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Machine Learning Foundations: A Case Study Approach, University of Washington

4.6
7,904 ratings
1,939 reviews

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....

Top reviews

By BL

Oct 17, 2016

Very good overview of ML. The GraphLab api wasn't that bad, and also it was very wise of the instructors to allow the use of other ML packages. Overall i enjoyed it very much and also leaned very much

By DS

Sep 28, 2015

Excellent course, with really good lectures, material and assignment. Plus the professors are really amazing and their enthusiasm is really refreshing and makes the class more interesting. Loved it!

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1,864 Reviews

By Abhishek Banshiwala

Dec 16, 2018

Good Machine Learning course for beginners.

By 宁莽

Dec 15, 2018

以实际案例结合的讲解,非常有意义,对于新手来说,更能亲自体验到机器学习的强大

By Md. Rezaul Karim

Dec 14, 2018

Awesome course to get started to ML with Python.

By Krishna Prabhakar Saraswatula

Dec 14, 2018

course is really good with real life examples. Able to correlate well with the concepts

By Jungshen Kao

Dec 12, 2018

Very comprehensive and hand-on fashioned course, recommended!

By Md Rizwan Ansari

Dec 11, 2018

Great Experience

By SaketKr

Dec 09, 2018

It was really good.

Pros:

Has really nice assignments.

Teaching is really good.

Cons:

Should've used and open source package. Graphlab is good, I accept, but I wasted like 4-5 hours trying to install it, because some or other errors or dependencies,. I mean some consideration should've been done about an easy smooth method for it, for a beginner like me, it was really frustrating.

By Christopher Manhave

Dec 07, 2018

This was a great course. The instructors were fun and knowledgeable and the assignments were well-written. I loved the flexibility of being allowed to use whatever software I wanted to solve the ML assignments since the quizzes were based on the results of the modeling rather than submitted code. For some assignments I used sklearn and for others I used the software recommended by the instructors (graphlab).

By Muhammad Amin Nadim

Dec 06, 2018

Excellent

By Zohaib Mushtaq

Dec 05, 2018

very good and excellent course.