December 1

Free Data Science eBooks – December 2016

Ebooks, Blog, Resources

0  comments

Every month we scour the internet seeking out free eBooks to help you on your educational journey, and we share with you the fruits of our labours.

I hope this will prove to be a valuable resource to you that you will visit regularly (and invite your friends too).

More...

If you haven't subscribed to our newsletter yet, why not subscribe using the form on the right - you'll be the very first to know when new resources are published.

This month, we have 3 Machine Learning eBooks. They're all FREE - but they might not always be, so you need to get your skates on and read them before they're published in paper version.

Enjoy!

Disclosure: The FREE ebooks were free to download at the time of posting 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

3 Free Machine Learning Ebooks for December
Azure Machine Learning

This ebook introduces Microsoft Azure Machine Learning, a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then easily deploy those models for consumption as cloud web services.

The ebook presents an overview of modern data science theory and principles, the associated workflow, and then covers some of the more common machine learning algorithms in use today.

It builds a variety of predictive analytics models using real world data, evaluates several different machine learning algorithms and modeling strategies, and then deploys the finished models as machine learning web services on Azure within a matter of minutes.

The ebook also expands on a working Azure Machine Learning predictive model example to explore the types of client and server applications you can create to consume Azure Machine Learning web services.

Related Books
Bayesian Reasoning and Machine Learning

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly.

People who know the methods have their choice of rewarding jobs.

This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus.

Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models.

Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter

Related Books

Deep Learning

by Ian Goodfellow, Yoshue Bengio and Aaron Courville

Deep Learning

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. This book introduces a broad range of topics in deep learning.

The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.

Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.

Related Books
Check out these 3 free machine learning ebooks for December #machinelearning #artificialintelligence #deeplearning

Tags

data science, free ebooks


You may also like

Awesome Gifts for Data Scientists

Computational Statistics is the New Holy Grail – Experts

3 Crucial Tips for Data Processing and Analysis

Free Must-Read Statistics Books for Aspiring Data Scientists

Correlation Is Not Causation – Pirates Prove It!

Fantastic Free Data Science Books for Aspiring Data Scientists

Free Data Science eBooks – April 2021

Free Data Science eBooks – March 2021

0 0 vote
Article Rating
Subscribe
Notify of
guest

This site uses Akismet to reduce spam. Learn how your comment data is processed.

0 Comments
Inline Feedbacks
View all comments
{"email":"Email address invalid","url":"Website address invalid","required":"Required field missing"}

Machine Learning Models:

The Big Picture

FREE Ultra HD pdf

Download your FREE mind map to learn about the different types of ML models in Machine Learning.

Chi-Squared Innovations
0
Would love your thoughts, please leave a comment!x
()
x