Top Stories of 2017: 10 Free Must-Read Books for Machine Learning and Data Science; Python overtakes R, becomes the leader in Data Science, Machine Learning platforms

2017 was a very successful year for KDnuggets.

We continued to cover interesting and important stories in the intersecting fields of AI, Analytics, Big Data, Data Science, Deep Learning, and Machine Learning.

We published over 1200 stories, had over 4.8 million visitors who saw over 14 million pages. Our subscribers have grown to over 200,000 subscribers/followers via email and social media.

Here are the top KDnuggets stories from 2017 - read them, if you have not already.

2017 Platinum Blog - over 80,000 unique page views in 2017

1. 10 Free Must-Read Books for Machine Learning and Data Science, by Matthew Mayo
2. Top 10 Machine Learning Algorithms for Beginners, by Reena Shaw
3. 30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets, by Matthew Mayo

2017 Gold Blog - over 60,000 unique page views in 2017

4. The Most Popular Language For Machine Learning and Data Science Is ..., by Jean-Francois Puget
5. Python overtakes R, becomes the leader in Data Science, Machine Learning platforms, by Gregory Piatetsky

2017 Silver Blog - over 40,000 unique page views in 2017

6. Top 15 Python Libraries for Data Science in 2017, by Igor Bobriakov
7. 5 Career Paths in Big Data and Data Science, Explained, by Matthew Mayo
8. Gartner 2017 Magic Quadrant for Data Science Platforms: gainers and losers, by Gregory Piatetsky
9. Top 20 Recent Research Papers on Machine Learning and Deep Learning, by Thuy T. Pham
10. Keep it simple! How to understand Gradient Descent algorithm, by Jahnavi Mahanta
11. 17 More Must-Know Data Science Interview Questions and Answers, by Gregory Piatetsky
12. 6 Reasons Why Python Is Suddenly Super Popular, by Kayla Matthews
13. 7 More Steps to Mastering Machine Learning With Python, by Matthew Mayo

Here are the most shared stories in 2017.

2017 Platinum Blog - over 6,000 shares in 2017

1. 10 Free Must-Read Books for Machine Learning and Data Science, by Matthew Mayo
2. Python overtakes R, becomes the leader in Data Science, Machine Learning platforms, by Gregory Piatetsky

2017 Gold Blog - over 3,000 shares in 2017

3. The Most Popular Language For Machine Learning and Data Science Is ..., by Jean-Francois Puget
4. The 4 Types of Data Analytics, by Thomas Maydon
5. 30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets, by Matthew Mayo
6. Top 15 Python Libraries for Data Science in 2017, by Igor Bobriakov
7. Big Data and the Internet of Things don't make business smarter, Analytics and Data Science do, by Michael O'Connell
8. Top 10 Machine Learning Algorithms for Beginners, by Reena Shaw
9. Deep Learning is not the AI future, by Fabio Ciucci

2017 Silver Blog - over 2,500 shares in 2017

10. Deep Learning Research Review: Natural Language Processing, by Adit Deshpande
11. 7 More Steps to Mastering Machine Learning With Python, by Matthew Mayo
12. 50 Companies Leading The AI Revolution, Detailed, by Thuy T. Pham
13. New Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?, by Gregory Piatetsky
14. Machine Learning Applied to Big Data, Explained, by Matthew Mayo
15. 6 Interesting Things You Can Do with Python on Facebook Data, by Nour Galaby
16. An Overview of Python Deep Learning Frameworks, by Madison May
17. Top 20 Recent Research Papers on Machine Learning and Deep Learning, by Thuy T. Pham

Finally, here are most viral stories, with the highest ratio of shares/UPV

1. Big Data and the Internet of Things don't make business smarter, Analytics and Data Science do, by Michael O'Connell
2. Machine Learning overtaking Big Data?, by Gregory Piatetsky
3. New Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?, by Gregory Piatetsky
4. Machine Learning & Artificial Intelligence: Main Developments in 2016 and Key Trends in 2017, by Matthew Mayo
5. The 4 Types of Data Analytics, by Thomas Maydon
6. Machine Learning Algorithms: Which One to Choose for Your Problem, by Daniil Korbut
7. Machine Learning Applied to Big Data, Explained, by Matthew Mayo