What Is It About?
With data collection on the rise, machine learning is a hot topic. The manner in which computers are able to mimic human thinking is rapidly exceeding human capabilities in everything from chess to picking the winner of a song contest.
Machine learning is a difficult subject to tackle and knowing where to start is often the hardest step. This book is not the most comprehensive beginner’s introduction to machine learning, but extra effort has been made to keep it approachable and easy-to-follow. This means plain-English explanations of complicated algorithms, detailed visual examples, and general advice on algorithm usage as well as their pros and cons.
Please note that the book does not include Python code snippets but does include statistical training tasks. Where basic statistics are used I do add clear explanations for readers rusty or new to statistics. If you hate statistics more than acne removal YouTube commercials that take 15 seconds until you can slam-click SKIP, then this book is not for you. If you’ve already aced other machine learning materials then this book is also probably not for you.
My intention of writing this book is to give readers a confidence booster so that they can go on to tackle more advanced topics, including the technical textbooks sold on Amazon or Udacity courses.
Topics covered in this book are:
- Artificial Intelligence
- Downloading Free Datasets
- Support Vector Machine Algorithms
- Deep Learning/Neural Networks
- Data Reduction, Principle Component Analysis
- k-Means and k-Nearest Neighbor Clustering
- Association Analysis
- Recommender Systems
- Decision Trees
- Machine Learning Careers
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