[Pluralsight] 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.
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
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.
Created by: Janani Ravi
Updated: Oct 29, 2019
Duration: 2h 44m
Size: 390.87 MB