Grokking Machine Learning

 Discover valuable machine learning techniques you can understand and apply using just high-school math.

In Grokking Machine Learning you will learn:

Supervised algorithms for classifying and splitting data
Methods for cleaning and simplifying data
Machine learning packages and tools
Neural networks and ensemble methods for complex datasets

Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python  code and high school-level math. No specialist knowledge is required to  tackle the hands-on exercises using Python and readily available  machine learning tools. Packed with easy-to-follow Python-based  exercises and mini-projects, this book sets you on the path to becoming a  machine learning expert.

About the technology
 Discover powerful machine learning techniques you can understand and  apply using only high school math! Put simply, machine learning is a set  of techniques for data analysis based on algorithms that deliver better  results as you give them more data. ML powers many cutting-edge  technologies, such as recommendation systems, facial recognition  software, smart speakers, and even self-driving cars. This unique book  introduces the core concepts of machine learning, using relatable  examples, engaging exercises, and crisp illustrations.

About the book
Grokking Machine Learning presents machine learning algorithms and techniques in a way that  anyone can understand. This book skips the confused academic jargon and  offers clear explanations that require only basic algebra. As you go,  you’ll build interesting projects with Python, including models for spam  detection and image recognition. You’ll also pick up practical skills  for cleaning and preparing data.

What's inside

Supervised algorithms for classifying and splitting data
Methods for cleaning and simplifying data
Machine learning packages and tools
Neural networks and ensemble methods for complex datasets

About the reader
For readers who know basic Python. No machine learning knowledge necessary.

About the author
Luis G. Serrano is a research scientist in quantum artificial intelligence. Previously,  he was a Machine Learning Engineer at Google and Lead Artificial  Intelligence Educator at Apple.

Table of Contents
1 What is machine learning? It is common sense, except done by a computer
2 Types of machine learning
3 Drawing a line close to our points: Linear regression
4 Optimizing the training process: Underfitting, overfitting, testing, and regularization
5 Using lines to split our points: The perceptron algorithm
6 A continuous approach to splitting points: Logistic classifiers
7 How do you measure classification models? Accuracy and its friends
8 Using probability to its maximum: The naive Bayes model
9 Splitting data by asking questions: Decision trees
10 Combining building blocks to gain more power: Neural networks
11 Finding boundaries with style: Support vector machines and the kernel method
12 Combining models to maximize results: Ensemble learning
13 Putting it all in practice: A real-life example of data engineering and machine learning