Most Popular Jan 9-15
- The Most Popular Language For Machine Learning and Data Science Is...
- The 10 Algorithms Machine Learning Engineers Need to Know
- 5 Machine Learning Projects You Can No Longer Overlook, January
- Big Data and the Internet of Things don't make business smarter, Analytics and Data Science do
- Exclusive: Interview with Jeremy Howard on Deep Learning, Kaggle, Data Science, and more
- Machine Learning & Artificial Intelligence: Main Developments in 2016 and Key Trends in 2017
- 50+ Data Science, Machine Learning Cheat Sheets, updated
Most Shared Jan 9-15
- The Most Popular Language For Machine Learning and Data Science Is... - Jan 11, 2017.
- Big Data and the Internet of Things don't make business smarter, Analytics and Data Science do - Jan 12, 2017.
- Exclusive: Interview with Jeremy Howard on Deep Learning, Kaggle, Data Science, and more - Jan 14, 2017.
- AI, Data Science, Machine Learning: Main Developments in 2016, Key Trends in 2017 - Jan 10, 2017.
- Social Media for Marketing and Healthcare: Focus on Adverse Side Effects - Jan 09, 2017.
- Text Mining Amazon Mobile Phone Reviews: Interesting Insights - Jan 10, 2017.
- The Surprising Ethics of Humans and Self-Driving Cars - Jan 09, 2017.
Previous weeks top stories:
- Jan 2-8: Machine Learning Projects You Can No Longer Overlook, January; Machine Learning and Cyber Security Resources
- Dec 26-Jan 1: Game Theory Reveals the Future of Deep Learning; A Funny Look at Big Data and Data Science
- Dec 19-25: Machine Learning & AI: Main Developments in 2016 and Key Trends in 2017; 4 Reasons Your Machine Learning Model is Wrong
- Dec 12-18: Data Science, Predictive Analytics 2016 Developments, 2017 Key Trends; 50+ Data Science, Machine Learning Cheat Sheets
- Dec 5-11: Why Deep Learning is Radically Different From Machine Learning; Data Science Trends To Look Out For In 2017
- Nov 28-Dec 4: Machine Learning vs Statistics; Hard Thing About Deep Learning; Developers’ Machine Learning Intro
- Nov 21-27: Top 20 Python Machine Learning Open Source Projects; Continuous Improvement for IoT
Top stories in 2016
- 21 Must-Know Data Science Interview Questions and Answers;
10 Algorithms Machine Learning Engineers Need to Know;
Software used for Analytics, Data Science, Machine Learning projects;
Top Algorithms and Methods Used by Data Scientists
2016 Top stories each month
- December: 50+ Data Science, Machine Learning Cheat Sheets; Machine Learning/AI: Main 2016 Developments, Key 2017 Trends
- November: Trump, Failure of Prediction, and Lessons for Data Scientists
- October: 5 EBooks to Read Before Getting into A Machine Learning Career; Top 10 Data Science Videos on YouTube
- September: Top Algorithms and Methods Used by Data Scientists
- August: The 10 Algorithms Machine Learning Engineers Need to Know; How to Become a Data Scientist
- July: Bayesian Machine Learning, Explained; Why Big Data is in Trouble: They Forgot About Applied Statistics
- June: The Difference Deep Learning and "Regular" Machine Learning? R, Python duel as top Data Science tools
- May: Poll: What software you used for Analytics, Data Mining, Data Science? How to Explain Machine Learning to a Software Engineer
- April: 10 Essential Books for Data Enthusiast; When Deep Learning is better than SVMs or Random Forests?
- March: R Learning Path: From beginner to expert in 7 steps; R or Python? Consider learning both
- February: 21 Must-Know Data Science Interview Q&A; Gartner 2016 MQ for Advanced Analytics: gainers and losers
- January: 20 Questions to Detect Fake Data Scientists, Machine Intelligence vs. Machine Learning vs. Deep Learning vs. AI
Top stories in 2015
- R vs Python for Data Science: The Winner is ...;
60+ Free Books on Big Data, Data Science, Data Mining
Top 20 Python Machine Learning Open Source Projects;
50+ Data Science and Machine Learning Cheat Sheets.
2015 Top stories each month
- December: Top 10 Machine Learning Projects on Github; 50 Useful Machine Learning, Prediction APIs
- November: TensorFlow Disappoints - Google Deep Learning falls shallow; 5 Best Machine Learning APIs for Data Science
- October: Top 5 arXiv Deep Learning Papers, Explained; R vs Python: head to head data analysis
- September: 60+ Free Books on Big Data, Data Science; The one language a Data Scientist must master
- August: How to become a Data Scientist for Free; Data is Ugly - Tales of Data Cleaning
- July: 50+ Data Science and Machine Learning Cheat Sheets; Deep Learning and the Triumph of Empiricism
- June: Top 20 Python Machine Learning Projects; Which Big Data, Data Mining Tools go together?
- May: Most popular Predictive Analytics, Data Mining, Data Science software; R vs Python
- April: Awesome Public Datasets on GitHub; Forrester Wave Big Data Predictive Analytics - Gainers and Losers
- March: 7 common Machine Learning mistakes; Deep Learning for Text Understanding from Scratch
- February: 10 things statistics taught about big data; Gartner Analytics MQ: gainers and losers
- January: (Deep Learning Deep Flaws) Deep Flaws; Research Leaders on key trends, papers
Top stories in 2014
- Does Deep Learning Have Deep Flaws?
Is Data Scientist the right career path for you?
Four main languages for Analytics, Data Mining, Data Science; Cartoon: Big Data and World Cup Football
2014 Top stories by month
- December: If programming languages were vehicles; Cartoon: Unexpected Data Science Recommendations
- November: 9 Must-Have Skills for a Data Scientist; IBM Watson Analytics replacing a data scientist?
- October: Ebola Analytics and Data Science Lessons; Will Deep Learning take over Machine Learning?
- September: Data Science is mainly a Human Science; Hiring Data Scientists: What to look for?
- August: Four main languages for Analytics, Data Mining, Data Science
- July: Cartoon: Facebook data science experiment and Cats; Data Mining/Data Science "Nobel Prize"
- June: Does Deep Learning Have Deep Flaws? Cartoon: Big Data and World Cup
- May: New Poll - Analytics, Data Mining Software; Data Science Cheat Sheets
- April: Apache Spark, the hot new trend in Big Data; Data Analytics Handbook, free download
- March: Machine Learning in 7 Pictures; How Many Data Scientists?
- February: 3 Ways to test the accuracy; Exclusive Interview with Yann LeCun; One Page R
- January: Tutorial: Data Science in Python; Learning from Data, Caltech free online course