the right numbers but the grader is still saying your answers are incorrect, check the shapes of your output. The number and placement of brackets need to match!
Write a procedure that takes a list of numbers and returns a 2D numpy array representing a row vector containing those numbers. Recall that a row vector in our usage will have shape (1, d) where d is the number of elements in the row.
Column Vector
Write a procedure that takes a list of numbers and returns a 2D numpy array representing a column vector containing those numbers. You can use the rv procedure.
Write a procedure that takes a column vector and returns the vector's Euclidean length (or equivalently, its magnitude) as a scalar . You may not use np.linalg.norm , and you may not use loops.
Remember that the formula for the Euclidean length for a vector \mathbf{x} is:
{\rm length}(\mathbf{x}) = \sqrt{x_1^2 + x_2^2 + ... + x_n^2} \\ = \sqrt{\sum_{i=1}^n{x^2_i}}
Write a procedure that takes a column vector and returns a unit vector (a vector of length 1 ) in the same direction. You may not use loops. Use your length procedure from above (you do not need to define it again).
Last Column
Write a procedure that takes a 2D array and returns the final column vector as a two dimensional array. You may not use loops. Hint: negative indices are interpreted as counting from the end of the array.
Matrix inverse
A scalar number x has an inverse x^{1} , such that x^{1} x = 1 , that is, their product is 1 . Similarly, a matrix A may have a welldefined inverse A^{1} , such that A^{1} A = I , where matrix multiplication is used, and I is the identity matrix. Such inverses generally only exist when A is a square matrix, and just as 0 has no well defined multiplicative inverse, there are also cases when matrices are "singular" and have no well defined inverses.
Write a procedure that takes a matrix A and returns its inverse, A^{1} . Assume that A is wellformed, such that its inverse exists. Feel free to use routines from np.linalg .
Working with Data in Numpy
Representing data
Mat T. Ricks has collected weight and height data of 3 people and has written it down below:
Weight, Height 150, 5.8 130, 5.5 120, 5.3
He wants to put this into a numpy array such that each column represents one individual's weight and height (in that order), in the order of individuals as listed. Write code to set data equal to the appropriate numpy array:
We are beginning our study of machine learning with linear regression which is a fundamental problem in supervised learning. Please study Sections 2.1 through 2.4 of the Chapter 2  Regression lecture notes before starting in on these problems.
A hypothesis in linear regression has the form y = \theta^T x + \theta_0 where x is a d \times 1 input vector, y is a scalar output prediction, \theta is a d \times 1 parameter vector and \theta_0 is a scalar offset parameter.
This week, just to get warmed up, we will consider a simple algorithm for trying to find a hypothesis that fits the data well: we will generate a lot of random hypotheses and see which one has the smallest error on this data, and return that one as our answer. (We don't recommend this method in actual practice, but it gets us started and makes some useful points.)
Here is a dataset for a regression problem, with d = 1 and n = 5 : \mathcal{D} = {([1], 2), ([2], 1), ([3], 4), ([4], 3), ([5], 5)} Recall from the notes that \mathcal{D} is a set of (x, y) (input, output) pairs.
Linear prediction
Assume we are given an input x as a column vector and the parameters specifying a linear hypothesis. Let's compute a predicted value.
Write a Python function which is given:
 x : input vector d \times 1
 th : parameter vector d \times 1
 th0 : offset parameter 1 \times 1 or scalar
and returns:
 y value predicted for input x by hypothesis th , th0
Lots of data!
Now assume we are given n points in an array, let's compute predictions for all the points.
 X : input array d \times n
 a 1\times n vector y of predicted values, one for each column of X for hypothesis th , th0
Try to make it so that your answer to this question can be used verbatim as an answer to the previous question.
Mean squared error
Given two 1 \times n vectors of output values, Y and Y_hat , compute a 1 \times 1 (or scalar) mean squared error.
 Read about np.mean
 Y : vector of output values 1 \times n
 Y_hat : vector of output values 1 \times n
 a 1\times 1 array with the mean square error
More mean squared error
Assume now that you have two k \times n arrays of output values, Y and Y_hat . Each row (0 \dots k1) in a matrix represents the results of using a different hypothesis. Compute a k \times 1 vector of the meansquared errors associate with each of the hypotheses (but averaged over all n data points, in each case.)
 Read about the axis and keepdims arguments to np.mean
(Try to make it so that your answer to this question can be used verbatim as an answer to the previous question.)
 Y : vector of output values k \times n
 Y_hat : vector of output values k \times n
 a k\times 1 vector of mean squared error values
Linear prediction error
Use the mse and lin_reg_predict procedures to implement a procedure that takes
 X : d \times n input array representing n points in d dimensions
 Y : 1 \times n output vector representing output values for n points
 th0 : offset 1 \times 1 (or scalar)
and returns
1 \times 1 (or scalar) value representing the MSE of hypothesis th , th0 on the data set X , Y .
Read about the axis argument to np.mean
Our first machine learning algorithm!
The code is below. It takes in
 X : d\times n input array representing n points in d dimensions
 Y : 1\times n output vector representing output values for n points
 k : a number of hypotheses to try
And generates as output
 the tuple ((th, th0), error) where th , th0 is a hypothesis and error is the MSE of that hypothesis on the input data.
def random_regress(X, Y, k): 1 d, n = X.shape 2 thetas = 2 *np.random.rand(d, k)  1 3 th0s = 2* np.random.rand(1, k)  1 4 errors = lin_reg_err(X, Y, thetas, th0s.T) 5 i = np.argmin(errors) 6 theta, th0 = thetas[:,[i]], th0s[:,[i]] return (theta, th0), errors[i]
Note that in this code we use np.random.rand rather than np.random.randn as we will see in the lab. So some of the behavior will be different, and we'll ask some questions about that below.
 Read about np.random.rand
 Read about np.argmin
Rather than asking you to write the code, we are going to ask you some questions about it.
c. When we call lin_reg_err in line 4, we have objects with the following dimensions:
 X : d \times n
 ths : d\times k
 th0s : 1\times k
If we want to get a matrix of predictions of all the hypotheses on all the data points, we can write np.dot(ths.T, X) + th0s.T But if we do the dimensional analysis here, there's something fishy.
(The form below is to help us improve/calibrate for future assignments; submission is encouraged but not required. Thanks!)
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Assignments
Jump to: [Homeworks] [Projects] [Quizzes] [Exams]
There will be one homework (HW) for each topical unit of the course. Due about a week after we finish that unit.
These are intended to build your conceptual analysis skills plus your implementation skills in Python.
 HW0 : Numerical Programming Fundamentals
 HW1 : Regression, CrossValidation, and Regularization
 HW2 : Evaluating Binary Classifiers and Implementing Logistic Regression
 HW3 : Neural Networks and Stochastic Gradient Descent
 HW4 : Trees
 HW5 : Kernel Methods and PCA
After completing each unit, there will be a 20 minute quiz (taken online via gradescope).
Each quiz will be designed to assess your conceptual understanding about each unit.
Probably 10 questions. Most questions will be true/false or multiple choice, with perhaps 13 short answer questions.
You can view the conceptual questions in each unit's inclass demos/labs and homework as good practice for the corresponding quiz.
There will be three larger "projects" throughout the semester:
 Project A: Classifying Images with Feature Transformations
 Project B: Classifying Sentiment from Text Reviews
 Project C: Recommendation Systems for Movies
Projects are meant to be openended and encourage creativity. They are meant to be case studies of applications of the ML concepts from class to three "real world" use cases: image classification, text classification, and recommendations of movies to users.
Each project will due approximately 4 weeks after being handed out. Start early! Do not wait until the last few days.
Projects will generally be centered around a particular methodology for solving a specific task and involve significant programming (with some combination of developing core methods from scratch or using existing libraries). You will need to consider some conceptual issues, write a program to solve the task, and evaluate your program through experiments to compare the performance of different algorithms and methods.
Your main deliverable will be a short report (24 pages), describing your approach and providing several figures/tables to explain your results to the reader.
You’ll be assessed on effort, the sophistication of your technical approach, the clarity of your explanations, the evidence that you present to support your evaluative claims, and the performance of your implementation. A highperforming approach with little explanation will receive little credit, while a careful set of experiments that illuminate why a particular direction turned out to be a dead end may receive close to full credit.
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Find their Turnaround Time
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Check Samples and Past Work
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Wrapping Up
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Consider the aforementioned crucial aspects to choose a service that helps in improving your learning experience and excel in your academic journey with the right guidance. Here’s wishing you good luck in making an informed decision!
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Practical Applications in Machine Learning (PAML)
Cornell Tech
Assignments
The goal of assignments is to assess students’ understanding of the course outcomes in homework and final project assignments. The assignment deadlines are due on Gradescope and as follows:
 Homeworks : Coding assignments on course topics are DUE 2 weeks after assigned.
 Final Project Proposal : Propose an FP idea; DUE on April 26th @ 11:59PM.
 Final Project Midpoint Report : Provide updates on project deliverables on May 1st during the class session.
 Final Project Presentation : Present FP in class for 15 minutes with a 3minute Q&A; scheduled on May 13th @ 2:40PM.
Late Policy
Students have 6 late days (2 max per assignment) to use for the semester for assignment submissions, including homework and the final project. After that, the grade will be dropped one letter grade per day late.
Students have 1 week after assignments are returned to make a regrade request (no exceptions). Send an email to Prof. Taylor, Jinzhao Kang, and Kathryn Guda.=
Course Assignment Schedule
1: Week of 1/22  Lecture 1: Introduction to PAML (Homework 0)  Lecture 2: Revisit Preliminaries 
2: Week of 1/29  Lecture 3: Building an EndtoEnd ML Pipeline  Lecture 4: Dataset Curation (Homework 0 DUE) 
3: Week of 2/5  Lecture 5: Preprocessing I
 Lecture 6: Preprocessing II (Inclass activities) (Homework 1) 
4: Week of 2/12  Lecture 7: Introduction to Regression  Lecture 8: Regression for Predicting Housing Prices 
5: Week of 2/19  Lecture 9: Regression for Predicting Housing Prices  Lecture 10: Regression for Predicting Housing Prices (Inclass activities) (Homework 2, Homework 1 DUE) 
6: Week of 2/26  No Class – February Break  Homework Review Q&A 
7: Week of 3/4  Lecture 11: Introduction to Classification  Lecture 12: Classification for Product Recommendation 
8: Week of 3/11  Lecture 13: Classification for Product Recommendation [REMOTE LECTURE]  Lecture 14: Classification for Product Recommendation [REMOTE LECTURE] (Inclass activities) (Homework 3, Homework 2 DUE) 
9: Week of 3/18  Lecture 15: Introduction to Clustering  Lecture 16: Clustering for Document Retrieval 
10: Week of 3/25  Lecture 17: Clustering for Document Retrieval  Lecture 18: Clustering for Document Retrieval (Inclass activities) (Homework 4, Homework 3 DUE) 
11: Week of 4/1  No Class – Spring Break  No Class – Spring Break 
12: Week of 4/8  Lecture 19: Deep Learning Fundamentals I  Lecture 20: Deep Learning for Image Search (Inclass activities) 
13: Week of 4/15  Lecture 21: Final Project Discussion  Guest Lecture – FP Working Day (Homework 4 DUE) 
14: Week of 4/22  Guest Lecture Tariq Iqbal (UVA)  Guest Lecture – Kilian Weinberger FP Proposal DUE Friday, April 26th 
15: Week of 4/29  Guest Lecture – Karen Levy  FP Midpoint Report 
16: Week of 5/6  Last Day of Instruction  No Class 
17: Week of 5/13  Final Project Presentation  Final Project Report & Code Due 
*Homework (HW)
*final project (fp).
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Recently Asked Machine Learning Questions
 Q 1 : Assignment 5: Matrix as a Linear Transformation See Answer
 Q 2 : Q1 Consider the problem where we want to predict the gender of a person from a set of input parameters, namely height, weight, and age. See Answer
 Q 3 : For this programming assignment you will implement the Naive Bayes algorithm from scratch and the functions to evaluate it with a kfold cross validation (also from scratch). You can use the code in the following tutorial to get started and get ideas for your implementation of the Naive Bayes algorithm but please, enhance it as much as you can (there are many things you can do to enhance it such as those mentioned at the end of the tutorial): See Answer
 Q 4 : Q1 Consider the problem where we want to predict the gender of a person from a set of input parameters, namely height, weight, and age. a) Using Cartesian distance, Manhattan distance and Minkowski distance of order 3 as the similarity measurements show the results of the gender prediction for the Evaluation data that is listed below generated training data for values of K of 1, 3, and 7. Include the intermediate steps (i.e., distance calculation, neighbor selection, and prediction). b) Implement the KNN algorithm for this problem. Your implementation should work with different training data sets as well as different values of K and allow to input a data point for the prediction. c) To evaluate the performance of the KNN algorithm (using Euclidean distance metric), implement a leave oneout evaluation routine for your algorithm. In leaveoneout validation, we repeatedly evaluate the algorithm by removing one data point from the training set, training the algorithm on the remaining data set and then testing it on the point we removed to see if the label matches or not. Repeating this for each of the data points gives us an estimate as to the percentage of erroneous predictions the algorithm makes and thus a measure of the accuracy of the algorithm for the given data. Apply your leaveoneout validation with your KNN algorithm to the dataset for Question 1 c) for values for K of 1, 3, 5, 7, 9, and 11 and report the results. For which value of K do you get the best performance? d) Repeat the prediction and validation you performed in Question 1 c) using KNN when the age data is removed (i.e. when only the height and weight features are used as part of the distance calculation in the KNN algorithm). Report the results and compare the performance without the age attribute with the ones from Question 1 c). Discuss the results. What do the results tell you about the data? See Answer
 Q 5 : Q2. Using the data from Problem 2, build a Gaussian Naive Bayes classifier for this problem. For this you have to learn Gaussian distribution parameters for each input data feature, i.e. for p(heightW), p(heightM), p(weightW), p(weightM), p(ageW), p(ageM). a) Learn/derive the parameters for the Gaussian Na ive Bayes Classifier for the data from Question 2 a) and apply them to the same target as in problem 1a). b) Implement the Gaussian Na ive Bayes Classifier for this problem. c) Repeat the experiment in part 1 c) and 1 d) with the Gaussian Native Bayes Classifier. Discuss the results, in particular with respect to the performance difference between using all features and using only height and weight. d) Same as 1d but with Naïve Bayes. e) Compare the results of the two classifiers (i.e., the results form 1 c) and 1d) with the ones from 2 c) 2d) and discuss reasons why one might perform better than the other. See Answer
 Q 6 : 6. (Programming) You need to implement the kNN algorithm as in the slides. The data we use for binary classification tasks is the UCI a4a data. See Answer
 Q 7 : Question 1 Download the SGEMM GPU kernel performance dataset from the below link. https://archive.ics.uci.edu/ml/datasets/SGEMM+GPU+kernel+performance Understand the dataset by performing exploratory analysis. Prepare the target parameter by taking the average of the THREE (3) runs with long performance times. Design a linear regression model to estimate the target using only THREE (3) attributes from the dataset. Discuss your results, relevant performance metrics and the impact of normalizing the dataset. See Answer
 Q 8 : Question 2 Load the wine dataset from sklearn package. Perform exploratory data analysis and Design a simple TWO (2) layer neural network for the classification. Compare the performance with the Naïve Bayes algorithm. Train the neural network such that it has better or same performance as that of the Naïve Bayes algorithm. See Answer
 Q 9 : Question 3 Download the MAGIC gamma telescope data 2004 dataset available in Kaggle (https://www.kaggle.com/abhinand05/magicgammatelescopedataset). Prepare the dataset and perform exploratory data analysis. Setup a random forest algorithm for identifying whether the pattern was caused by gamma signal or not. Propose optimal values for the depth and number of trees in the random forest. Assess and compare the performance of optimized random forest with the Naïve Bayes algorithm. Discuss the performance metrics and the computational complexity. See Answer
 Q 10 : Question 4 Use the Fashion MNIST dataset from the keras package. Perform exploratory data analysis. Show a random set of FIVE (5) images from each class in the dataset with their corresponding class names. Prepare the dataset by normalizing the pixel values to be between 0 and 1. Design a CNN with TWO (2) convolutional layers and FOUR (4) dense layers (including the final output layer). Employ 'ReLU' activation and "MaxPooling'. Keep 15% of the train dataset for validation. Rate the performance of the algorithm and provide necessary plots. Pick a random image from the test dataset, pass it to the algorithm and compare the algorithm output with the actual class label. See Answer
 Q 11 : Question 5 Select any stock listed in Singapore stock exchange. Using Yahoo finance, download the daily stock data (Open, High, Low, Close, Adj Close, Volume) from year 1 Jan 2020 to 3 Jan 2022. Use data until 31 Dec 2020 for training and the remaining data for testing. You must select the stock such that the data is available from 1 Jan 2020 to 3 Jan 2022. Use previous 30 days of stock information to predict the next day stock price. Use the data in 'High' column to predict the price, i.e., the next day high price of the stock. Design a LSTM network to do the predictions. You are required to use LSTM with a cell state of at least 60 dimension and do at least 50 epochs of training. Rate the performance of the LSTM classifier and provide necessary plots. See Answer
 Q 12 : This is a machine learning model in python using scikit learn to classify the handwritten Arabic letters. There are two files. The train data and the test data. The code is available, and we need to optimize the code so under box number 6 when we do the cross validation of the model, the accuracy of the model should be in high 80s and low 90s. we should be tuning the hyperparameters and improve the pipeline as needed. Anything is allowed to be used from the scikit learn but nothing more. The code as it is, the model accuracy is 79 The goal is to modify the code to be able to get an accuracy of the model in the high 80s and low 90s. In box 3 of the code, there are the hyperparameters that need to be tuned and the pipeline that might need to be modifed. Voting model can be used to get high accuracy. We need to improve the model accuracy from the existing code. Info about the dataset: The dataset is composed of 16,800 characters written by 60 participants, the age range is between 19 to 40 years, and 90% of participants are righthand. Each participant wrote each character (from 'alef' to 'yeh') ten times on two forms. The forms were scanned at the resolution of 300 dpi. The dataset is partitioned into two sets: a training set (13,440 characters to 480 images per class) and a test set (3,360 characters to 120 images per class). Writers of training set and test set are exclusive. Ordering of including writers to test set are randomized to make sure that writers of test set were not from a single institution (to ensure variability of the test set). The code: This is a machine learning model in python using scikit learn to classify the handwritten Arabic letters. There are two files. The train data and the test data. The code is available, and we need to optimize the code so under box number 6 when we do the cross validation of the model, the accuracy of the model should be in high 80s and low 90s. we should be tuning the hyperparameters and improve the pipeline as needed. Anything is allowed to be used from the scikit learn but nothing more. Voting model can be used to improve accuracy. Goal: build an image classifier to classify handwritten Arabic language characters using scikit learn. The model accuracy have to be in high 80s like 89% or low 90s like 92% This is all about tuning the hyperparameters and the model pipeline See Answer
 Q 13 : This is a machine learning model in python using scikit learn to classify the handwritten Arabic letters. There are two files. The train data and the test data. The code is available, and we need to optimize the code so under box number 6 when we do the cross validation of the model, the accuracy of the model should be in high 80s and low 90s. we should be tuning the hyperparameters and improve the pipeline as needed. Anything is allowed to be used from the scikit learn but nothing more. The code as it is, the model accuracy is 79 The goal is to modify the code to be able to get an accuracy of the model in the high 80s and low 90s. In box 3 of the code, there are the hyperparameters that need to be tuned and the pipeline that might need to be modifed. Voting model can be used to get high accuracy. We need to improve the model accuracy from the existing code. Info about the dataset: The dataset is composed of 16,800 characters written by 60 participants, the age range is between 19 to 40 years, and 90% of participants are righthand. Each participant wrote each character (from 'alef' to 'yeh') ten times on two forms. The forms were scanned at the resolution of 300 dpi. The dataset is partitioned into two sets: a training set (13,440 characters to 480 images per class) and a test set (3,360 characters to 120 images per class). Writers of training set and test set are exclusive. Ordering of including writers to test set are randomized to make sure that writers of test set were not from a single institution (to ensure variability of the test set). The code: This is a machine learning model in python using scikit learn to classify the handwritten Arabic letters. There are two files. The train data and the test data. The code is available, and we need to optimize the code so under box number 6 when we do the cross validation of the model, the accuracy of the model should be in high 80s and low 90s. we should be tuning the hyperparameters and improve the pipeline as needed. Anything is allowed to be used from the scikit learn but nothing more. Voting model can be used to improve accuracy. Goal: build an image classifier to classify handwritten Arabic language characters using scikit learn. The model accuracy have to be in high 80s like 89% or low 90s like 92% This is all about tuning the hyperparameters and the model pipeline See Answer
 Q 14 : There are four folders, each folder contains a set of exercises, the expected results are written at the top of each ipynb. some files are just example solutions Day 1 all about fitting a linear regression or logistic regression to the data. Also to determine the decision boundaries. Day 2 Use Neural Networks to solve simple classification examples Day 3 Using Convolutional Neural Network with PyTorch with one example solution Day 4 Deep learning, the solution is ready just we add the testing data and test the built model and output a submission file with labels See Answer
 Q 15 : The main aim of this project is to analyze a movie review's textual content in order to determine its underlying sentiment. In this project, we try to classify whether a person liked the movie or not based on the review they give for the movie. 1) You need to develop a python code to calculate the sentiment using NLP analysis and should use CNN and logisitic regression 2) You need to create a report of what you have done in the code and also you need to explain how our work is different from the references we have taken (references are in the document) See Answer
 Q 16 : Programming Assignment 2 For this programming assignment you will implement the Lenet 5 CNN using either pytorch or tensorflow, but not Keras. You can take a look at other implementations in internet but please, when coding use your personal coding style and add references to your sources. The goal of this implementation is that you completely understand what happens in the code because our TA will ask you questions about it when reviewing your assignment (you need to make an appointment with your TA for this). Here is an implementation in Pytorch! implementingyannlecunslenet5inpytorch5e05a0911320  lenet5_pytorch.ipynb Here is an implementation in Tensorflow (careful: the tutorial and implementation don't match, I couldn't find the pair from the same author)!  lenetwithtensorflowa35da0d503df  6751b1b92fe8f4ff617f10c7f9f9d315 Test your implementation with the MNIST dataset from Kaggle. Submission:  Code of your implementation of LeNet 5.  Brief report of the results on the MNIST dataset.  An analysis of your results on the MNIST dataset. TA Review:  Your will show your implementation to our TA and he will ask you details about how LeNet works in order grade you. NOTES: 1. DO NOT JUST COPY THE CODE FROM THE TUTORIAL See Answer
 Q 17 : Linear Regression: 1. Consider a simplified fitting problem in the frequency domain where we are looking to find the best fit of data with a set of periodic (trigonometric) basis functions of the form 1, sin²(x), sin(kx), sin²(2kx)..., where k is effectively the frequency increment. The resulting function for a given "frequency increment", k, and "function depth", d, and parameter vector is then: y = 00 * 1+(9; * sin(i + k + x)* sin(i * k*x) i=1 Try "frequency increment" k from 110 For example, if k = 1 and d = 1, your basis (feature) functions are: 1, sin²(x) if k = 1 and d = 2, your basis (feature) functions are: 1, sin(x), sin²(2.x) if k=3 and d = 4, your basis (feature) functions are: 1, sin²(3*1*x), sin²(3*2*x), sin²(3*3*x), sin²(3*4*x) This means that this problem can be solved using linear regression as the function is linear in terms of the parameters Ⓒ. Try "frequency increment" k from 110 and thus your basis functions as part of the data generation process described above. a) Implement a linear regression learner to solve this best fit problem for 1 dimensional data. Make sure your implementation can handle fits for different "function depths" (at least to "depth" 6). b) Apply your regression learner to the data set that was generated for Question 1b) and plot the resulting function for "function depth" 0, 1, 2, 3, 4, 5, and 6. Plot the resulting function together with the data points c) Evaluate your regression functions by computing the error on the test data points that were generated for Question 1c) Compare the error results and try to determine for what "function depths" overfitting might be a problem. Which "function depth" would you consider the best prediction function and why? For which values of k and d do you get minimum error? d) Repeat the experiment and evaluation of part b) and c) using only the first 20 elements of the training data set part b) and the Test set of part c). What differences do you see and why might they occur? Locally Weighted Linear Regression 2. Another way to address nonlinear functions with a lower likelihood of overfitting is the use of locally weighted linear regression where the neighborhood function addresses nonlinearity and the feature vector stays simple. In this case we assume that we will use only the raw feature, x, as well as the bias (i.e. a constant feature 1). Thus the locally applied regression function is y = 0 + 0₁ *x As discussed in class, locally weighted linear regression solves a linear regression problem for each query point, deriving a local approximation for the shape of the function at that point (as well as for its value). To achieve this, it uses a modified error function that applies a weight to each data point's error that is related to its distance from the query point. Here we will assume that the weight function for the i data point and query point x is: w(s) (x) = e (z (6)_x)² Use y: 0.204 where y is a measure of the "locality" of the weight function, indicating how fast the influence of a data point changes with its distance from the query point. a. Implement a locally weighted linear regression learner to solve the best fit problem for 1 dimensional data. b. Apply your locally weighted linear regression learner to the data set that was generated for Question 1b) and plot the resulting function together with the data points c. Evaluate the locally weighted linear regression on the Test data from Question 1 c). How does the performance compare to the one for the results from Question 1 c) ? d. Repeat the experiment and evaluation of part b) and c) using only the first 20 elements of the training data set. How does the performance compare to the one for the results from Question 1 d) ? Why might this be the case? e. Given the results form parts c) and d), do you believe the data set you used was actually derived from a function that is consistent with the function format in Question 1? Justify your answer. Logistic Regression 3. Consider again the problem from Questions 1 and 2 in the first assignment where we want to predict the gender of a person from a set of input parameters, namely height, weight, and age. Assume the same datasets you generated for the first assignment. Use learning rate=0.01. Try different values for number of iterations. a. Implement logistic regression to classify this data (use the individual data elements, i.e. height, weight, and age, as features). Your implementation should take different data sets as input for learning. b. Plot the resulting separating surface together with the data. To do this plotting you need to project the data and function into one or more 2D space. The best visual results will be if projection is done along the separating hyperplane (i.e. into a space described by the normal of the hyperplane and one of the dimension within the hyperplane) c. Evaluate the performance of your logistic regression classifier in the same way as for Project 1 using leaveone out validation and compare the results with the ones for KNN and Naïve Bayes Discuss what differences exist and why one method might outperform the others for this problem. d. Repeat the evaluation and comparison from part c) with the age feature removed. Again, discuss what differences exist and why one method might outperform the others in this case. See Answer
 Q 18 : CSE 6363  Machine Learning Data Set Use the dataset given at the bottom of this file. Do Not Use You are not allowed to use any ML libraries other than NumPy. You cannot use sklearn or any ML library. If used, you will receive a penalty of 90 points. You cannot use pandas. If used, you will receive a penalty of 20 points. Libraries You are allowed to use NumPy, math. You can use matplotlib to plot graphs. If you want to use any other library apart from these, please check with your GTA and get their approval. Where to code 1. We will provide you with a directory structure with python files for each part of every question. You must write your code in these files. 2. It will contain a script to execute the files. You must run this script and verify that your code runs before you submit. To run this script you must make it executable first or else you will get permission denied error. See Answer
 Q 19 : 1. Design and develop a text classifier which can be used as an amazon review categorizer. Your classifier must be able to train to classify reviews into one of two classes. Positive and negative reviews. Description can be found in the readme file. Please note that we are using only the test set as the dataset is huge. This test set contains 400k data points. a. Data set can be found in the canvas b. Use the TfidfVectorizer found in Sciekitlearn library in python to vectorize the dataset c. Use GaussianNB for the classifier d. Calculate the accuracy of the model. You need to use the data partitioning to create train set and test set from the data set given. e. Input a sample text and determine the class of the text provided See Answer
 Q 20 : Use the dataset given at the bottom of this file. See Answer
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Choosing an AWS machine learning service
Pick the right ML services and frameworks to support your work
Help determine which AWS ML services are the best fit for your needs.  
May 3, 2024  
Introduction
At its most basic, machine learning (ML) is designed to provide digital tools and services to learn from data, identify patterns, make predictions, and then act on those predictions. Almost all artificial intelligence (AI) systems today are created using ML. ML uses large amounts of data to create and validate decision logic. This decision logic forms the basis of the AI model .
Scenarios where AWS machine learning services may be applied include:
Specific use cases — AWS machine learning services can support your AI powered use cases with a broad range of prebuilt algorithms, models, and solutions for common use cases and industries. You have a choice of 23 pretrained services, including Amazon Personalize, Amazon Kendra, and Amazon Monitron.
Customizing and scaling machine learning — Amazon SageMaker is designed to help you build, train, and deploy ML models for any use case. You can build your own or access open source foundational models on AWS through Amazon SageMaker and Amazon Bedrock.
Accessing specialized infrastructure — Use the ML frameworks and infrastructure provided by AWS when you require even greater flexibility and control over your machine learning workflows, and are willing to manage the underlying infrastructure and resources yourself.
This decision guide will help you ask the right questions, evaluate your criteria and business problem, and determine which services are the best fit for your needs.
In this 7 minute video excerpt, Rajneesh Singh, general manager of Amazon SageMaker LowCode/NoCode team at AWS, explains how machine learning can address business problems.
As organizations continue to adopt AI and ML technologies, the importance of understanding and choosing among AWS ML services is an ongoing challenge.
AWS provides a range of ML services designed to help organizations to build, train, and deploy ML models more quickly and easily. These services can be used to solve a wide range of business problems such as customer churn prediction, fraud detection, and image and speech recognition.
Before diving deeper into AWS ML services, let's look at the relationship between AI and ML.
At a high level, artificial intelligence is a way to describe any system that can replicate tasks that previously required human intelligence. Most AI use cases are looking for a probabilistic outcome—making a prediction or decision with a high degree of certainty, similar to human judgement.
Almost all AI systems today are created using machine learning . ML uses large amounts of data to create and validate decision logic, which is known as a model.
Classification AI is a subset of ML that recognizes patterns to identify something. Predictive AI is a subset of ML that predicts future trends based on statistical patterns an historical data.
Finally, generative AI is a subset of deep learning that can create new content and ideas, like conversations, stories, images, videos, and music. Generative AI is powered by very large models that are pretrained on vast corpora of data, called the Foundation Models or FMs. Amazon Bedrock is a fully managed service that offers a choice of highperforming FMs for building and scaling generative AI applications. Amazon Q Developer and Amazon Q Business are generativeAI powered assistants for specific use cases.
This guide is designed primarily to cover services in the Classification AI and Predictive AI machine learning categories.
In addition, AWS offers specialized, accelerated hardware for high performance ML training and inference.
Amazon EC2 P5 instances are equipped with NVIDIA H100 Tensor Core GPUs, which are wellsuited for both training and inference tasks in machine learning. Amazon EC2 G5 instances feature up to 8 NVIDIA A10G Tensor Core GPUs, and second generation AMD EPYC processors, for a wide range of graphicsintensive and machine learning use cases.
AWS Trainium is the secondgeneration ML accelerator that AWS has purposebuilt for deep learning (DL) training of 100B+ parameter models.
AWS Inferentia2based Amazon EC2 Inf2 instances are designed to deliver high performance at the lowest cost in Amazon EC2 for your DL and generative AI inference applications.
When solving a business problem with AWS ML services, consideration of several key criteria can help ensure success. The following section outlines some of the key criteria to consider when choosing a ML service.
Problem definition
The first step in the ML lifecycle is to frame the business problem. Understanding the problem you are trying to solve is essential for choosing the right AWS ML service, as different services are designed to address different problems. It is also important to determine whether ML is the best fit for your business problem.
Once you have determined that ML is the best fit, you can start by choosing from a range of purposebuilt AWS AI services (in areas such as speech, vision and documents).
Amazon SageMaker provides fully managed infrastructure if you need to build and train your own models. AWS offers an array of advanced ML frameworks and infrastructure choices for the cases where you require highly customized and specialized ML models. AWS also offers a broad set of popular foundation models for building new applications with generative AI.
ML algorithm
Choosing the ML algorithm for the business problem you are trying to solve depends on the type of data you are working with, as well as the desired outcomes. The following information outlines how each of the major AWS AI/ML service categories empowers you to work with its algorithms:
Specialized AI services: These services offer a limited ability to customize the ML algorithm, as they are pretrained models optimized for specific tasks. You can typically customize the input data and some parameters, but do not have access to the underlying ML models or the ability to build your own models.
Amazon SageMaker: This service provides the most flexibility and control over the ML algorithm. You can use SageMaker to build custom models using your own algorithms and frameworks, or use prebuilt models and algorithms provided by AWS. This allows for a high degree of customization and control over the ML process.
Lowerlevel ML frameworks and infrastructure: These services offer the most flexibility and control over the ML algorithm. You can use these services to build highly customized ML models using their own algorithms and frameworks. However, using these services requires significant ML expertise and may not be feasible for all every use case.
If you need a private endpoint in your VPC, your options will vary based on the layer of AWS ML services you are using. These include:
Specialized AI services: Most specialized AI services do not currently support private endpoints in VPCs. However, Amazon Rekognition Custom Labels and Amazon Comprehend Custom can be accessed using VPC endpoints.
Core AI services: Amazon Translate, Amazon Transcribe, and Amazon Comprehend all support VPC endpoints.
Amazon SageMaker: SageMaker provides builtin support for VPC endpoints, allowing you to deploy their trained models as an endpoint accessible only from within their VPC.
Lowerlevel ML frameworks and infrastructure: You can deploy your models on Amazon EC2 instances or in containers within your VPC, providing complete control over the networking configuration.
Higherlevel AI services, such as Amazon Rekognition and Amazon Transcribe, are designed to handle a wide variety of use cases and offer high performance in terms of speed. However, they might not meet certain latency requirements.
If you are using lowerlevel ML frameworks and infrastructure, we recommended leveraging Amazon SageMaker. This option is generally faster than building custom models due to its fully managed service and optimized deployment options. While a highly optimized custom model may outperform SageMaker, it will require significant expertise and resources to build.
The accuracy of AWS ML services varies based on the specific use case and level of customization required. Higherlevel AI services, such as Amazon Rekognition, are built on pretrained models that have been optimized for specific tasks and offer high accuracy in many use cases.
In some cases, you can choose to use Amazon SageMaker, which provides a more flexible and customizable platform for building and training custom ML models. By building your own models, you may be able to achieve even higher accuracy than what is possible with pretrained models.
You can also choose to use ML frameworks and infrastructure, such as TensorFlow and Apache MXNet, to build highly customized models that offer the highest possible accuracy for your specific use case.
AWS and responsible AI
AWS builds foundation models (FMs) with responsible AI in mind at each stage of its development process. Throughout design, development, deployment, and operations we consider a range of factors including:
Accuracy (how closely a summary matches the underlying document; whether a biography is factually correct)
Fairness, (whether outputs treat demographic groups similarly)
Intellectual property and copyright considerations
Appropriate usage (filtering out user requests for legal advice, or medical diagnoses, or illegal activities)
Toxicity (hate speech, profanity, and insults)
Privacy (protecting personal information and customer prompts)
AWS builds solutions to address these issues into the processes used for acquiring training data, into the FMs themselves, and into the technology used to preprocess user prompts and postprocess outputs.
Now that you know the criteria by which you will be evaluating your ML service options, you are ready to choose which AWS ML service is right for your organizational needs. The following table highlights which ML services are optimized for which circumstances. Use it to help determine the AWS ML service that is the best fit for your use case.
These artificial intelligence services are intended to meet specific needs. They include personalization, forecasting, anomaly detection, speech transcription, and others. Since they are delivered as services, they can be embedded into applications without requiring any ML expertise.  Use the AI services provided by AWS when you require specific, prebuilt functionalities to be integrated into your applications, without the need for extensive customizations or machine learning expertise. These services are designed to be easy to use and do not require much coding or configuration.  These services are designed to be easy to use and do not require much coding, configuration, or ML expertise. 

These services can be used to develop customized machine learning models or workflows that go beyond the prebuilt functionalities offered by the core AI services.  Use these services when when you need more customized machine learning models or workflows that go beyond the prebuilt functionalities offered by the core AI services.  These services are optimized for building and training custom machine learning models, largescale training on multiple instances or GPU clusters, more control over machine learning model deployment, realtime inference, and for building endtoend workflows. 

To deploy machine learning in production, you need costeffective infrastructure, which Amazon enables with AWSbuilt silicon.  Use when you want to achieve the lowest cost for training models and need to run inference in the cloud.  Optimized for supporting the costeffective deployment of machine learning. 

These tools and associated services are designed to help you ease deployment of machine learning.  These services and tools are designed to help you accelerate deep learning in the cloud, providing Amazon machine images, docker images and entity resolution.  Optimized for helping you accelerate deep learning in the cloud. 

Now that you have a clear understanding of the criteria you need to apply in choosing an AWS ML service, you can select which AWS AI/ML service(s) are optimized for your business needs.
To explore how to use and learn more about the service(s) you have chosen, we have provided three sets of pathways to explore how each service works. The first set of pathways provides indepth documentation, handson tutorials, and resources to get started with Amazon Comprehend, Amazon Textract, Amazon Translate, Amazon Lex, Amazon Polly, Amazon Rekognition, and Amazon Transcribe.
Get started with Amazon Comprehend
Use the Amazon Comprehend console to create and run an asynchronous entity detection job.
Get started with the tutorial »
Analyze insights in text with Amazon Comprehend
Learn how to use Amazon Comprehend to analyze and derive insights from text.
Amazon Comprehend Pricing
Explore information on Amazon Comprehend pricing and examples.
Explore the guide »
Getting Started with Amazon Textract
Learn how Amazon Textract can be used with formatted text to detect words and lines of words that are located close to each other, as well as analyze a document for items such as related text, tables, keyvalue pairs, and selection elements.
Extract text and structured data with Amazon Textract
Learn how to use Amazon Textract to extract text and structured data from a document.
AWS Power Hour: Machine Learning
Dive into Amazon Textract in this episode, spend time in the AWS Management Console, and review code samples that will help you understand how to make the most of service APIs.
Watch the video »
Getting started with Amazon Translate using the console
The easiest way to get started with Amazon Translate is to use the console to translate some text. Learn how to translate up to 10,000 characters using the console.
Translate Text Between Languages in the Cloud
In this tutorial example, as part of an international luggage manufacturing firm, you need to understand what customers are saying about your product in reviews in the local market language  French.
Amazon Translate pricing
Explore Amazon Translate pricing, including Free Tier  which provides 2 million characters per month for 12 months.
Amazon Lex V2 Developer Guide
Explore information about getting started, how it works, and pricing information for Amazon Lex V2.
Introduction to Amazon Lex We introduce you to the Amazon Lex conversational service, and walk you through examples that show you how to create a bot and deploy it to different chat services.
Take the course » (signin required)
Exploring Generative AI in conversational experiences
Explore the use of generative AI in conversation experiences.
Read the blog »
What is Amazon Polly?
Explore a complete overview of the cloud service that converts text into lifelike speech, and can be used to develop applications to increase your customer engagement and accessibility.
Explore the guide »
Highlight text as it’s being spoken using Amazon Polly
We introduce you to approaches for highlighting text as it’s being spoken to add visual capabilities to audio in books, websites, blogs, and other digital experiences.
Read the blog »
Create audio for content in multiple languages with the same TTS voice persona in Amazon Polly
We explain Neural TexttoSpeech (NTTS) and discuss how a broad portfolio of available voices, providing a range of distinct speakers in supported languages, can work for you.
What is Amazon Rekognition?
Explore how you can use this service to add image and video analysis to your applications.
Handson Rekognition: Automated Image and Video Analysis
Learn how facial recognition works with streaming video, along with code examples and key points at a selfguided pace.
Get started with the tutorial »
Amazon Rekognition FAQs
Learn the basics of Amazon Rekognition and how it can help you improve your deep learning and visually analyze your applications.
Read the FAQs »
What is Amazon Transcribe?
Explore the AWS automatic speech recognition service using ML to convert audio to text. Learn how to use this service as a standalone transcription or add speechtotext capability to any application.
Amazon Transcribe Pricing
We introduce you to the AWS payasyougo transcription, including custom language model options and the Amazon Transcribe Free Tier.
Create an audio transcript with Amazon Transcribe
Learn how to use Amazon Transcribe to create a text transcript of recorded audio files using a realworld use case scenario for testing against your needs.
Build an Amazon Transcribe streaming app
Learn how to build an app to record, transcribe, and translate live audio in realtime, with results emailed directly to you.
The second set of AI/ML AWS service pathways provide indepth documentation, handson tutorials, and resources to get started with the services in the Amazon SageMaker family.
How Amazon SageMaker works
Explore the overview of machine learning and how SageMaker works.
Getting started with Amazon SageMaker
Learn how to join an Amazon SageMaker Domain, giving you access to Amazon SageMaker Studio and RStudio on SageMaker.
Use Apache Spark with Amazon SageMaker
Learn how to use Apache Spark for preprocessing data and SageMaker for model training and hosting.
Use Docker containers to build models
Explore how Amazon SageMaker makes extensive use of Docker containers for build and runtime tasks. Learn how to deploy the prebuilt Docker images for its builtin algorithms and the supported deep learning frameworks used for training and inference.
Machine learning frameworks and languages
Learn how to get started with SageMaker using the Amazon SageMaker Python SDK.
Create an Amazon SageMaker Autopilot experiment for tabular data
Learn you how to create an Amazon SageMaker Autopilot experiment to explore, preprocess, and train various model candidates on a tabular dataset.
Automatically create machine learning models
Learn how to use Amazon SageMaker Autopilot to automatically build, train, and tune a ML model, and deploy the model to make predictions.
Explore modeling with Amazon SageMaker Autopilot with these example notebooks
Explore example notebooks for direct marketing, customer churn prediction and how to bring your own data processing code to Amazon SageMaker Autopilot.
Get started using Amazon SageMaker Canvas
Learn how to get started with using SageMaker Canvas.
Generate machine learning predictions without writing code
This tutorial explains how to use Amazon SageMaker Canvas to build ML models and generate accurate predictions without writing a single line of code.
Dive deeper into SageMaker Canvas
Explore an indepth look at SageMaker Canvas and its visual, no code ML capabilities.
Read the blog »
Use Amazon SageMaker Canvas to make your first ML Model
Learn how to use Amazon SageMaker Canvas to create an ML model to assess customer retention, based on an email campaign for new products and services.
Get started with the lab »
Getting started with Amazon SageMaker Data Wrangler
Explore how set up SageMaker Data Wrangler and then provides a walkthrough using an existing example dataset.
Prepare training data for machine learning with minimal code
Learn how to prepare data for ML using Amazon SageMaker Data Wrangler.
SageMaker Data Wrangler deep dive workshop
Learn how to apply appropriate analysis types on your dataset to detect anomalies and issues, use the derived results/insights to formulate remedial actions in the course of transformations on your dataset, and test the right choice and sequence of transformations using quick modeling options provided by SageMaker Data Wrangler.
Get started with the workshop »
Getting Started with Amazon Ground Truth
Explore how to use the console to create a labeling job, assign a public or private workforce, and send the labeling job to your workforce. Learn how to monitor the progress of a labeling job.
Label Training Data for Machine Learning
Learn how to set up a labeling job in Amazon SageMaker Ground Truth to annotate training data for your ML model.
Getting started with Amazon Ground Truth Plus Explore how to complete the necessary steps to start an Amazon SageMaker Ground Truth Plus project, review labels, and satisfy SageMaker Ground Truth Plus prerequisites.
Get started with Amazon Ground Truth Watch how to get started with labeling your data in minutes through the SageMaker Ground Truth console.
Amazon SageMaker Ground Truth Plus – create training datasets without code or inhouse resources
Learn about Ground Truth Plus, a turnkey service that uses an expert workforce to deliver highquality training datasets fast, and reduces costs by up to 40 percent.
Get started with machine learning with SageMaker JumpStart
Explore SageMaker JumpStart solution templates that set up infrastructure for common use cases, and executable example notebooks for machine learning with SageMaker.
Get Started with your machine learning project quickly using Amazon SageMaker JumpStart
Learn how to fasttrack your ML project using pretrained models and prebuilt solutions offered by Amazon SageMaker JumpStart. You can then deploy the selected model through Amazon SageMaker Studio notebooks.
Get handson with Amazon SageMaker JumpStart with this Immersion Day workshop
Learn how the lowcode ML capabilities found in Amazon SageMaker Data Wrangler, Autopilot and Jumpstart, make it easier to experiment faster and bring highly accurate models to production.
Getting Started with Amazon SageMaker Pipelines
Learn how to create endtoend workflows that manage and deploy SageMaker jobs. SageMaker Pipelines comes with SageMaker Python SDK integration, so you can build each step of your pipeline using a Pythonbased interface.
Automate machine learning workflows
Learn how to create and automate endtoend machine learning (ML) workflows using Amazon SageMaker Pipelines, Amazon SageMaker Model Registry, and Amazon SageMaker Clarify.
How to create fully automated ML workflows with Amazon SageMaker Pipelines
Learn about Amazon SageMaker Pipelines, the world’s first ML CI/CD service designed to be accessible for every developer and data scientist. SageMaker Pipelines brings CI/CD pipelines to ML, reducing the coding time required.
Build and train a machine learning model locally
Learn how to build and train a ML model locally within your Amazon SageMaker Studio notebook.
SageMaker Studio integration with EMR workshop
Learn how to utilize distributed processing at scale to prepare data and subsequently train machine learning models.
The third set of AI/ML AWS service pathways provide indepth documentation, handson tutorials, and resources to get started with AWS Trainium, AWS Inferentia, and Amazon Titan.
Scaling distributed training with AWS Trainium and Amazon EKS
Learn how you can benefit from the general availability of Amazon EC2 Trn1 instances powered by AWS Trainium—a purposebuilt ML accelerator optimized to provide a highperformance, costeffective, and massively scalable platform for training deep learning models in the cloud.
Overview of AWS Trainium
Learn about AWS Trainium, the secondgeneration machine learning (ML) accelerator that AWS purpose built for deep learning training of 100B+ parameter models. Each Amazon Elastic Compute Cloud (EC2) Trn1 instance deploys up to 16 AWS Trainium accelerators to deliver a highperformance, lowcost solution for deep learning (DL) training in the cloud.
Recommended Trainium Instances
Explore how AWS Trainium instances are designed to provide high performance and cost efficiency for deep learning model inference workloads.
Overview of AWS Inferentia
Understand how accelerators are designed by AWS to deliver high performance at the lowest cost for your deep learning (DL) inference applications.
Explore the guide »
AWS Inferentia2 builds on AWS Inferentia1 by delivering 4x higher throughput and 10x lower latency
Understand what AWS Inferentia2 is optimized for  and explores how it was designed from the ground up to deliver higher performance while lowering the cost of LLMs and generative AI inference.
Machine learning inference using AWS Inferentia
Learn how to create an Amazon EKS cluster with nodes running Amazon EC2 Inf1 instances and (optionally) deploy a sample application. Amazon EC2 Inf1 instances are powered by AWS Inferentia chips, which are custom built by AWS to provide high performance and lowest cost inference in the cloud.
Overview of Amazon Titan
Explore how Amazon Titan FMs are pretrained on large datasets, making them powerful, generalpurpose models. Learn how you can use them as is  or privately  to customize them with your own data for a particular task without annotating large volumes of data.
Architecture diagrams
These reference architecture diagrams show examples of AWS AI and ML services in use.
Explore architecture diagrams »
Whitepapers
Explore whitepapers to help you get started and learn best practices in choosing and using AI/ML services.
Explore whitepapers »
AWS Solutions
Explore vetted solutions and architectural guidance for common use cases for AI and ML services.
Explore solutions »
Foundation models
Supported foundation models include:
Anthropic Claude
Cohere Command & Embed
AI21 Labs Jurassic
Stable Diffusion XL
Amazon Titan
Using Amazon Bedrock, you can experiment with a variety of foundation models and privately customize them with your data.
Use case or industryspecific services
Amazon Comprehend Medical
Amazon Fraud Detector
AWS HealthLake
Amazon Lookout for Equipment
Amazon Lookout for Metrics
Amazon Lookout for Vision
Amazon Monitron
AWS HealthOmics
AWS Panorama
Associated blog posts
Significant new capabilities make it easier to use Amazon Bedrock to build and scale generative AI applications – and achieve impressive results
AWS Inferentia and AWS Trainium deliver lowest cost to deploy Llama 3 models in Amazon SageMaker JumpStart
Revolutionize Customer Satisfaction with tailored reward models for your business on Amazon SageMaker
Amazon Personalize launches new recipes supporting larger item catalogs with lower latency
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