Grokking Machine Learning
- 15h 1m 10s
- Luis G. Serrano
- Manning Publications
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.
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.
In this Audiobook
Chapter 1 - What is machine learning? It is common sense, except done by a computer
Chapter 2 - Types of machine learning
Chapter 3 - Drawing a line close to our points: Linear regression
Chapter 4 - Optimizing the training process: Underfitting, overfitting, testing, and regularization
Chapter 5 - Using lines to split our points: The perceptron algorithm
Chapter 6 - A continuous approach to splitting points: Logistic classifiers
Chapter 7 - How do you measure classification models? Accuracy and its friends
Chapter 8 - Using probability to its maximum: The naive Bayes model
Chapter 9 - Splitting data by asking questions: Decision trees
Chapter 10 - Combining building blocks to gain more power: Neural networks
Chapter 11 - Finding boundaries with style: Support vector machines and the kernel method
Chapter 12 - Combining models to maximize results: Ensemble learning
Chapter 13 - Putting it all in practice: A real-life example of data engineering and machine learning