- Data Journalism Handbook 2 – Online beta access to the first 21 chapters
- Select Star SQL – A book that is also a walk-through interactive tutorial for learning SQL
- Dive Into Deep Learning – A very detailed and up-to-date book on Deep Learning; used at Berkeley. It also includes Jupyter notebooks.
- R for Data Science – Just like the title says, learn to use R for data science.
- Advanced R – A work in progress for the second edition of the book.
The textbook for the UC Berkeley Data Science course is available for free online at Computational and Inferential Thinking. It is an online textbook and appears to be created as a collection of Jupyter notebooks. Here are some of the topics covered:
- An Intro to Python for Data Science
- Basic Plotting
- and much more
The book is free and open-source.
Christopher Bishop, a Technical Fellow at Microsoft Research, has released his textbook Pattern Recognition and Machine Learning as a free PDF download.
The book is a bit older, published in 2006, but it stills contains some great information. Some of the topics covered include:
- Probability Distributions
- Neural Networks
- Ensemble Models
- K-Means Clustering
- and many more….
Reinforcement Learning: An Introduction by Rich Sutton and Andrew Barto was recently released on October 15, 2018. The authors were kind enough to put a late draft version of the book online as a PDF. If you are hoping to learn about Reinforcement Learning, this is a great place to start.
Full text is available on a Google Drive at Reinforcement Learning. Take a look.
Good Luck and Happy Learning.
Here are the latest articles from Microsoft regarding cloud data science products and updates. This week it includes Measuring Model Goodness, a free ebook, AI discovery days, and more AI goodness.
- Measuring Model Goodness – Part 2 – Measurability is an important aspect of the Team Data Science Process (TDSP) as it quantifies how good the machine learning model is for the business and helps gain acceptance from the key stakeholders. In part 1 of this series, we defined a template for …[Read More]
- (RDS) Tip of the Day: Free eBook – The Developer’s Guide to Microsoft Azure eBook – August update is now available – This book includes all the updates from Microsoft Build, along with new services and features announced since … You’ll also see brand new sections on IoT, DevOps and AI/ML that you can take advantage of today.[Read More]
- AI Discovery Days at a Microsoft Location Near You – Join us to learn how to start building intelligence into your solutions with the Microsoft AI platform, including pre-trained AI services like Cognitive Services and Bot Framework, as well as deep learning tools like Azure Machine Learning.[Read More]
- Current use cases for machine learning in retail and consumer goods – Retail and consumer goods companies are seeing the applicability of machine learning (ML … Customers can build artificial intelligence (AI) applications that intelligently process and act on data, often in near real time. This helps organizations …[Read More]
- Describe, diagnose, and predict with IoT Analytics – … in IoT Analytics Machine learning (ML) is playing an increasingly important role in IoT analytics. One could argue that the recent emergence of real-world applications of ML in manufacturing is thanks to the explosion of data, most of which we can …[Read More]
- See How AI is Inspiring the Next Generation of Developers – One other thing most all the winning teams had in common – they used AI as a core part of their solutions. Recent developments have accelerated the application of machine learning technologies … cloud where the Cognitive Services Custom Vision model …[Read More]
- Where is the market opportunity for developers of IoT solutions? – GUI IDEs and data modelers. IoT-specific APIs. Device connectors and protocol adapters. Application security and management services. Note that most IoT … or historical. Pre-built AI/ML models for predictive maintenance, inventory optimization, energy …[Read More]
- Microsoft Translator adds Telugu as a supported language – Microsoft Translator is excited to announce the launch of Telugu, our latest AI-powered text translation language … education, financial services, government services, and many more.[Read More]
Pablo Casas has published a book freely available online, Data Science Live Book. To quote from the book,
It is a book about data preparation, data analysis and machine learning.
The book is open source, and the code examples are written in R.
Today brings us a very welcome guest post by Zacharias Voulgaris, author of Julia for Data Science. This is an excellent new book about the Julia language. By reading it you will learn about:
- IDEs for using Julia
- Basics of the Julia language
- Accessing and exploring data
- Machine learning
- Advanced data science techniques with Julia (cross-validation, clustering, PCA, and more)
The book has a nice flow for someone starting out with Julia and the topics are well explained. Enjoy the post, and hopefully you get a chance to check out the book.
Introducing Julia for Data Science (Technics Publications), a Great Resource for Anyone Interested in Data Science.
Over the past couple of years, there have been several books on the Julia language, a relatively new and versatile tool for computationally-heavy applications. Julia has been adopted extensively by the scientific community as it provided a great alternative to MATLAB and R, while its high-level programming style made it easy for people who were not adept programmers. Also, lately it has attracted the attention of computer science professionals (including Python programmers) as well as data scientists. These people who were already very effective coders, decided to learn this language as well, since it provided undeniable benefits in terms of performance and rapid prototype development, esp. when it came to numeric applications. In addition, the fact that Julia was and is still being developed by a few top MIT graduates goes on to show that this is not a novelty doomed to fade away soon, but instead it is a serious effort that’s bound to linger for many years to come.
However, this post is not about Julia per se, since there are many other people who have made its many merits known to the world since the language was first released in 2012. Instead, we aim to talk about the lesser-known aspects of the language, namely its abundant applications in the fascinating field of data science. Although there are already some reliable resources out there pinpointing the fact that Julia is undoubtedly ready for data science, this book is the first and most complete resource on this topic. Without assuming any prior knowledge of the language, it guides you step-by-step to the mastery of the Julia essentials, helping you get comfortable enough to use it for a variety data science applications. It may not make you an expert in the language, but data scientists rarely care about the esoteric aspects of the programming tools they use, since this level of know-how is not required for getting stuff done. However, the reader is given enough information to be able to investigate those aspects on his own.
The Julia for Data Science book has been in development for about a year and is heavily focused on the applications part, with lots of code snippets, examples, and even questions and exercises, in every chapter. Also, it makes use of a couple of datasets that closely resemble the real-world ones that data scientists encounter in their everyday work. On top of that, it provides you with some theory on the data science process (there is a whole chapter of it dedicated to this, although other books usually devote a couple of pages to it). Although the book is not a complete guide to data science, it provides you with enough information to have a sense of perspective and understand how everything fits together. It is by no means a recipe book, though you can use it as reference one, once you have finished reading it.
The Julia for Data Science book is available at the publisher’s website, as well as on Amazon, in both paperback and eBook formats. We encourage you to give it a read and experience first-hand how Julia can enrich your data science toolbox!