num_pages: 512
description: Discover valuable machine learning techniques you can understand and apply using just high-school math.In Grokking Machine Learning you will 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 datasetsGrokking 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 technologyDiscover 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 bookGrokking 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 youll build interesting projects with Python including models for spam detection and image recognition. Youll 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 readerFor readers who know basic Python. No machine learning knowledge necessary.About the authorLuis 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 Contents1 What is machine learning? It is common sense except done by a computer2 Types of machine learning3 Drawing a line close to our Linear regression4 Optimizing the training Underfitting overfitting testing and regularization5 Using lines to split our The perceptron algorithm6 A continuous approach to splitting Logistic classifiers7 How do you measure classification models? Accuracy and its friends8 Using probability to its The naive Bayes model9 Splitting data by asking Decision trees10 Combining building blocks to gain more Neural networks11 Finding boundaries with Support vector machines and the kernel method12 Combining models to maximize Ensemble learning13 Putting it all in A real-life example of data engineering and machine learning
language: English
title: Grokking Machine Learning
publisher: Manning