Creating Machine Learning Models

This course covers the important types of machine learning algorithms, solution techniques based on the specifics of the problem you are trying to solve, as well as the classic machine learning workflow.
Course info
Level
Intermediate
Updated
Oct 29, 2019
Duration
2h 44m
Table of contents
Course Overview
Understanding Approaches to Machine Learning
Understanding and Implementing Regression Models
Understanding and Implementing Classification Models
Understanding and Implementing Clustering Model
Description
Course info
Level
Intermediate
Updated
Oct 29, 2019
Duration
2h 44m
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, Creating Machine Learning Models you will gain the ability to choose the right type of model for your problem, then build that model, and evaluate its performance. First, you will learn how rule-based and ML-based systems differ and their strengths and weaknesses and how supervised and unsupervised learning models differ from each other. Next, you will discover how to implement a range of techniques to solve the supervised learning problems of classification and regression. You will gain an intuitive understanding of the the model algorithms you can use for classification and regression. Finally, you will round out your knowledge by building clustering models using a couple of different algorithms, and validating the results. 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 and evaluation techniques 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
Hi, my name is Janani Ravi, and welcome to this course on Creating Machine Learning Models. 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 even 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 choose the right type of model for your problem, then build that model and evaluate its performance. First, you will learn how rule-based and ML-based systems differ and their strengths and weaknesses and how supervised and unsupervised learning models differ from each other. Next, you will discover how to implement a range of techniques to solve the supervised learning problems of classification and regression. You'll gain an intuitive understanding of the model algorithms you can use for both of these. Finally, you will round out your knowledge by building clustering models using a couple of different algorithms and evaluating the results. 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 and evaluation techniques for your use case.