# Grokking Machine Learning

## Luis Serrano

- 512 pagine
- English
- ePUB (disponibile sull'app)
- Disponibile su iOS e Android

# Grokking Machine Learning

## Luis Serrano

## Informazioni sul libro

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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. 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 datasetsAbout 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

## Domande frequenti

## Informazioni

# 1 What is machine learning? It is common sense, except done by a computer

- what is machine learning
- is machine learning hard (spoiler: no)
- what do we learn in this book
- what is artificial intelligence, and how does it differ from machine learning
- how do humans think, and how can we inject those ideas into a machine
- some basic machine learning examples in real life

## Do I need a heavy math and coding background to understand machine learning?

*are*an expert in either of them, or both, you will certainly find your skills will be rewarded. But if you are not, you can still learn machine learning and pick up the mathematics and coding as you go. In this book, we introduce all the mathematical concepts we need at the moment we need them. When it comes to coding, how much code you write in machine learning is up to you. Machine learning jobs range from those who code all day long, to those who don’t code at all. Many packages, APIs, and tools help us do machine learning with minimal coding. Every day, machine learning is more available to everyone in the world, and I’m glad you’ve jumped on the bandwagon!

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*i*. It looks ugly at first glance, but it represents a very simple sum, namely, 1 + 2 + 3 + 4. And what about Σ

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*P*(

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*B*) = 0.8 simply means “The probability that it rains today given that we live in the Amazon rain forest is 80%.”

## OK, so what exactly is machine learning?

*Artificial*

*intelligence*(AI) is a general term, which we define as follows:

*based on data*

- By using logic and reasoning
- By using our experience