Machine Learning Foundations: A Case Study Approach

About this 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.

Created by:  University of Washington

• Taught by:  Carlos Guestrin, Amazon Professor of Machine Learning

  Computer Science and Engineering

• Taught by:  Emily Fox, Amazon Professor of Machine Learning

  Statistics

Basic Info Course 1 of 4 in the Machine Learning Specialization Commitment 6 weeks of study, 5-8 hours/week Language English, Subtitles: Korean, Vietnamese, Chinese (Simplified) How To Pass Pass all graded assignments to complete the course. User Ratings 4.6 stars Average User Rating 4.6See what learners said Coursework

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University of Washington 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.

Syllabus


WEEK 1


Welcome



Machine learning is everywhere, but is often operating behind the scenes. <p>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.</p>We also discuss who we are, how we got here, and our view of the future of intelligent applications.


18 videos, 6 readings expand

1. Reading: Important Update regarding the Machine Learning Specialization
2. Reading: Slides presented in this module
3. Video: Welcome to this course and specialization
4. Video: Who we are
5. Video: Machine learning is changing the world
6. Video: Why a case study approach?
7. Video: Specialization overview
8. Video: How we got into ML
9. Video: Who is this specialization for?
10. Video: What you'll be able to do
11. Video: The capstone and an example intelligent application
12. Video: The future of intelligent applications
13. Reading: Reading: Getting started with Python, IPython Notebook & GraphLab Create
14. Reading: Reading: where should my files go?
15. Reading: Download the IPython Notebook used in this lesson to follow along
16. Video: Starting an IPython Notebook
17. Video: Creating variables in Python
18. Video: Conditional statements and loops in Python
19. Video: Creating functions and lambdas in Python
20. Reading: Download the IPython Notebook used in this lesson to follow along
21. Video: Starting GraphLab Create & loading an SFrame
22. Video: Canvas for data visualization
23. Video: Interacting with columns of an SFrame
24. 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.<p>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,...). <p>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.</p>You will also examine how to analyze the performance of your predictive model and implement regression in practice using an iPython notebook.


19 videos, 3 readings expand

1. Reading: Slides presented in this module
2. Video: Predicting house prices: A case study in regression
3. Video: What is the goal and how might you naively address it?
4. Video: Linear Regression: A Model-Based Approach
5. Video: Adding higher order effects
6. Video: Evaluating overfitting via training/test split
7. Video: Training/test curves
8. Video: Adding other features
9. Video: Other regression examples
10. Video: Regression ML block diagram
11. Reading: Download the IPython Notebook used in this lesson to follow along
12. Video: Loading & exploring house sale data
13. Video: Splitting the data into training and test sets
14. Video: Learning a simple regression model to predict house prices from house size
15. Video: Evaluating error (RMSE) of the simple model
16. Video: Visualizing predictions of simple model with Matplotlib
17. Video: Inspecting the model coefficients learned
18. Video: Exploring other features of the data
19. Video: Learning a model to predict house prices from more features
20. Video: Applying learned models to predict price of an average house
21. Video: Applying learned models to predict price of two fancy houses
22. Reading: Reading: Predicting house prices assignment

Graded: Regression
Graded: 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?<p>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.</p>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.


19 videos, 3 readings expand

1. Reading: Slides presented in this module
2. Video: Analyzing the sentiment of reviews: A case study in classification
3. Video: What is an intelligent restaurant review system?
4. Video: Examples of classification tasks
5. Video: Linear classifiers
6. Video: Decision boundaries
7. Video: Training and evaluating a classifier
8. Video: What's a good accuracy?
9. Video: False positives, false negatives, and confusion matrices
10. Video: Learning curves
11. Video: Class probabilities
12. Video: Classification ML block diagram
13. Reading: Download the IPython Notebook used in this lesson to follow along
14. Video: Loading & exploring product review data
15. Video: Creating the word count vector
16. Video: Exploring the most popular product
17. Video: Defining which reviews have positive or negative sentiment
18. Video: Training a sentiment classifier
19. Video: Evaluating a classifier & the ROC curve
20. Video: Applying model to find most positive & negative reviews for a product
21. Video: Exploring the most positive & negative aspects of a product
22. Reading: Reading: Analyzing product sentiment assignment

Graded: Classification
Graded: 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?<p>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).</p>You will actually build an intelligent document retrieval system for Wikipedia entries in an iPython notebook.


17 videos, 3 readings expand

1. Reading: Slides presented in this module
2. Video: Document retrieval: A case study in clustering and measuring similarity
3. Video: What is the document retrieval task?
4. Video: Word count representation for measuring similarity
5. Video: Prioritizing important words with tf-idf
6. Video: Calculating tf-idf vectors
7. Video: Retrieving similar documents using nearest neighbor search
8. Video: Clustering documents task overview
9. Video: Clustering documents: An unsupervised learning task
10. Video: k-means: A clustering algorithm
11. Video: Other examples of clustering
12. Video: Clustering and similarity ML block diagram
13. Reading: Download the IPython Notebook used in this lesson to follow along
14. Video: Loading & exploring Wikipedia data
15. Video: Exploring word counts
16. Video: Computing & exploring TF-IDFs
17. Video: Computing distances between Wikipedia articles
18. Video: Building & exploring a nearest neighbors model for Wikipedia articles
19. Video: Examples of document retrieval in action
20. Reading: Reading: Retrieving Wikipedia articles assignment

Graded: Clustering and Similarity
Graded: 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. <p>You will learn how to build such a recommender system using a variety of techniques, and explore their tradeoffs.</p> 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.


19 videos, 3 readings expand

1. Reading: Slides presented in this module
2. Video: Recommender systems overview
3. Video: Where we see recommender systems in action
4. Video: Building a recommender system via classification
5. Video: Collaborative filtering: People who bought this also bought...
6. Video: Effect of popular items
7. Video: Normalizing co-occurrence matrices and leveraging purchase histories
8. Video: The matrix completion task
9. Video: Recommendations from known user/item features
10. Video: Predictions in matrix form
11. Video: Discovering hidden structure by matrix factorization
12. Video: Bringing it all together: Featurized matrix factorization
13. Video: A performance metric for recommender systems
14. Video: Optimal recommenders
15. Video: Precision-recall curves
16. Video: Recommender systems ML block diagram
17. Reading: Download the IPython Notebook used in this lesson to follow along
18. Video: Loading and exploring song data
19. Video: Creating & evaluating a popularity-based song recommender
20. Video: Creating & evaluating a personalized song recommender
21. Video: Using precision-recall to compare recommender models
22. Reading: Reading: Recommending songs assignment

Graded: Recommender Systems
Graded: 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.<p>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.</p>Using iPhython notebooks, you will build an image classifier and an intelligent image retrieval system with deep learning.


18 videos, 4 readings expand

1. Reading: Slides presented in this module
2. Video: Searching for images: A case study in deep learning
3. Video: What is a visual product recommender?
4. Video: Learning very non-linear features with neural networks
5. Video: Application of deep learning to computer vision
6. Video: Deep learning performance
7. Video: Demo of deep learning model on ImageNet data
8. Video: Other examples of deep learning in computer vision
9. Video: Challenges of deep learning
10. Video: Deep Features
11. Video: Deep learning ML block diagram
12. Reading: Download the IPython Notebook used in this lesson to follow along
13. Video: Loading image data
14. Video: Training & evaluating a classifier using raw image pixels
15. Video: Training & evaluating a classifier using deep features
16. Reading: Download the IPython Notebook used in this lesson to follow along
17. Video: Loading image data
18. Video: Creating a nearest neighbors model for image retrieval
19. Video: Querying the nearest neighbors model to retrieve images
20. Video: Querying for the most similar images for car image
21. Video: Displaying other example image retrievals with a Python lambda
22. Reading: Reading: Deep features for image retrieval assignment

Graded: Deep Learning
Graded: 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.<p>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.


7 videos, 1 reading expand

1. Reading: Slides presented in this module
2. Video: You've made it!
3. Video: Deploying an ML service
4. Video: What happens after deployment?
5. Video: Open challenges in ML
6. Video: Where is ML going?
7. Video: What's ahead in the specialization
8. Video: Thank you!