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Top October Stories: Top 10 Machine Learning Algorithms for Beginners


Also: Understanding Machine Learning Algorithms; Want to Become a Data Scientist? Read This Interview First; 6 Books Every Data Scientist Should Keep Nearby.



For the month of October, we also recognize the most popular posts and blogger based on unique page views (UPV) and social shares.

Platinum BlogMost Viewed - Platinum Badge
(>24,000 views)

  1. Top 10 Machine Learning Algorithms for Beginners, by Reena Shaw (*)



 

Gold BlogMost Viewed - Gold Badges (>12,000 UPV)

  1. Understanding Machine Learning Algorithms, by Brett Wujek
  2. Want to Become a Data Scientist? Read This Interview First, by Kevin Gray


Silver BlogMost Viewed - Silver Badges (> 6,000 UPV)

  1. 7 Types of Artificial Neural Networks for Natural Language Processing, by Olga Davydova
  2. Deep Learning for Object Detection: A Comprehensive Review, by Joyce Xu (*)
  3. How I started with learning AI in the last 2 months, by Shival Gupta (*)
  4. Learning git is not enough: becoming a data scientist after a science PhD, by Mike Lee Williams (*)
  5. XGBoost, a Top Machine Learning Method on Kaggle, Explained, by Ilan Reinstein
  6. AlphaGo Zero: The Most Significant Research Advance in AI, by Gregory Piatetsky (*)
  7. 6 Books Every Data Scientist Should Keep Nearby, by Kayla Matthews
  8. Using Machine Learning to Predict and Explain Employee Attrition, by Matt Dancho (*)
  9. 7 Steps to Mastering Deep Learning with Keras, by Matthew Mayo







Platinum Blog, October 2017Most Shared - Platinum Badge
(>2,400 shares)

  1. Top 10 Machine Learning Algorithms for Beginners, by Reena Shaw



 

Gold BlogMost Shared - Gold Badges (>1,200 shares)

  1. Understanding Machine Learning Algorithms, by Brett Wujek
  2. 6 Books Every Data Scientist Should Keep Nearby, by Kayla Matthews
  3. Want to Become a Data Scientist? Read This Interview First, by Kevin Gray


Silver BlogMost Shared - Silver Gold Badges (>700 shares)

  1. Ranking Popular Deep Learning Libraries for Data Science, by Rachel Allen and Michael Li
  2. 7 Types of Artificial Neural Networks for Natural Language Processing, by Olga Davydova
  3. Using Machine Learning to Predict and Explain Employee Attrition, by Matt Dancho
  4. AlphaGo Zero: The Most Significant Research Advance in AI, by Gregory Piatetsky
  5. XGBoost: A Concise Technical Overview, by Reena Shaw (*)
  6. An Overview of 3 Popular Courses on Deep Learning, by Vishnu Subramanian.
  7. XGBoost, a Top Machine Learning Method on Kaggle, Explained, by Ilan Reinstein
  8. TensorFlow: Building Feed-Forward Neural Networks Step-by-Step, by Ahmed Gad
  9. Deep Learning for Object Detection: A Comprehensive Review, by Joyce Xu
  10. Random Forests(r), Explained, by Ilan Reinstein (*)
  11. How LinkedIn Makes Personalized Recommendations via Photon-ML Machine Learning tool, by Yiming Ma, Bee-Chung Chen, and Deepak Agarwal
  12. 7 Techniques to Visualize Geospatial Data, by Vasavi Ayalasomayajula (*)


(*) indicates that badge added or upgraded based on these monthly results.

Most Shareable (Viral) Blogs

Among the top blogs, here are the 5 blogs with the highest ratio of shares/unique views, which suggests that people who read it really liked it.
  1. Ranking Popular Deep Learning Libraries for Data Science, by Rachel Allen and Michael Li
  2. Edge Analytics - What, Why, When, Who, Where, How?, by Ramesh Dontha
  3. Best practices of orchestrating Python and R code in ML projects, by Marija Ilic
  4. Learn Generalized Linear Models (GLM) using R, by Chaitanya Sagar
  5. How LinkedIn Makes Personalized Recommendations via Photon-ML Machine Learning tool, by Yiming Ma, Bee-Chung Chen, and Deepak Agarwal