The Ultimate Guide to Grokking Machine Learning: Mini Grey

Welcome to the ultimate guide to Grokking Machine Learning: Mini Grey. In this article, we will dive deep into the world of machine learning and explore the fascinating possibilities that the Mini Grey edition brings.

Understanding Machine Learning

Machine learning, a subset of artificial intelligence, is revolutionizing various industries by enabling computers to learn and make predictions or decisions without explicit programming. It uses algorithms and statistical models to analyze and interpret complex data, mimicking human intelligence. Machine learning algorithms can be classified into three main types: supervised learning, unsupervised learning, and reinforcement learning.

Grokking Machine Learning: Mini Grey is a comprehensive book that explores the fundamentals of machine learning in a practical and accessible manner. This edition presents step-by-step tutorials and real-world examples to help readers grasp the concepts and applications effectively.

Grokking Machine Learning

by Mini Grey(Kindle Edition)

4.5 out of 5

Language	:	English
File size	:	18084 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	906 pages
Screen Reader	:	Supported

FREE

What Sets Mini Grey Apart

Mini Grey edition of Grokking Machine Learning offers a unique experience for learners. It caters to both beginners and professionals in the field, providing a solid foundation for those starting their journey into machine learning while also delivering advanced techniques and cutting-edge concepts for experienced practitioners.

The book not only focuses on theoretical knowledge but also emphasizes hands-on exercises, enabling readers to build their machine learning models and apply them to real-world scenarios. With Mini Grey, you will gain practical skills that can be immediately implemented in your work or projects.

Furthermore, Mini Grey's approach is highly interactive and visually engaging. The book includes illustrations, diagrams, and code examples that simplify complex concepts, making it easier for readers to understand and absorb the material.

The Contents: Unveiling the Treasures

Let's take a closer look at what this Mini Grey edition has to offer:

Chapter 1: to Machine Learning

This chapter provides an overview of machine learning, its history, and its application in various fields. It sets the stage for the subsequent chapters, ensuring that readers grasp the fundamental concepts before delving into the practical aspects.

Chapter 2: Understanding Supervised Learning

Supervised learning is one of the most commonly used types of machine learning. In this chapter, you will explore different algorithms, such as linear regression, logistic regression, and decision trees, and learn how to train models using labeled data.

Chapter 3: Unsupervised Learning Techniques

Unsupervised learning algorithms are used when the data is unlabelled or when finding patterns and structures is the primary goal. This chapter covers techniques like clustering, dimensionality reduction, and anomaly detection.

Chapter 4: Reinforcement Learning and Beyond

Reinforcement learning focuses on training models to make sequential decisions through interaction with the environment. This chapter explains the concepts of reinforcement learning and explores advanced topics like deep reinforcement learning and policy gradients.

Chapter 5: Neural Networks and Deep Learning

Neural networks form the building blocks of deep learning. In this chapter, you will learn about the fundamentals of neural networks, including basic architectures, activation functions, and optimization techniques.

Chapter 6: Case Studies and Real-World Applications

This chapter provides real-world case studies that apply the concepts learned throughout the book. Additionally, it explores various practical applications of machine learning in diverse industries like healthcare, finance, and marketing.

The Journey Begins!

Grokking Machine Learning: Mini Grey acts as a compass to navigate the complexities of machine learning. It equips you with the knowledge and skills required to understand and build your machine learning models. Whether you are a beginner or an experienced professional, Mini Grey will accompany you on your journey to master machine learning.

So, are you ready to embark on this exciting adventure? Get your copy of Grokking Machine Learning: Mini Grey today!

Grokking Machine Learning

by Mini Grey(Kindle Edition)

4.5 out of 5

Language	:	English
File size	:	18084 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	906 pages
Screen Reader	:	Supported

FREE

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 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