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Machine Learning Foundations: A Case Study Approach

Coursera · University of Washington · 3 HN comments

HN Academy has aggregated all Hacker News stories and comments that mention Coursera's "Machine Learning Foundations: A Case Study Approach" from University of Washington.
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 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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This course is offered by University of Washington on the Coursera platform.
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Hacker News Stories and Comments

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For complete newbies (but with programming experience), I would recommend this UW Coursera course to get introduced to ML Basics:

Early this year Apple acquired Turi for $200 million. It was founded by Carlos Guestrin, one of the professors who is teaching the course.

We (Class Central) are also working on a six part Wirecutter style guide to learning Data Science online. Here is part 1:

Feedback would be appreciated (on the format as well as content)!

I'm a huge fan of the rest of this Coursera specialization (or was, until they started charging to submit assignments for it mid-specialization, but I digress...)

Carlos and Emily do a great job diving deeper than most other online courses into the math behind different algorithms without making the math too theoretical. I'm a grad student in engineering, so I wanted to understand not only how to run these algorithms but also how they work and these courses were great for learning in a mathematically rigorous but still approachable sort of way.

The only criticism I've heard of this series is that it uses Turi/Dato/Graphlab instead of SciKit-Learn. I did the courses that exist so far using GraphLab, but I'm starting to redo the assignments using SciKit now so that I learn that toolkit as well.

I think they start charging after the second course.

I am in the same boat as you. I am currently doing Udacity's Machine Learning Nanodegree. But I think I would have felt lost if I hadn't done the first two courses of that Coursera Specialization.

Just started, but it seems that Pandas and SciKit-Learn are very similar to Dato/Graphlab from a usage perspective.

I liked the UW Coursera class that gave a broad overview of these topics with some applications:

It's part of a Machine Learning Specialization on Coursera (5 courses + a capstone project) which goes deeper on some areas after the foundations course:

I am taking this specialization and I have learned a lot so far. The material seems like it's at exactly the right level of depth (balances giving a high level overview of the field, with enough depth in specific areas to understand how things work and be able to apply them). Disclaimer: I work at Dato, and the CEO of Dato is also one of the instructors of this course.

Certainly understanding the math is very important but it is harder to get expertise on the pre-requisite math because the horizon is much bigger. I would recommend taking a case study approach and side by side learning the math stuff needed. If you are looking for an example then take a look at this,
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