The StatQuest Illustrated Guide to Machine Learning!!!: Master the concepts, one full-color picture at a time, from the basics all the way to neural networks. BAM! PDF
The StatQuest Illustrated Guide to Machine Learning!!!: Master the concepts, one full-color picture at a time, from the basics all the way to neural networks. BAM! PDF
- Length: 306 pages
- Edition: 1
- Language: English
- Publisher: Packt Publishing
- Publication Date: 2022-07-14
With over 300 pages of easy to follow, step-by-step illustrations, everyone can understand Machine Learning from the basics to advanced topics like neural networks
Key Features
- Fully illustrated in color and written in the style of a graphic novel. BAM!
- Every concept is taught with a very gentle learning curve. DOUBLE BAM!!
- Every page is labeled as Main Ideas or Details, and you can focus on one, or the other, or both. TRIPLE BAM!!!
Book Description
Machine Learning is awesome and powerful, but it can also appear incredibly complicated. That’s where The StatQuest Illustrated Guide to Machine Learning comes in. This book takes the machine learning algorithms, no matter how complicated, and breaks them down into small, bite-sized pieces that are easy to understand. Each concept is clearly illustrated to provide you, the reader, with an intuition about how the methods work that goes beyond the equations alone. The StatQuest Illustrated Guide does not dumb down the concepts. Instead, it builds you up so that you are smarter and have a deeper understanding of Machine Learning.
The StatQuest Illustrated Guide to Machine Learning starts with the basics, showing you what machine learning is and what are its goals, and builds on those, one picture at a time, until you have mastered the concepts behind self driving cars and facial recognition.
What you will learn
- Master the fundamentals to use, optimize and evaluate machine learning
- Develop an intuition for fundamental statistics concepts
- Apply Statistical distributions, R-squared, p-values to your ML models
- Gain deep insight into the building blocks like Gradient Descent
- Visualize machine learning methods, including Neural Networks
- Learn about the limitations of machine learning
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