Overview

This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!Explore many algorithms and models:

Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.

Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.

Get ready to do more learning than your machine!

Syllabus

Module 1 - Supervised vs Unsupervised Learning

Machine Learning vs Statistical Modelling

Supervised vs Unsupervised Learning

Supervised Learning Classification

Unsupervised Learning

Module 2 - Supervised Learning I

K-Nearest Neighbors

Decision Trees

Random Forests

Reliability of Random Forests

Advantages & Disadvantages of Decision Trees

Module 3 - Supervised Learning II

Regression Algorithms

Model Evaluation

Model Evaluation: Overfitting & Underfitting

Understanding Different Evaluation Models

Module 4 - Unsupervised Learning

K-Means Clustering plus Advantages & Disadvantages

Hierarchical Clustering plus Advantages & Disadvantages

Measuring the Distances Between Clusters - Single Linkage Clustering

Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering

Density-Based Clustering

Module 5 - Dimensionality Reduction & Collaborative Filtering

Dimensionality Reduction: Feature Extraction & Selection

Collaborative Filtering & Its Challenges

Reviews

5.0 rating, based on 1 Class Central review

Start your review of Machine Learning with Python

Yaragani Narendhar

I would not recommend the Kindle edition because of the formatting of the book. All the equations, inline and standalone, are too small to read. The graphs are too small to read. You have to double click on each one to zoom in. Then you click to go…

I would not recommend the Kindle edition because of the formatting of the book. All the equations, inline and standalone, are too small to read. The graphs are too small to read. You have to double click on each one to zoom in. Then you click to go back to page. This horrible formatting makes reading the book tiring and frustrating. The book gets a 1 star for the page layout making it difficult.

I don't understand how the publisher let this go. I will check samples carefully in the future to be sure not to get stuck with one of these duds again. I would advise anyone to try the epub format straight from the publisher. I can't say that is better, but can't imagine it could be worse.

The content was okay, but only left me looking for another book on the subject.

The examples are excellent working code case studies.

The balance of theory vs practice in the book wasn't right for me. I want to use Machine Learning. I do not intend to advance the field. While the math is interesting, it doesn't help a user decide what to do nor how to apply a method to a problem. Too much of the advice in the field seems to reduce to "try it, vary the parameters, maybe it will do something for your problem" or "if that one doesn't work, here are others to try". I realize that this may be state of the art, that people don't really know _why_ the various machine models work or don't work. But to move machine learning from a science experiment to a methodology with predictable outcomes, we need more books focused on practical advice.
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