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University of Washington

Machine Learning Foundations: A Case Study Approach

University of Washington via Coursera


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.


  • 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.
  • 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 a Jupyter notebook.
  • 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 a Jupyter notebook, and take a first stab at a core piece of the intelligent application you will build and deploy in your capstone.
  • 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 Jupyter notebook.
  • 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 a Jupyter notebook, you will use these techniques to build a real song recommender system.
  • 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.
  • 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.

Taught by

Carlos Guestrin and Emily Fox


4.0 rating, based on 40 Class Central reviews

4.6 rating at Coursera based on 13374 ratings

Start your review of Machine Learning Foundations: A Case Study Approach

  • I had already completed Andrew Ng's Machine Learning course (Coursera/Stanford), and a couple of courses in the Data Science specialization (Coursera/Johns Hopkins). Although I loved Andrew Ng's course, I was looking for something more in-depth and…
  • Machine Learning Foundations: A Case Study Approach is a 6-week introductory machine learning course offered by the University of Washington on Coursera. It is the first course in a 5-part Machine Learning specialization. The course provides a broad…
  • Ericdo1810
    This course is easily the best introductory course to Machine Learning one can get. Well-designed, beginner-friendly but also rich in content at the same time. There's a pretty nice balance between theory and practice. Basic, foundational machine le…
  • Gary Hess
    EDIT: After finishing 4 courses in the specialization, my opinion has gone downhill. Here is my review of the whole specialization: :END EDIT I completed this course…
  • Profile image for Igor Filippov
    Igor Filippov
    This is an introductory course, so don't expect any in-depth explanation. What it teaches you is "you can take this data and have this prediction", but course doesn't explain math behind the solutions.
    The most frustrating for me is that staff isn't responsive on the forums. Questions can stay answered for weeks. Moreover, "Course by University of Washington" isn't quite right, since they "decided not to put UW logo on certificate".
  • Anonymous
    This is supposed to be an introductory course to machine learning. It is not quite introductory in the sense of a gentle start from basics, but is geared towards providing some kind of introduction or overview of regression, classification, clusteri…
  • Anonymous
    A lot of people is attacking the Course as a high-level, not deep. I must say that at the very beginning I thought the same way. However in the second course, they force you to develop your own routines in python. So there is no need to pay a license or anything in the real world.

    I didn´t know about UW declining to sign the certificates, I wish we all could know the reason.

    Keep studying, this course will take you far!
  • Profile image for Anton Poznyakovskiy
    Anton Poznyakovskiy
    This is an introductory course in the specialization, and a such, aims more for the breadth than for the depth. It gives a good overview of the areas of machine learning and motivates and explains them with the case studies mentioned in the title. I can't say that the case study approach is different from other data science courses that I have participated in, but the lecturers present the concepts of machine learning in a clearly explained and memorable way. The only thing that I disliked about the course (and the reason why I rate it only 4 out of 5) is that the programming assignments amount to modifying already existing code. This makes them much too easy in my opinion, and also reduces their learning outcome.
  • Profile image for Tim Haines
    Tim Haines
    This course is pretty basic, offering an overview, and some workshops to work through. I gave this course a low 2 star rating because the workshops use software called Graphlab. Graphlab is actually pretty cool, and is made by one of the course pr…
  • This course consist of 6 week, each week has two part.
    Part 1: They discus about problem and algorithms we can use to solve the problem.
    Part 2: After explaining ways that is possible to solve the problem, They try to implement the algorithm using GraphLab software.
    sometimes in course you just feel that it is a GraphLab workshop ( Carlos, one of the instructors, is founder of Dato-GraphLab company ) but i don't think that it is a problem!
    Altogether i think Carlos and Emily put too much effort for this course, and if you excited about Machine Learning, definitely you will enjoy this course.
  • It is a great course for beginneers in Machine Learning, to know what you could do.

    Many people may say that you won´t do your own algorithms, and that you will always require a commercial license, but that is not true. Even when in this course it is absolutely true, the following courses are not like that.

    It is awesome and the professors are great!
  • For what it is, this class does a good job overviewing different analytical techniques in machine learning. It's light on the details (that's what the follow-up courses are for), but gives you a flavor on how this stuff works.
  • Pros: Well explained lessons and the case study approach is good to help you understand in what situations you might apply what you are learning. It's not too expensive and it focuses on understanding the concepts and not in the programming language.

    Cons: It does not use native python, but a proprietary library, so the code you develop during the course cannot be used latter as you would need to buy a licence. The course is an introduction to the rest of the specialization so if you are not planning to take the whole specialization, this course will serve as a very general introduction to the topic.
  • Anonymous
    Spent a lot of time learning how to use Graphlab. They start saying the benefits of using Python as it is open source and then we have to spend significant amount of time learning Graphlab, for which you would have to pay money for non-academic purposes. Seriously considering dropping the course because of this.
  • Research such as Library of Congress areas Psychology

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  • Suresh
    Gives good basic foundation on Machine Learning for people who have absolutely no idea on what's ML is. Anyways got to complete the whole specialisation to try your own models.
  • the aim of this course is to learn artificial learning and the modeling of classification systems but also to study and ultimately be able to design prediction algorithms
  • Mikael

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