Machine Learning Foundations: A Case Study Approach
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Coursera online courses
Coursera's online classes are designed to help students achieve mastery over
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knowledge through video lectures. They will also provide challenging
assessments, interactive exercises during each lesson, and the opportunity to
use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over
course material. Some of the best professors in the world - like neurobiology
professor and author Peggy Mason from the University of Chicago, and computer
science professor and Folding@Home director Vijay Pande - will supplement your
knowledge through video lectures. They will also provide challenging
assessments, interactive exercises during each lesson, and the opportunity to
use a mobile app to keep up with your coursework. Coursera also partners with
the US State Department to create “learning hubs” around the world. Students
can get internet access, take courses, and participate in weekly in-person
study groups to make learning even more collaborative. Begin your journey into
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to navigate the data infrastructures that multinational corporations use when
you discover the world of data analysis. Follow one of Coursera’s “Skill
Tracks”. Or try any one of its more than 560 available courses to help you
achieve your academic and professional goals.
Provider Subject Specialization
Humanities
Sciences & Technology
Course Description
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 le...
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.
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- 9 reviews
- 8 completed
3 years, 11 months ago
First of all, this is entry level very easy introductory course. It should give you general topic awareness, overview of things you can achieve with machine learning, etc. Don't expect math or even algorithms explanations here. Nonetheless even though I have some deeper knowledge of the topic the course was very enjoyable to me and I look forward to more detailed courses in Machine Learning specialization from Coursera. First of all, instructors were amazing very enthusiastic guys and went to great length to explain concepts and get across ideas they wanted to convey (I'm not new to the subject though so I might be wrong here, but my impression was they were very cool). Assignments were interesting and in python notebook format which I like. Also, I find doing assignments in python more fun then in Matlab.
Things I didn't like: forum was not very active, I didn't monitor it closely though. Android version of this course didn't hav...
First of all, this is entry level very easy introductory course. It should give you general topic awareness, overview of things you can achieve with machine learning, etc. Don't expect math or even algorithms explanations here. Nonetheless even though I have some deeper knowledge of the topic the course was very enjoyable to me and I look forward to more detailed courses in Machine Learning specialization from Coursera. First of all, instructors were amazing very enthusiastic guys and went to great length to explain concepts and get across ideas they wanted to convey (I'm not new to the subject though so I might be wrong here, but my impression was they were very cool). Assignments were interesting and in python notebook format which I like. Also, I find doing assignments in python more fun then in Matlab.
Things I didn't like: forum was not very active, I didn't monitor it closely though. Android version of this course didn't have a button for videos download.
- 3 reviews
- 3 completed
3 years, 6 months ago
Content: Great introduction with some amount of projects to get better understanding of the concept. Not as challenging as other courses in this specialization. It is more of the summary and invitation to go through the rest of specialization. Reccomended.
Instructors: Emily Fox and Carlos Guestrin are very good at outlining subjects and then getting deeper into details. If they would prepare other courses, I would definitely enroll.
Provider. What else to say... Coursera = 5-star.
- 116 reviews
- 107 completed
3 years, 10 months ago
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 overview of key areas in machine learning, including regression, classification, clustering , recommender systems and deep learning, using short programming case studies as examples. The course assumes basic Python programming skills and it uses a software package called GraphLab that requires a 64-bit operating system running Python 2.7. Grades are based on periodic comprehension quizzes and short programming assignments.
The course covers a broad range of machine learning topics at a high level with the promise of drilling down into the details in future courses in the specialization. The lecturers have good chemistry, but they tend to get distracted when they are on screen together....
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 overview of key areas in machine learning, including regression, classification, clustering , recommender systems and deep learning, using short programming case studies as examples. The course assumes basic Python programming skills and it uses a software package called GraphLab that requires a 64-bit operating system running Python 2.7. Grades are based on periodic comprehension quizzes and short programming assignments.
The course covers a broad range of machine learning topics at a high level with the promise of drilling down into the details in future courses in the specialization. The lecturers have good chemistry, but they tend to get distracted when they are on screen together. The video and slide quality are very good and although the delivery is a little rough around the edges at times, the lectures are informative. The machine learning methods covered aren’t necessarily treated as complete black boxes, but the course intentionally avoids getting too deep into the details, putting the emphasis on conceptual understanding.
The weekly labs are contained in short IPython Notebooks—interactive text and code documents rendered in a web browser—that illustrate some simple models in GraphLab. The labs themselves are easy and don’t require much coding other than calling various built in GraphLab functions. The hardest part about the class is getting your programming environment set up in the first place. If you don’t have a new version of 64-bit Python 2.7, you can’t run GraphLab. It is relatively easy to get set up if you can use the recommended Anaconda Python distribution, but getting things set up manually on an existing Python installation may prove troublesome. The instructors provided some workarounds for doing the course without GraphLab or using GraphLab on Amazon’s cloud computing service; I wouldn’t take the course without getting GraphLab working in some form. Many students decried the use of a non-open source package for an open class; I think it is useful to be exposed to new tools and GraphLab seems cleaner than Python’s popular scikit-learn package. In this sort of course, the focus should be one concepts rather than syntax.
Machine Learning Foundations: A Case Study Approach achieves its goal of introducing machine learning at a high level without rushing or trying to cram too much into any particular week. What the professors lack in terms of polish they make up for with enthusiasm. Compatibility and setup issues will be a roadblock for some, but overcoming them is worth it.
I give Machine Learning Foundations: A Case Study Approach 4.5 out of 5 stars: Great.
- 2 reviews
- 1 completed
3 years, 11 months ago
I was very pleased with this course (and optimistic about the specialization.) The instructors are very thorough in breaking down the concepts. This course is not very math heavy, rather a high level introduction to machine learning. That being said, there is enough quality content & opportunities to work with the concepts presented to make this course worth the time (& money if upgrading) invested.
I was skeptical of using graphlab initially (primarily because I would prefer using pandas & scikit learn,) however, I ended up agreeing with the instructors decision to use this tool (their intent is to teach ML concepts not the tools used.) The instructors do, however, make it fairly easy to use pandas & scikit learn if that's the direction a student wants to go.
I strongly recommend this course to anyone who is interested in learning more about machine learning.