Call me a geek if you like, but statistics and machine learning are bang in fashion right now.
Ya, like, totally dude.
If you're into forecasting and prediction, then this months FREE eBooks should hit the spot.
They're really proving to be very popular, especially the forecasting one, which has over a million readers. My books have been popular, but I must admit to being rather jealous with the size of readership they're getting. But anyway, size doesn't matter does it boys? It's what you do with it that counts...
This month we highlight 3 books:
- Machine Learning Yearning
- Forecasting: Principles and Practice
- Machine Learning: An Algorithmic Perspective
They're all FREE, so help yourselves...
Disclosure: The FREE ebooks are free to download but other links in this post may contain affiliate links. As Amazon Associates we may earn from qualifying purchases.
You can find further details in our TCs
by Andrew Ng
AI, Machine Learning and Deep Learning are transforming numerous industries, and Andrew Ng has been writing a book, Machine Learning Yearning, to teach you how to structure Machine Learning projects.
This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. Some technical AI classes will give you a hammer; this book teaches you how to use the hammer. If you aspire to be a technical leader in AI and want to learn how to set direction for your team, this book will help.
by Rob J Hyndman and George Athanasopoulos
Forecasting is required in many situations. Deciding whether to build another power generation plant in the next five years requires forecasts of future demand. Scheduling staff in a call centre next week requires forecasts of call volumes. Stocking an inventory requires forecasts of stock requirements. Telecommunication routing requires traffic forecasts a few minutes ahead.
Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.
by Stephen Marsland
A Proven, Hands-On Approach for Students without a Strong Statistical Foundation.
Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area.
Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.