Course in Machine Learning


Program Description

Course in Machine Learning


This Specialization provides a case-based introduction to the exciting, high-demand field of machine learning. You’ll learn to analyze large and complex datasets, build applications that can make predictions from data, and create systems that adapt and improve over time. In the final Capstone Project, you’ll apply your skills to solve an original, real-world problem through implementation of machine learning algorithms.


Machine Learning Foundations: A Case Study Approach
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.

Machine Learning: Regression
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.

Machine Learning: Classification
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.

Coursera has 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).

Machine Learning: Clustering & Retrieval

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.

Machine Learning: Recommender Systems & Dimensionality Reduction
Case Study: Recommending Products

How does Amazon recommend products you might be interested in purchasing?

How does Netflix decide which movies or TV shows you might want to watch?

What if you are a new user, should Netflix just recommend the most popular movies?

Who might you form a new link with on Facebook or LinkedIn?

These questions are endemic to most service-based industries, and underlie the notion of collaborative filtering and the recommender systems deployed to solve these problems. In this fourth case study, you will explore these ideas in the context of recommending products based on customer reviews.

In this course, you will explore dimensionality reduction techniques for modeling high-dimensional data.

In the case of recommender systems, your data is represented as user-product relationships, with potentially millions of users and hundred of thousands of products. You will implement matrix factorization and latent factor models for the task of predicting new user-product relationships.

You will also use side information about products and users to improve predictions.

Learning Outcomes: By the end of this course, you will be able to:

  • Create a collaborative filtering system.
  • Reduce dimensionality of data using SVD, PCA, and random projections.
  • Perform matrix factorization using coordinate descent.
  • Deploy latent factor models as a recommender system.
  • Handle the cold start problem using side information.
  • Examine a product recommendation application.
  • Implement these techniques in Python.

Machine Learning Capstone: An Intelligent Application with Deep Learning
Have you ever wondered how a product recommender is built? How you can infer the underlying sentiment from reviews? How you can extract information from images to find visually-similar products to recommend? How you construct an application that does all of these things in real time, and provides a front-end user experience? That’s what you will build in this course!

Using what you’ve learned about machine learning thus far, you will build a general product recommender system that does much more than just find similar products You will combine images of products with product descriptions and their reviews to create a truly innovative intelligent application.

You’ve probably heard that Deep Learning is making news across the world as one of the most promising techniques in machine learning, especially for analyzing image data. With every industry dedicating resources to unlock the deep learning potential, to be competitive, you will want to use these models in tasks such as image tagging, object recognition, speech recognition, and text analysis.

In this capstone, you will build deep learning models using neural networks, explore what they are, what they do, and how. To remove the barrier introduced by designing, training, and tuning networks, and to be able to achieve high performance with less labeled data, you will also build deep learning classifiers tailored to your specific task using pre-trained models, which Coursera calls deep features.

As a core piece of this capstone project, you will implement a deep learning model for image-based product recommendation. You will then combine this visual model with text descriptions of products and information from reviews to build an exciting, end-to-end intelligent application that provides a novel product discovery experience.

You will then deploy it as a service, which you can share with your friends and potential employers.

Learning Outcomes: By the end of this capstone, you will be able to:

  • Explore a dataset of products, reviews and images.
  • Build a product recommender.
  • Describe how a neural network model is represented and how it encodes non-linear features.
  • Combine different types of layers and activation functions to obtain better performance.
  • Use pretrained models, such as deep features, for new classification tasks.
  • Describe how these models can be applied in computer vision, text analytics and speech recognition.
  • Use visual features to find the products your users want.
  • Incorporate review sentiment into the recommendation.
  • Build an end-to-end application.
  • Deploy it as a service.
  • Implement these techniques in Python.

Keystone Academic Solutions is an official affiliate partner of Coursera.

This school offers programs in:
  • English

Last updated August 16, 2017
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8 months
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