Designing a Machine Learning Model

This course covers the important differences between various canonical problems in machine learning, as well as the considerations in choosing the right solution techniques, based on the specifics of the problem you are trying to solve and the data that you have available.
Course info
Level
Intermediate
Updated
Aug 13, 2019
Duration
3h 25m
Table of contents
Course Overview
Exploring Approaches to Machine Learning
Choosing the Right Machine Learning Problem
Choosing the Right Machine Learning Solution
Building Simple Machine Learning Solutions
Designing Machine Learning Workflows
Building Ensemble Solutions and Neural Network Solutions
Description
Course info
Level
Intermediate
Updated
Aug 13, 2019
Duration
3h 25m
Description

As Machine Learning explodes in popularity, it is becoming ever more important to know precisely how to frame a machine learning model in a manner appropriate to the problem we are trying to solve, and the data that we have available. In this course, Designing a Machine Learning Model you will gain the ability to appropriately frame your use-case and then choose the right solution technique to model it. First, you will learn how rule-based systems and ML systems differ and how traditional and deep learning models work. Next, you will discover how supervised, unsupervised, and reinforcement learning techniques differ from each other. You will learn how classic supervised learning techniques such as regression and classification complement classic unsupervised techniques such as clustering and dimensionality reduction. You will then understand the assumptions and outcomes of these four classes of techniques and how solutions can be evaluated. Finally, you will round out your knowledge by designing end-to-end ML workflows for canonical ML problems, ensemble learning, and neural networks. When you’re finished with this course, you will have the skills and knowledge to identify the correct machine learning problem setup, and the appropriate solution technique for your use-case.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
(Music) Hi. My name is Janani Ravi, and welcome to this course on Designing a Machine Learning Model. A little about myself: I have a master's degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. As machine learning explodes in popularity, it is becoming ever more important to know precisely how to frame a machine learning model in a manner appropriate to the problem we are trying to solve and the data that we have available. In this course, you will gain the ability to appropriately frame your use case and then choose the right solution technique to model it. First, you will learn how rule-based systems and ML systems differ, and how traditional and deep learning models work. Next, you will discover how supervised, unsupervised, and reinforcement learning techniques differ from each other. You will learn how classic supervised learning techniques such as regression and classification compliment classic unsupervised techniques such as clustering and dimensionality reduction. You will then understand the assumptions and outcomes of these four classes of techniques and how solutions can be evaluated. Finally, you will round out your knowledge by designing end-to-end ML workflows, for canonical ML problems, ensemble learning, as well as neural networks. When you're finished with this course, you will have the skills and knowledge to identify the correct machine learning problem setup and the appropriate solution technique for your use case.