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Machine Learning

Master machine learning fundamentals in four hands-on courses

About This Specialization This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data. Created by: Industry Partners: 4 courses Follow the suggested order or choose your own. Projects Designed to help you practice and apply the skills you learn. Certificates Highlight your new skills on your resume or

Machine Learning

Machine Learning

Master machine learning fundamentals in four hands-on courses



About This Specialization

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
Created by:
Industry Partners:
courses
4 courses
Follow the suggested order or choose your own.
projects
Projects
Designed to help you practice and apply the skills you learn.
certificates
Certificates
Highlight your new skills on your resume or LinkedIn.
Projects Overview
Courses
Intermediate Specialization.
Some related experience required.


  1. COURSE 1

    Machine Learning Foundations: A Case Study Approach

    Current session: Oct 23
    Commitment
    6 weeks of study, 5-8 hours/week
    Subtitles
    English, Korean, Vietnamese, Chinese (Simplified)

    About the Course

    Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.

    WEEK 1
    Welcome
    Machine learning is everywhere, but is often operating behind the scenes.
    This introduction to the specialization provides you with insights into the power of machine learning, and the multitude of intelligent applications you personally will be able to develop and deploy upon completion.
    We also discuss who we are, how we got here, and our view of the future of intelligent applications.
    Reading · Important Update regarding the Machine Learning Specialization
    Reading · Slides presented in this module
    Video · Welcome to this course and specialization
    Video · Who we are
    Video · Machine learning is changing the world
    Video · Why a case study approach?
    Video · Specialization overview
    Video · How we got into ML
    Video · Who is this specialization for?
    Video · What you'll be able to do
    Video · The capstone and an example intelligent application
    Video · The future of intelligent applications
    Reading · Reading: Getting started with Python, IPython Notebook & GraphLab Create
    Reading · Reading: where should my files go?
    Reading · Download the IPython Notebook used in this lesson to follow along
    Video · Starting an IPython Notebook
    Video · Creating variables in Python
    Video · Conditional statements and loops in Python
    Video · Creating functions and lambdas in Python
    Reading · Download the IPython Notebook used in this lesson to follow along
    Video · Starting GraphLab Create & loading an SFrame
    Video · Canvas for data visualization
    Video · Interacting with columns of an SFrame
    Video · Using .apply() for data transformation

    WEEK 2
    Regression: Predicting House Prices
    This week you will build your first intelligent application that makes predictions from data.
    We will explore this idea within the context of our first case study, predicting house prices, where you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...).
    This is just one of the many places where regression can be applied.Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.
    You will also examine how to analyze the performance of your predictive model and implement regression in practice using an iPython notebook.
    Reading · Slides presented in this module
    Video · Predicting house prices: A case study in regression
    Video · What is the goal and how might you naively address it?
    Video · Linear Regression: A Model-Based Approach
    Video · Adding higher order effects
    Video · Evaluating overfitting via training/test split
    Video · Training/test curves
    Video · Adding other features
    Video · Other regression examples
    Video · Regression ML block diagram
    Quiz · Regression
    Reading · Download the IPython Notebook used in this lesson to follow along
    Video · Loading & exploring house sale data
    Video · Splitting the data into training and test sets
    Video · Learning a simple regression model to predict house prices from house size
    Video · Evaluating error (RMSE) of the simple model
    Video · Visualizing predictions of simple model with Matplotlib
    Video · Inspecting the model coefficients learned
    Video · Exploring other features of the data
    Video · Learning a model to predict house prices from more features
    Video · Applying learned models to predict price of an average house
    Video · Applying learned models to predict price of two fancy houses
    Reading · Reading: Predicting house prices assignment
    Quiz · Predicting house prices

    WEEK 3
    Classification: Analyzing Sentiment
    How do you guess whether a person felt positively or negatively about an experience, just from a short review they wrote?
    In our second case study, analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...).This task is an example of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification.
    You will analyze the accuracy of your classifier, implement an actual classifier in an iPython notebook, and take a first stab at a core piece of the intelligent application you will build and deploy in your capstone.
    Reading · Slides presented in this module
    Video · Analyzing the sentiment of reviews: A case study in classification
    Video · What is an intelligent restaurant review system?
    Video · Examples of classification tasks
    Video · Linear classifiers
    Video · Decision boundaries
    Video · Training and evaluating a classifier
    Video · What's a good accuracy?
    Video · False positives, false negatives, and confusion matrices
    Video · Learning curves
    Video · Class probabilities
    Video · Classification ML block diagram
    Quiz · Classification
    Reading · Download the IPython Notebook used in this lesson to follow along
    Video · Loading & exploring product review data
    Video · Creating the word count vector
    Video · Exploring the most popular product
    Video · Defining which reviews have positive or negative sentiment
    Video · Training a sentiment classifier
    Video · Evaluating a classifier & the ROC curve
    Video · Applying model to find most positive & negative reviews for a product
    Video · Exploring the most positive & negative aspects of a product
    Reading · Reading: Analyzing product sentiment assignment
    Quiz · Analyzing product sentiment

    WEEK 4
    Clustering and Similarity: Retrieving Documents
    A reader is interested in a specific news article and you want to find a similar articles to recommend. What is the right notion of similarity? How do I automatically search over documents to find the one that is most similar? How do I quantitatively represent the documents in the first place?
    In this third case study, retrieving documents, you will examine various document representations and an algorithm to retrieve the most similar subset. You will also consider structured representations of the documents that automatically group articles by similarity (e.g., document topic).
    You will actually build an intelligent document retrieval system for Wikipedia entries in an iPython notebook.
    Reading · Slides presented in this module
    Video · Document retrieval: A case study in clustering and measuring similarity
    Video · What is the document retrieval task?
    Video · Word count representation for measuring similarity
    Video · Prioritizing important words with tf-idf
    Video · Calculating tf-idf vectors
    Video · Retrieving similar documents using nearest neighbor search
    Video · Clustering documents task overview
    Video · Clustering documents: An unsupervised learning task
    Video · k-means: A clustering algorithm
    Video · Other examples of clustering
    Video · Clustering and similarity ML block diagram
    Quiz · Clustering and Similarity
    Reading · Download the IPython Notebook used in this lesson to follow along
    Video · Loading & exploring Wikipedia data
    Video · Exploring word counts
    Video · Computing & exploring TF-IDFs
    Video · Computing distances between Wikipedia articles
    Video · Building & exploring a nearest neighbors model for Wikipedia articles
    Video · Examples of document retrieval in action
    Reading · Reading: Retrieving Wikipedia articles assignment
    Quiz · Retrieving Wikipedia articles

    WEEK 5
    Recommending Products
    Ever wonder how Amazon forms its personalized product recommendations? How Netflix suggests movies to watch? How Pandora selects the next song to stream? How Facebook or LinkedIn finds people you might connect with? Underlying all of these technologies for personalized content is something called collaborative filtering.
    You will learn how to build such a recommender system using a variety of techniques, and explore their tradeoffs.
    One method we examine is matrix factorization, which learns features of users and products to form recommendations. In an iPython notebook, you will use these techniques to build a real song recommender system.
    Reading · Slides presented in this module
    Video · Recommender systems overview
    Video · Where we see recommender systems in action
    Video · Building a recommender system via classification
    Video · Collaborative filtering: People who bought this also bought...
    Video · Effect of popular items
    Video · Normalizing co-occurrence matrices and leveraging purchase histories
    Video · The matrix completion task
    Video · Recommendations from known user/item features
    Video · Predictions in matrix form
    Video · Discovering hidden structure by matrix factorization
    Video · Bringing it all together: Featurized matrix factorization
    Video · A performance metric for recommender systems
    Video · Optimal recommenders
    Video · Precision-recall curves
    Video · Recommender systems ML block diagram
    Quiz · Recommender Systems
    Reading · Download the IPython Notebook used in this lesson to follow along
    Video · Loading and exploring song data
    Video · Creating & evaluating a popularity-based song recommender
    Video · Creating & evaluating a personalized song recommender
    Video · Using precision-recall to compare recommender models
    Reading · Reading: Recommending songs assignment
    Quiz · Recommending songs

    WEEK 6
    Deep Learning: Searching for Images
    You’ve probably heard that Deep Learning is making news across the world as one of the most promising techniques in machine learning. Every industry is dedicating resources to unlock the deep learning potential, including for tasks such as image tagging, object recognition, speech recognition, and text analysis.
    In our final case study, searching for images, you will learn how layers of neural networks provide very descriptive (non-linear) features that provide impressive performance in image classification and retrieval tasks. You will then construct deep features, a transfer learning technique that allows you to use deep learning very easily, even when you have little data to train the model.
    Using iPhython notebooks, you will build an image classifier and an intelligent image retrieval system with deep learning.
    Reading · Slides presented in this module
    Video · Searching for images: A case study in deep learning
    Video · What is a visual product recommender?
    Video · Learning very non-linear features with neural networks
    Video · Application of deep learning to computer vision
    Video · Deep learning performance
    Video · Demo of deep learning model on ImageNet data
    Video · Other examples of deep learning in computer vision
    Video · Challenges of deep learning
    Video · Deep Features
    Video · Deep learning ML block diagram
    Quiz · Deep Learning
    Reading · Download the IPython Notebook used in this lesson to follow along
    Video · Loading image data
    Video · Training & evaluating a classifier using raw image pixels
    Video · Training & evaluating a classifier using deep features
    Reading · Download the IPython Notebook used in this lesson to follow along
    Video · Loading image data
    Video · Creating a nearest neighbors model for image retrieval
    Video · Querying the nearest neighbors model to retrieve images
    Video · Querying for the most similar images for car image
    Video · Displaying other example image retrievals with a Python lambda
    Reading · Reading: Deep features for image retrieval assignment
    Quiz · Deep features for image retrieval

    Closing Remarks
    In the conclusion of the course, we will describe the final stage in turning our machine learning tools into a service: deployment.
    We will also discuss some open challenges that the field of machine learning still faces, and where we think machine learning is heading. We conclude with an overview of what's in store for you in the rest of the specialization, and the amazing intelligent applications that are ahead for us as we evolve machine learning.
    Reading · Slides presented in this module
    Video · You've made it!
    Video · Deploying an ML service
    Video · What happens after deployment?
    Video · Open challenges in ML
    Video · Where is ML going?
    Video · What's ahead in the specialization
    Video · Thank you!

  2. COURSE 2

    Machine Learning: Regression

    Current session: Oct 23
    Commitment
    6 weeks of study, 5-8 hours/week
    Subtitles
    English

    About the Course

    Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to: -Describe the input and output of a regression model. -Compare and contrast bias and variance when modeling data. -Estimate model parameters using optimization algorithms. -Tune parameters with cross validation. -Analyze the performance of the model. -Describe the notion of sparsity and how LASSO leads to sparse solutions. -Deploy methods to select between models. -Exploit the model to form predictions. -Build a regression model to predict prices using a housing dataset. -Implement these techniques in Python.

    WEEK 1
    Welcome
    Regression is one of the most important and broadly used machine learning and statistics tools out there. It allows you to make predictions from data by learning the relationship between features of your data and some observed, continuous-valued response. Regression is used in a massive number of applications ranging from predicting stock prices to understanding gene regulatory networks.
    This introduction to the course provides you with an overview of the topics we will cover and the background knowledge and resources we assume you have.
    Reading · Slides presented in this module
    Video · Welcome!
    Video · What is the course about?
    Video · Outlining the first half of the course
    Video · Outlining the second half of the course
    Video · Assumed background
    Reading · Reading: Software tools you'll need

    Simple Linear Regression
    Our course starts from the most basic regression model: Just fitting a line to data. This simple model for forming predictions from a single, univariate feature of the data is appropriately called "simple linear regression".
    In this module, we describe the high-level regression task and then specialize these concepts to the simple linear regression case. You will learn how to formulate a simple regression model and fit the model to data using both a closed-form solution as well as an iterative optimization algorithm called gradient descent. Based on this fitted function, you will interpret the estimated model parameters and form predictions. You will also analyze the sensitivity of your fit to outlying observations.
    You will examine all of these concepts in the context of a case study of predicting house prices from the square feet of the house.
    Reading · Slides presented in this module
    Video · A case study in predicting house prices
    Video · Regression fundamentals: data & model
    Video · Regression fundamentals: the task
    Video · Regression ML block diagram
    Video · The simple linear regression model
    Video · The cost of using a given line
    Video · Using the fitted line
    Video · Interpreting the fitted line
    Video · Defining our least squares optimization objective
    Video · Finding maxima or minima analytically
    Video · Maximizing a 1d function: a worked example
    Video · Finding the max via hill climbing
    Video · Finding the min via hill descent
    Video · Choosing stepsize and convergence criteria
    Video · Gradients: derivatives in multiple dimensions
    Video · Gradient descent: multidimensional hill descent
    Video · Computing the gradient of RSS
    Video · Approach 1: closed-form solution
    Reading · Optional reading: worked-out example for closed-form solution
    Video · Approach 2: gradient descent
    Reading · Optional reading: worked-out example for gradient descent
    Video · Comparing the approaches
    Reading · Download notebooks to follow along
    Video · Influence of high leverage points: exploring the data
    Video · Influence of high leverage points: removing Center City
    Video · Influence of high leverage points: removing high-end towns
    Video · Asymmetric cost functions
    Video · A brief recap
    Quiz · Simple Linear Regression
    Reading · Reading: Fitting a simple linear regression model on housing data
    Quiz · Fitting a simple linear regression model on housing data

    WEEK 2
    Multiple Regression
    The next step in moving beyond simple linear regression is to consider "multiple regression" where multiple features of the data are used to form predictions.
    More specifically, in this module, you will learn how to build models of more complex relationship between a single variable (e.g., 'square feet') and the observed response (like 'house sales price'). This includes things like fitting a polynomial to your data, or capturing seasonal changes in the response value. You will also learn how to incorporate multiple input variables (e.g., 'square feet', '# bedrooms', '# bathrooms'). You will then be able to describe how all of these models can still be cast within the linear regression framework, but now using multiple "features". Within this multiple regression framework, you will fit models to data, interpret estimated coefficients, and form predictions.
    Here, you will also implement a gradient descent algorithm for fitting a multiple regression model.
    Reading · Slides presented in this module
    Video · Multiple regression intro
    Video · Polynomial regression
    Video · Modeling seasonality
    Video · Where we see seasonality
    Video · Regression with general features of 1 input
    Video · Motivating the use of multiple inputs
    Video · Defining notation
    Video · Regression with features of multiple inputs
    Video · Interpreting the multiple regression fit
    Reading · Optional reading: review of matrix algebra
    Video · Rewriting the single observation model in vector notation
    Video · Rewriting the model for all observations in matrix notation
    Video · Computing the cost of a D-dimensional curve
    Video · Computing the gradient of RSS
    Video · Approach 1: closed-form solution
    Video · Discussing the closed-form solution
    Video · Approach 2: gradient descent
    Video · Feature-by-feature update
    Video · Algorithmic summary of gradient descent approach
    Video · A brief recap
    Quiz · Multiple Regression
    Reading · Reading: Exploring different multiple regression models for house price prediction
    Quiz · Exploring different multiple regression models for house price prediction
    Reading · Numpy tutorial
    Reading · Reading: Implementing gradient descent for multiple regression
    Quiz · Implementing gradient descent for multiple regression

    WEEK 3
    Assessing Performance
    Having learned about linear regression models and algorithms for estimating the parameters of such models, you are now ready to assess how well your considered method should perform in predicting new data. You are also ready to select amongst possible models to choose the best performing.
    This module is all about these important topics of model selection and assessment. You will examine both theoretical and practical aspects of such analyses. You will first explore the concept of measuring the "loss" of your predictions, and use this to define training, test, and generalization error. For these measures of error, you will analyze how they vary with model complexity and how they might be utilized to form a valid assessment of predictive performance. This leads directly to an important conversation about the bias-variance tradeoff, which is fundamental to machine learning. Finally, you will devise a method to first select amongst models and then assess the performance of the selected model.
    The concepts described in this module are key to all machine learning problems, well-beyond the regression setting addressed in this course.
    Reading · Slides presented in this module
    Video · Assessing performance intro
    Video · What do we mean by "loss"?
    Video · Training error: assessing loss on the training set
    Video · Generalization error: what we really want
    Video · Test error: what we can actually compute
    Video · Defining overfitting
    Video · Training/test split
    Video · Irreducible error and bias
    Video · Variance and the bias-variance tradeoff
    Video · Error vs. amount of data
    Video · Formally defining the 3 sources of error
    Video · Formally deriving why 3 sources of error
    Video · Training/validation/test split for model selection, fitting, and assessment
    Video · A brief recap
    Quiz · Assessing Performance
    Reading · Reading: Exploring the bias-variance tradeoff
    Quiz · Exploring the bias-variance tradeoff

    WEEK 4
    Ridge Regression
    You have examined how the performance of a model varies with increasing model complexity, and can describe the potential pitfall of complex models becoming overfit to the training data. In this module, you will explore a very simple, but extremely effective technique for automatically coping with this issue. This method is called "ridge regression". You start out with a complex model, but now fit the model in a manner that not only incorporates a measure of fit to the training data, but also a term that biases the solution away from overfitted functions. To this end, you will explore symptoms of overfitted functions and use this to define a quantitative measure to use in your revised optimization objective. You will derive both a closed-form and gradient descent algorithm for fitting the ridge regression objective; these forms are small modifications from the original algorithms you derived for multiple regression. To select the strength of the bias away from overfitting, you will explore a general-purpose method called "cross validation".
    You will implement both cross-validation and gradient descent to fit a ridge regression model and select the regularization constant.
    Reading · Slides presented in this module
    Video · Symptoms of overfitting in polynomial regression
    Reading · Download the notebook and follow along
    Video · Overfitting demo
    Video · Overfitting for more general multiple regression models
    Video · Balancing fit and magnitude of coefficients
    Video · The resulting ridge objective and its extreme solutions
    Video · How ridge regression balances bias and variance
    Reading · Download the notebook and follow along
    Video · Ridge regression demo
    Video · The ridge coefficient path
    Video · Computing the gradient of the ridge objective
    Video · Approach 1: closed-form solution
    Video · Discussing the closed-form solution
    Video · Approach 2: gradient descent
    Video · Selecting tuning parameters via cross validation
    Video · K-fold cross validation
    Video · How to handle the intercept
    Video · A brief recap
    Quiz · Ridge Regression
    Reading · Reading: Observing effects of L2 penalty in polynomial regression
    Quiz · Observing effects of L2 penalty in polynomial regression
    Reading · Reading: Implementing ridge regression via gradient descent
    Quiz · Implementing ridge regression via gradient descent

    WEEK 5
    Feature Selection & Lasso
    A fundamental machine learning task is to select amongst a set of features to include in a model. In this module, you will explore this idea in the context of multiple regression, and describe how such feature selection is important for both interpretability and efficiency of forming predictions.
    To start, you will examine methods that search over an enumeration of models including different subsets of features. You will analyze both exhaustive search and greedy algorithms. Then, instead of an explicit enumeration, we turn to Lasso regression, which implicitly performs feature selection in a manner akin to ridge regression: A complex model is fit based on a measure of fit to the training data plus a measure of overfitting different than that used in ridge. This lasso method has had impact in numerous applied domains, and the ideas behind the method have fundamentally changed machine learning and statistics. You will also implement a coordinate descent algorithm for fitting a Lasso model.
    Coordinate descent is another, general, optimization technique, which is useful in many areas of machine learning.
    Reading · Slides presented in this module
    Video · The feature selection task
    Video · All subsets
    Video · Complexity of all subsets
    Video · Greedy algorithms
    Video · Complexity of the greedy forward stepwise algorithm
    Video · Can we use regularization for feature selection?
    Video · Thresholding ridge coefficients?
    Video · The lasso objective and its coefficient path
    Video · Visualizing the ridge cost
    Video · Visualizing the ridge solution
    Video · Visualizing the lasso cost and solution
    Reading · Download the notebook and follow along
    Video · Lasso demo
    Video · What makes the lasso objective different
    Video · Coordinate descent
    Video · Normalizing features
    Video · Coordinate descent for least squares regression (normalized features)
    Video · Coordinate descent for lasso (normalized features)
    Video · Assessing convergence and other lasso solvers
    Video · Coordinate descent for lasso (unnormalized features)
    Video · Deriving the lasso coordinate descent update
    Video · Choosing the penalty strength and other practical issues with lasso
    Video · A brief recap
    Quiz · Feature Selection and Lasso
    Reading · Reading: Using LASSO to select features
    Quiz · Using LASSO to select features
    Reading · Reading: Implementing LASSO using coordinate descent
    Quiz · Implementing LASSO using coordinate descent

    WEEK 6
    Nearest Neighbors & Kernel Regression
    Up to this point, we have focused on methods that fit parametric functions---like polynomials and hyperplanes---to the entire dataset. In this module, we instead turn our attention to a class of "nonparametric" methods. These methods allow the complexity of the model to increase as more data are observed, and result in fits that adapt locally to the observations.
    We start by considering the simple and intuitive example of nonparametric methods, nearest neighbor regression: The prediction for a query point is based on the outputs of the most related observations in the training set. This approach is extremely simple, but can provide excellent predictions, especially for large datasets. You will deploy algorithms to search for the nearest neighbors and form predictions based on the discovered neighbors. Building on this idea, we turn to kernel regression. Instead of forming predictions based on a small set of neighboring observations, kernel regression uses all observations in the dataset, but the impact of these observations on the predicted value is weighted by their similarity to the query point. You will analyze the theoretical performance of these methods in the limit of infinite training data, and explore the scenarios in which these methods work well versus struggle. You will also implement these techniques and observe their practical behavior.
    Reading · Slides presented in this module
    Video · Limitations of parametric regression
    Video · 1-Nearest neighbor regression approach
    Video · Distance metrics
    Video · 1-Nearest neighbor algorithm
    Video · k-Nearest neighbors regression
    Video · k-Nearest neighbors in practice
    Video · Weighted k-nearest neighbors
    Video · From weighted k-NN to kernel regression
    Video · Global fits of parametric models vs. local fits of kernel regression
    Video · Performance of NN as amount of data grows
    Video · Issues with high-dimensions, data scarcity, and computational complexity
    Video · k-NN for classification
    Video · A brief recap
    Quiz · Nearest Neighbors & Kernel Regression
    Reading · Reading: Predicting house prices using k-nearest neighbors regression
    Quiz · Predicting house prices using k-nearest neighbors regression

    Closing Remarks
    In the conclusion of the course, we will recap what we have covered. This represents both techniques specific to regression, as well as foundational machine learning concepts that will appear throughout the specialization. We also briefly discuss some important regression techniques we did not cover in this course.
    We conclude with an overview of what's in store for you in the rest of the specialization.
    Reading · Slides presented in this module
    Video · Simple and multiple regression
    Video · Assessing performance and ridge regression
    Video · Feature selection, lasso, and nearest neighbor regression
    Video · What we covered and what we didn't cover
    Video · Thank you!

  3. COURSE 3

    Machine Learning: Classification

    Current session: Oct 23
    Commitment
    7 weeks of study, 5-8 hours/week
    Subtitles
    English

    About the Course

    Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier. This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. We've also included optional content in every module, covering advanced topics for those who want to go even deeper! Learning Objectives: By the end of this course, you will be able to: -Describe the input and output of a classification model. -Tackle both binary and multiclass classification problems. -Implement a logistic regression model for large-scale classification. -Create a non-linear model using decision trees. -Improve the performance of any model using boosting. -Scale your methods with stochastic gradient ascent. -Describe the underlying decision boundaries. -Build a classification model to predict sentiment in a product review dataset. -Analyze financial data to predict loan defaults. -Use techniques for handling missing data. -Evaluate your models using precision-recall metrics. -Implement these techniques in Python (or in the language of your choice, though Python is highly recommended).

    WEEK 1
    Welcome!
    Classification is one of the most widely used techniques in machine learning, with a broad array of applications, including sentiment analysis, ad targeting, spam detection, risk assessment, medical diagnosis and image classification. The core goal of classification is to predict a category or class y from some inputs x. Through this course, you will become familiar with the fundamental models and algorithms used in classification, as well as a number of core machine learning concepts. Rather than covering all aspects of classification, you will focus on a few core techniques, which are widely used in the real-world to get state-of-the-art performance. By following our hands-on approach, you will implement your own algorithms on multiple real-world tasks, and deeply grasp the core techniques needed to be successful with these approaches in practice. This introduction to the course provides you with an overview of the topics we will cover and the background knowledge and resources we assume you have.
    Reading · Slides presented in this module
    Video · Welcome to the classification course, a part of the Machine Learning Specialization
    Video · What is this course about?
    Video · Impact of classification
    Video · Course overview
    Video · Outline of first half of course
    Video · Outline of second half of course
    Video · Assumed background
    Video · Let's get started!
    Reading · Reading: Software tools you'll need

    Linear Classifiers & Logistic Regression
    Linear classifiers are amongst the most practical classification methods. For example, in our sentiment analysis case-study, a linear classifier associates a coefficient with the counts of each word in the sentence. In this module, you will become proficient in this type of representation. You will focus on a particularly useful type of linear classifier called logistic regression, which, in addition to allowing you to predict a class, provides a probability associated with the prediction. These probabilities are extremely useful, since they provide a degree of confidence in the predictions. In this module, you will also be able to construct features from categorical inputs, and to tackle classification problems with more than two class (multiclass problems). You will examine the results of these techniques on a real-world product sentiment analysis task.
    Reading · Slides presented in this module
    Video · Linear classifiers: A motivating example
    Video · Intuition behind linear classifiers
    Video · Decision boundaries
    Video · Linear classifier model
    Video · Effect of coefficient values on decision boundary
    Video · Using features of the inputs
    Video · Predicting class probabilities
    Video · Review of basics of probabilities
    Video · Review of basics of conditional probabilities
    Video · Using probabilities in classification
    Video · Predicting class probabilities with (generalized) linear models
    Video · The sigmoid (or logistic) link function
    Video · Logistic regression model
    Video · Effect of coefficient values on predicted probabilities
    Video · Overview of learning logistic regression models
    Video · Encoding categorical inputs
    Video · Multiclass classification with 1 versus all
    Video · Recap of logistic regression classifier
    Quiz · Linear Classifiers & Logistic Regression
    Reading · Predicting sentiment from product reviews
    Quiz · Predicting sentiment from product reviews

    WEEK 2
    Learning Linear Classifiers
    Once familiar with linear classifiers and logistic regression, you can now dive in and write your first learning algorithm for classification. In particular, you will use gradient ascent to learn the coefficients of your classifier from data. You first will need to define the quality metric for these tasks using an approach called maximum likelihood estimation (MLE). You will also become familiar with a simple technique for selecting the step size for gradient ascent. An optional, advanced part of this module will cover the derivation of the gradient for logistic regression. You will implement your own learning algorithm for logistic regression from scratch, and use it to learn a sentiment analysis classifier.
    Reading · Slides presented in this module
    Video · Goal: Learning parameters of logistic regression
    Video · Intuition behind maximum likelihood estimation
    Video · Data likelihood
    Video · Finding best linear classifier with gradient ascent
    Video · Review of gradient ascent
    Video · Learning algorithm for logistic regression
    Video · Example of computing derivative for logistic regression
    Video · Interpreting derivative for logistic regression
    Video · Summary of gradient ascent for logistic regression
    Video · Choosing step size
    Video · Careful with step sizes that are too large
    Video · Rule of thumb for choosing step size
    Video · (VERY OPTIONAL) Deriving gradient of logistic regression: Log trick
    Video · (VERY OPTIONAL) Expressing the log-likelihood
    Video · (VERY OPTIONAL) Deriving probability y=-1 given x
    Video · (VERY OPTIONAL) Rewriting the log likelihood into a simpler form
    Video · (VERY OPTIONAL) Deriving gradient of log likelihood
    Video · Recap of learning logistic regression classifiers
    Quiz · Learning Linear Classifiers
    Reading · Implementing logistic regression from scratch
    Quiz · Implementing logistic regression from scratch

    Overfitting & Regularization in Logistic Regression
    As we saw in the regression course, overfitting is perhaps the most significant challenge you will face as you apply machine learning approaches in practice. This challenge can be particularly significant for logistic regression, as you will discover in this module, since we not only risk getting an overly complex decision boundary, but your classifier can also become overly confident about the probabilities it predicts. In this module, you will investigate overfitting in classification in significant detail, and obtain broad practical insights from some interesting visualizations of the classifiers' outputs. You will then add a regularization term to your optimization to mitigate overfitting. You will investigate both L2 regularization to penalize large coefficient values, and L1 regularization to obtain additional sparsity in the coefficients. Finally, you will modify your gradient ascent algorithm to learn regularized logistic regression classifiers. You will implement your own regularized logistic regression classifier from scratch, and investigate the impact of the L2 penalty on real-world sentiment analysis data.
    Reading · Slides presented in this module
    Video · Evaluating a classifier
    Video · Review of overfitting in regression
    Video · Overfitting in classification
    Video · Visualizing overfitting with high-degree polynomial features
    Video · Overfitting in classifiers leads to overconfident predictions
    Video · Visualizing overconfident predictions
    Video · (OPTIONAL) Another perspecting on overfitting in logistic regression
    Video · Penalizing large coefficients to mitigate overfitting
    Video · L2 regularized logistic regression
    Video · Visualizing effect of L2 regularization in logistic regression
    Video · Learning L2 regularized logistic regression with gradient ascent
    Video · Sparse logistic regression with L1 regularization
    Video · Recap of overfitting & regularization in logistic regression
    Quiz · Overfitting & Regularization in Logistic Regression
    Reading · Logistic Regression with L2 regularization
    Quiz · Logistic Regression with L2 regularization

    WEEK 3
    Decision Trees
    Along with linear classifiers, decision trees are amongst the most widely used classification techniques in the real world. This method is extremely intuitive, simple to implement and provides interpretable predictions. In this module, you will become familiar with the core decision trees representation. You will then design a simple, recursive greedy algorithm to learn decision trees from data. Finally, you will extend this approach to deal with continuous inputs, a fundamental requirement for practical problems. In this module, you will investigate a brand new case-study in the financial sector: predicting the risk associated with a bank loan. You will implement your own decision tree learning algorithm on real loan data.
    Reading · Slides presented in this module
    Video · Predicting loan defaults with decision trees
    Video · Intuition behind decision trees
    Video · Task of learning decision trees from data
    Video · Recursive greedy algorithm
    Video · Learning a decision stump
    Video · Selecting best feature to split on
    Video · When to stop recursing
    Video · Making predictions with decision trees
    Video · Multiclass classification with decision trees
    Video · Threshold splits for continuous inputs
    Video · (OPTIONAL) Picking the best threshold to split on
    Video · Visualizing decision boundaries
    Video · Recap of decision trees
    Quiz · Decision Trees
    Reading · Identifying safe loans with decision trees
    Quiz · Identifying safe loans with decision trees
    Reading · Implementing binary decision trees
    Quiz · Implementing binary decision trees

    WEEK 4
    Preventing Overfitting in Decision Trees
    Out of all machine learning techniques, decision trees are amongst the most prone to overfitting. No practical implementation is possible without including approaches that mitigate this challenge. In this module, through various visualizations and investigations, you will investigate why decision trees suffer from significant overfitting problems. Using the principle of Occam's razor, you will mitigate overfitting by learning simpler trees. At first, you will design algorithms that stop the learning process before the decision trees become overly complex. In an optional segment, you will design a very practical approach that learns an overly-complex tree, and then simplifies it with pruning. Your implementation will investigate the effect of these techniques on mitigating overfitting on our real-world loan data set.
    Reading · Slides presented in this module
    Video · A review of overfitting
    Video · Overfitting in decision trees
    Video · Principle of Occam's razor: Learning simpler decision trees
    Video · Early stopping in learning decision trees
    Video · (OPTIONAL) Motivating pruning
    Video · (OPTIONAL) Pruning decision trees to avoid overfitting
    Video · (OPTIONAL) Tree pruning algorithm
    Video · Recap of overfitting and regularization in decision trees
    Quiz · Preventing Overfitting in Decision Trees
    Reading · Decision Trees in Practice
    Quiz · Decision Trees in Practice

    Handling Missing Data
    Real-world machine learning problems are fraught with missing data. That is, very often, some of the inputs are not observed for all data points. This challenge is very significant, happens in most cases, and needs to be addressed carefully to obtain great performance. And, this issue is rarely discussed in machine learning courses. In this module, you will tackle the missing data challenge head on. You will start with the two most basic techniques to convert a dataset with missing data into a clean dataset, namely skipping missing values and inputing missing values. In an advanced section, you will also design a modification of the decision tree learning algorithm that builds decisions about missing data right into the model. You will also explore these techniques in your real-data implementation.
    Reading · Slides presented in this module
    Video · Challenge of missing data
    Video · Strategy 1: Purification by skipping missing data
    Video · Strategy 2: Purification by imputing missing data
    Video · Modifying decision trees to handle missing data
    Video · Feature split selection with missing data
    Video · Recap of handling missing data
    Quiz · Handling Missing Data

    WEEK 5
    Boosting
    One of the most exciting theoretical questions that have been asked about machine learning is whether simple classifiers can be combined into a highly accurate ensemble. This question lead to the developing of boosting, one of the most important and practical techniques in machine learning today. This simple approach can boost the accuracy of any classifier, and is widely used in practice, e.g., it's used by more than half of the teams who win the Kaggle machine learning competitions. In this module, you will first define the ensemble classifier, where multiple models vote on the best prediction. You will then explore a boosting algorithm called AdaBoost, which provides a great approach for boosting classifiers. Through visualizations, you will become familiar with many of the practical aspects of this techniques. You will create your very own implementation of AdaBoost, from scratch, and use it to boost the performance of your loan risk predictor on real data.
    Reading · Slides presented in this module
    Video · The boosting question
    Video · Ensemble classifiers
    Video · Boosting
    Video · AdaBoost overview
    Video · Weighted error
    Video · Computing coefficient of each ensemble component
    Video · Reweighing data to focus on mistakes
    Video · Normalizing weights
    Video · Example of AdaBoost in action
    Video · Learning boosted decision stumps with AdaBoost
    Reading · Exploring Ensemble Methods
    Quiz · Exploring Ensemble Methods
    Video · The Boosting Theorem
    Video · Overfitting in boosting
    Video · Ensemble methods, impact of boosting & quick recap
    Quiz · Boosting
    Reading · Boosting a decision stump
    Quiz · Boosting a decision stump

    WEEK 6
    Precision-Recall
    In many real-world settings, accuracy or error are not the best quality metrics for classification. You will explore a case-study that significantly highlights this issue: using sentiment analysis to display positive reviews on a restaurant website. Instead of accuracy, you will define two metrics: precision and recall, which are widely used in real-world applications to measure the quality of classifiers. You will explore how the probabilities output by your classifier can be used to trade-off precision with recall, and dive into this spectrum, using precision-recall curves. In your hands-on implementation, you will compute these metrics with your learned classifier on real-world sentiment analysis data.
    Reading · Slides presented in this module
    Video · Case-study where accuracy is not best metric for classification
    Video · What is good performance for a classifier?
    Video · Precision: Fraction of positive predictions that are actually positive
    Video · Recall: Fraction of positive data predicted to be positive
    Video · Precision-recall extremes
    Video · Trading off precision and recall
    Video · Precision-recall curve
    Video · Recap of precision-recall
    Quiz · Precision-Recall
    Reading · Exploring precision and recall
    Quiz · Exploring precision and recall

    WEEK 7
    Scaling to Huge Datasets & Online Learning
    With the advent of the internet, the growth of social media, and the embedding of sensors in the world, the magnitudes of data that our machine learning algorithms must handle have grown tremendously over the last decade. This effect is sometimes called "Big Data". Thus, our learning algorithms must scale to bigger and bigger datasets. In this module, you will develop a small modification of gradient ascent called stochastic gradient, which provides significant speedups in the running time of our algorithms. This simple change can drastically improve scaling, but makes the algorithm less stable and harder to use in practice. In this module, you will investigate the practical techniques needed to make stochastic gradient viable, and to thus to obtain learning algorithms that scale to huge datasets. You will also address a new kind of machine learning problem, online learning, where the data streams in over time, and we must learn the coefficients as the data arrives. This task can also be solved with stochastic gradient. You will implement your very own stochastic gradient ascent algorithm for logistic regression from scratch, and evaluate it on sentiment analysis data.
    Reading · Slides presented in this module
    Video · Gradient ascent won't scale to today's huge datasets
    Video · Timeline of scalable machine learning & stochastic gradient
    Video · Why gradient ascent won't scale
    Video · Stochastic gradient: Learning one data point at a time
    Video · Comparing gradient to stochastic gradient
    Video · Why would stochastic gradient ever work?
    Video · Convergence paths
    Video · Shuffle data before running stochastic gradient
    Video · Choosing step size
    Video · Don't trust last coefficients
    Video · (OPTIONAL) Learning from batches of data
    Video · (OPTIONAL) Measuring convergence
    Video · (OPTIONAL) Adding regularization
    Video · The online learning task
    Video · Using stochastic gradient for online learning
    Video · Scaling to huge datasets through parallelization & module recap
    Quiz · Scaling to Huge Datasets & Online Learning
    Reading · Training Logistic Regression via Stochastic Gradient Ascent
    Quiz · Training Logistic Regression via Stochastic Gradient Ascent

  4. COURSE 4

    Machine Learning: Clustering & Retrieval

    Current session: Oct 23
    Commitment
    6 weeks of study, 5-8 hours/week
    Subtitles
    English

    About the Course

    Case Studies: Finding Similar Documents A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover? In this third case study, finding similar documents, you will examine similarity-based algorithms for retrieval. In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce. Learning Outcomes: By the end of this course, you will be able to: -Create a document retrieval system using k-nearest neighbors. -Identify various similarity metrics for text data. -Reduce computations in k-nearest neighbor search by using KD-trees. -Produce approximate nearest neighbors using locality sensitive hashing. -Compare and contrast supervised and unsupervised learning tasks. -Cluster documents by topic using k-means. -Describe how to parallelize k-means using MapReduce. -Examine probabilistic clustering approaches using mixtures models. -Fit a mixture of Gaussian model using expectation maximization (EM). -Perform mixed membership modeling using latent Dirichlet allocation (LDA). -Describe the steps of a Gibbs sampler and how to use its output to draw inferences. -Compare and contrast initialization techniques for non-convex optimization objectives. -Implement these techniques in Python.

    WEEK 1
    Welcome
    Clustering and retrieval are some of the most high-impact machine learning tools out there. Retrieval is used in almost every applications and device we interact with, like in providing a set of products related to one a shopper is currently considering, or a list of people you might want to connect with on a social media platform. Clustering can be used to aid retrieval, but is a more broadly useful tool for automatically discovering structure in data, like uncovering groups of similar patients.
    This introduction to the course provides you with an overview of the topics we will cover and the background knowledge and resources we assume you have.
    Reading · Slides presented in this module
    Video · Welcome and introduction to clustering and retrieval tasks
    Video · Course overview
    Video · Module-by-module topics covered
    Video · Assumed background
    Reading · Software tools you'll need for this course
    Reading · A big week ahead!

    WEEK 2
    Nearest Neighbor Search
    We start the course by considering a retrieval task of fetching a document similar to one someone is currently reading. We cast this problem as one of nearest neighbor search, which is a concept we have seen in the Foundations and Regression courses. However, here, you will take a deep dive into two critical components of the algorithms: the data representation and metric for measuring similarity between pairs of datapoints. You will examine the computational burden of the naive nearest neighbor search algorithm, and instead implement scalable alternatives using KD-trees for handling large datasets and locality sensitive hashing (LSH) for providing approximate nearest neighbors, even in high-dimensional spaces. You will explore all of these ideas on a Wikipedia dataset, comparing and contrasting the impact of the various choices you can make on the nearest neighbor results produced.
    Reading · Slides presented in this module
    Video · Retrieval as k-nearest neighbor search
    Video · 1-NN algorithm
    Video · k-NN algorithm
    Video · Document representation
    Video · Distance metrics: Euclidean and scaled Euclidean
    Video · Writing (scaled) Euclidean distance using (weighted) inner products
    Video · Distance metrics: Cosine similarity
    Video · To normalize or not and other distance considerations
    Quiz · Representations and metrics
    Reading · Choosing features and metrics for nearest neighbor search
    Quiz · Choosing features and metrics for nearest neighbor search
    Video · Complexity of brute force search
    Video · KD-tree representation
    Video · NN search with KD-trees
    Video · Complexity of NN search with KD-trees
    Video · Visualizing scaling behavior of KD-trees
    Video · Approximate k-NN search using KD-trees
    Reading · (OPTIONAL) A worked-out example for KD-trees
    Quiz · KD-trees
    Video · Limitations of KD-trees
    Video · LSH as an alternative to KD-trees
    Video · Using random lines to partition points
    Video · Defining more bins
    Video · Searching neighboring bins
    Video · LSH in higher dimensions
    Video · (OPTIONAL) Improving efficiency through multiple tables
    Quiz · Locality Sensitive Hashing
    Reading · Implementing Locality Sensitive Hashing from scratch
    Quiz · Implementing Locality Sensitive Hashing from scratch
    Video · A brief recap

    WEEK 3
    Clustering with k-means
    In clustering, our goal is to group the datapoints in our dataset into disjoint sets. Motivated by our document analysis case study, you will use clustering to discover thematic groups of articles by "topic". These topics are not provided in this unsupervised learning task; rather, the idea is to output such cluster labels that can be post-facto associated with known topics like "Science", "World News", etc. Even without such post-facto labels, you will examine how the clustering output can provide insights into the relationships between datapoints in the dataset. The first clustering algorithm you will implement is k-means, which is the most widely used clustering algorithm out there. To scale up k-means, you will learn about the general MapReduce framework for parallelizing and distributing computations, and then how the iterates of k-means can utilize this framework. You will show that k-means can provide an interpretable grouping of Wikipedia articles when appropriately tuned.
    Reading · Slides presented in this module
    Video · The goal of clustering
    Video · An unsupervised task
    Video · Hope for unsupervised learning, and some challenge cases
    Video · The k-means algorithm
    Video · k-means as coordinate descent
    Video · Smart initialization via k-means++
    Video · Assessing the quality and choosing the number of clusters
    Quiz · k-means
    Reading · Clustering text data with k-means
    Quiz · Clustering text data with K-means
    Video · Motivating MapReduce
    Video · The general MapReduce abstraction
    Video · MapReduce execution overview and combiners
    Video · MapReduce for k-means
    Quiz · MapReduce for k-means
    Video · Other applications of clustering
    Video · A brief recap

    WEEK 4
    Mixture Models
    In k-means, observations are each hard-assigned to a single cluster, and these assignments are based just on the cluster centers, rather than also incorporating shape information. In our second module on clustering, you will perform probabilistic model-based clustering that provides (1) a more descriptive notion of a "cluster" and (2) accounts for uncertainty in assignments of datapoints to clusters via "soft assignments". You will explore and implement a broadly useful algorithm called expectation maximization (EM) for inferring these soft assignments, as well as the model parameters. To gain intuition, you will first consider a visually appealing image clustering task. You will then cluster Wikipedia articles, handling the high-dimensionality of the tf-idf document representation considered.
    Reading · Slides presented in this module
    Video · Motiving probabilistic clustering models
    Video · Aggregating over unknown classes in an image dataset
    Video · Univariate Gaussian distributions
    Video · Bivariate and multivariate Gaussians
    Video · Mixture of Gaussians
    Video · Interpreting the mixture of Gaussian terms
    Video · Scaling mixtures of Gaussians for document clustering
    Video · Computing soft assignments from known cluster parameters
    Video · (OPTIONAL) Responsibilities as Bayes' rule
    Video · Estimating cluster parameters from known cluster assignments
    Video · Estimating cluster parameters from soft assignments
    Video · EM iterates in equations and pictures
    Video · Convergence, initialization, and overfitting of EM
    Video · Relationship to k-means
    Reading · (OPTIONAL) A worked-out example for EM
    Quiz · EM for Gaussian mixtures
    Video · A brief recap
    Reading · Implementing EM for Gaussian mixtures
    Quiz · Implementing EM for Gaussian mixtures
    Reading · Clustering text data with Gaussian mixtures
    Quiz · Clustering text data with Gaussian mixtures

    WEEK 5
    Mixed Membership Modeling via Latent Dirichlet Allocation
    The clustering model inherently assumes that data divide into disjoint sets, e.g., documents by topic. But, often our data objects are better described via memberships in a collection of sets, e.g., multiple topics. In our fourth module, you will explore latent Dirichlet allocation (LDA) as an example of such a mixed membership model particularly useful in document analysis. You will interpret the output of LDA, and various ways the output can be utilized, like as a set of learned document features. The mixed membership modeling ideas you learn about through LDA for document analysis carry over to many other interesting models and applications, like social network models where people have multiple affiliations.
    Throughout this module, we introduce aspects of Bayesian modeling and a Bayesian inference algorithm called Gibbs sampling. You will be able to implement a Gibbs sampler for LDA by the end of the module.
    Reading · Slides presented in this module
    Video · Mixed membership models for documents
    Video · An alternative document clustering model
    Video · Components of latent Dirichlet allocation model
    Video · Goal of LDA inference
    Quiz · Latent Dirichlet Allocation
    Video · The need for Bayesian inference
    Video · Gibbs sampling from 10,000 feet
    Video · A standard Gibbs sampler for LDA
    Video · What is collapsed Gibbs sampling?
    Video · A worked example for LDA: Initial setup
    Video · A worked example for LDA: Deriving the resampling distribution
    Video · Using the output of collapsed Gibbs sampling
    Video · A brief recap
    Quiz · Learning LDA model via Gibbs sampling
    Reading · Modeling text topics with Latent Dirichlet Allocation
    Quiz · Modeling text topics with Latent Dirichlet Allocation

    WEEK 6
    Hierarchical Clustering & Closing Remarks
    In the conclusion of the course, we will recap what we have covered. This represents both techniques specific to clustering and retrieval, as well as foundational machine learning concepts that are more broadly useful.
    We provide a quick tour into an alternative clustering approach called hierarchical clustering, which you will experiment with on the Wikipedia dataset. Following this exploration, we discuss how clustering-type ideas can be applied in other areas like segmenting time series. We then briefly outline some important clustering and retrieval ideas that we did not cover in this course.
    We conclude with an overview of what's in store for you in the rest of the specialization.
    Reading · Slides presented in this module
    Video · Module 1 recap
    Video · Module 2 recap
    Video · Module 3 recap
    Video · Module 4 recap
    Video · Why hierarchical clustering?
    Video · Divisive clustering
    Video · Agglomerative clustering
    Video · The dendrogram
    Video · Agglomerative clustering details
    Video · Hidden Markov models
    Reading · Modeling text data with a hierarchy of clusters
    Quiz · Modeling text data with a hierarchy of clusters
    Video · What we didn't cover
    Video · Thank you!

Creators

The University of Washington is a national and international leader in the core fields that are driving data science: computer science, statistics, human-centered design, and applied math.
Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world.


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Deep Learning Specialization

Master Deep Learning, and Break into AI
About This Specialization If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning. In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice. AI is transforming m…

Launch Your Career in Data Science

A nine-course introduction to data science, developed and taught by leading professors.
About This Specialization Ask the right questions, manipulate data sets, and create visualizations to communicate results. This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material. Created by: Industry Partners: 10 courses Follow the suggested order or choose your own. Projects Designed to help you practice and apply the skills you learn.

Master of Computer Science in Data Science

A flexible and affordable degree from one of the top Computer Science programs in the world, focused on one of the hottest fields of the new millennium

Enroll in the Master of Computer Science in Data Science (MCS-DS) and gain access to the computational and statistical knowledge needed to turn big data into meaningful insights. Build expertise in four core areas of computer science—data visualization, machine learning, data mining, and cloud computing—while learning key skills in statistics and information science. This completely online degree is an affordable gateway to one of the most lucrative and fastest growing careers of the new millennium. The MCS-DS is offered by CS @ ILLINOIS, a U.S. News & World Report top five CS graduate program, in collaboration with the University’s Statistics Department and top-ranked iSchool. Join our alumni network of entrepreneurs, educators, and technical visionaries, who have revolutionized the way people communicate, shop, conduct business,…

An Introduction to Interactive Programming in Python (Part 1)

About this course: This two-part course is designed to help students with very little or no computing background learn the basics of building simple interactive applications. Our language of choice, Python, is an easy-to learn, high-level computer language that is used in many of the computational courses offered on Coursera. To make learning Python easy, we have developed a new browser-based programming environment that makes developing interactive applications in Python simple. These applications will involve windows whose contents are graphical and respond to buttons, the keyboard and the mouse. In part 1 of this course, we will introduce the basic elements of programming (such as expressions, conditionals, and functions) and then use these elements to create simple interactive applications such as a digital stopwatch. Part 1 of this class will culminate in building a version of the classic arcade game "Pong".
Who is this class for: Recommended Background - A knowledge o…

Программирование на Python

About this course: Python – простой, гибкий и невероятно популярный язык, который используется практически во всех областях современной разработки. С его помощью можно создавать веб-приложения, писать игры, заниматься анализом данных, автоматизировать задачи системного администрирования и многое другое. “Программирование на Python” читают разработчики, применяющие Python в проектах, которыми ежедневно пользуются миллионы людей. Курс покрывает все необходимые для ежедневной работы программиста темы, а также рассказывает про многие особенности языка, которые часто опускают при его изучении. В ходе курса вы изучите конструкции языка, типы и структуры данных, функции, научитесь применять объектно-ориентированное и функциональное программирование, узнаете про особенности реализации Python, научитесь писать асинхронный и многопоточный код. Помимо теории вас ждут практические задания, которые помогут проверить полученные знания и отточить навыки программирования на Python. После успешного о…

Front-End JavaScript Frameworks: Angular

About this course: This course concentrates mainly on Javascript based front-end frameworks, and in particular the Angular framework (Currently Ver. 4.x). This course will use Typescript for developing Angular application. Typescript features will be introduced in the context of Angular as part of the exercises. You will also get an introduction to the use of Angular Material and Angular Flex-Layout for responsive UI design. You will be introduced to various aspects of Angular including components, directives and services. You will learn about data binding, Angular router and its use for developing single-page applications. You will also learn about designing both template-driven forms and reactive forms. A quick introduction to Observables, reactive programming and RxJS in the context of Angular is included. You will then learn about Angular support for client-server communication and the use of REST API on the server side. You will use Restangular for communicating with a server sup…