# Tag: Statistical Learning (20)

**Learning from 3 big Data Science career mistakes**- Feb 25, 2020.

Thinking of data science as merely a technical profession, like programming, may take you away from your goals. We explain big mistakes to avoid, including not understanding the 2 cultures of statistics, and not understanding the shift to industrial focus.**The 10 Statistical Techniques Data Scientists Need to Master**- Nov 15, 2017.

The author presents 10 statistical techniques which a data scientist needs to master. Build up your toolbox of data science tools by having a look at this great overview post.**Short course: Statistical Learning and Data Mining IV, NYC, Nov 2-3**- Oct 13, 2017.

This new two-day course gives a detailed and modern overview of statistical models used by data scientists for prediction and inference, with emphasis on tools useful for tackling modern-day data analysis problems.**Top KDnuggets tweets, Mar 15-21: Reverse-engineering a $500M AI company in one week; Climate Change Denial and CO2 Emissions**- Mar 22, 2017.

Also Hastie, Tibshirani and Friedman - The Elements of Statistical Learning Book PDF; Getting Close and Personal w. #MachineLearning #Algorithms; Open Source Toolkits for Speech Recognition.**Top KDnuggets tweets, Mar 08-14: In-depth introduction to Machine Learning in 15 hours of expert videos**- Mar 15, 2017.

Also: #ICYMI The #DataScience Project Playbook; Every Intro to #DataScience Course on the Internet, Ranked; Quick reference to #Python in a single script.**Short course: Statistical Learning and Data Mining IV, Palo Alto, Apr 6-7**- Feb 21, 2017.

This new two-day course gives a detailed and modern overview of statistical models used by data scientists for prediction and inference, with emphasis on tools useful for tackling modern-day data analysis problems.**Short course: Statistical Learning and Data Mining IV, Washington, DC, Oct 19-20**- Aug 8, 2016.

This new two-day course gives a detailed and modern overview of statistical models used by data scientists for prediction and inference, including sparse models and deep learning.**Short course: Statistical Learning and Data Science, Palo Alto, Apr 18-19**- Feb 23, 2016.

This *new* two-day course gives a detailed and modern overview of statistical models used by data scientists for prediction and inference, with emphasis on tools useful for tackling modern-day data analysis problems.**Free Online Course: Statistical Learning**- Jan 12, 2016.

With a free MOOC from Stanford, dive into statistical learning with the respected professors who literally wrote the book on it.**A Statistical View of Deep Learning**- Nov 13, 2015.

A statistical overview of deep learning, with a focus on testing wide-held beliefs, highlighting statistical connections, and the unseen implications of deep learning. The post links to 6 articles covering a number of related topics.**Statistical Learning and Data Mining: 10 Hot Ideas for Learning from Data, NYC, Oct 8-9**- Aug 27, 2015.

Taught by top Stanford professors and leading statisticians Trevor Hastie and Robert Tibshirani, this course presents 10 hot ideas for learning from data, and gives a detailed overview of statistical models for data mining, inference and prediction.**New Machine Learning and Data Science Books – Save 20%**- Jun 15, 2015.

New books include Statistical Learning with Sparsity: The Lasso and Generalizations, Statistical Reinforcement Learning: Modern Machine Learning Approaches, and Healthcare Data Analytics. Use Promotion Code GZP42 to save 20% off.**Interview: Kaiser Fung, NYU on Why Statistical Reasoning is more important than Number Crunching**- Mar 5, 2015.

We discuss why every individual should care about statistics, inspiration behind the book Numbersense, teaching statistics as liberal arts, Junk Charts blog, advice and more.**All Machine Learning Models Have Flaws**- Mar 3, 2015.

This classic post examines what is right and wrong with different models of machine learning, including Bayesian learning, Graphical Models, Convex Loss Optimization, Statistical Learning, and more.**Statistical Learning and Data Mining III: 10 Hot Ideas for Learning from Data, Mar 19-20, Palo Alto**- Feb 23, 2015.

Taught by top Stanford professors and leading statisticians Trevor Hastie and Robert Tibshirani, this course presents 10 hot ideas for learning from data, and gives a detailed overview of statistical models for data mining, inference and prediction.**Top /r/MachineLearning posts, January**- Feb 13, 2015.

Talking Machines, SVM lectures, a new Stanford statistical learning online course, and a listing of open-source datasets top the most popular Reddit posts on /r/MachineLearning for the month of January.**Top KDnuggets tweets, Sep 24-25: Statistical & Machine learning with R, great 15 hour online course**- Sep 26, 2014.

Watch: Statistical and Machine learning with R, 15 hour online course; 50 Data Science and Statistics Blogs worth reading; Microsoft: Data Scientist; Book: Frequent Pattern Mining.**Statistical Learning and Data Mining III, Boston, Oct 27-28**- Aug 14, 2014.

Taught by top Stanford professors and leading statisticians Trevor Hastie and Robert Tibshirani, this course presents 10 hot ideas for learning from data, and gives a detailed overview of statistical models for data mining, inference and prediction.**Top KDnuggets tweets, Jan 31 – Feb 2: Free books on statistical learning; Intro: Machine Learning and Apache Mahout**- Feb 3, 2014.

3 Free books on statistical learning; Machine Learning and Apache Mahout : very good Introduction; 16 Top #BigData Analytics Platforms; Knowledge Graph, Google giant database of all the facts in the world**SLDM Statistical Learning and Data Mining III – 10 Hot Ideas, Palo Alto, Mar 20-21**- Jan 23, 2014.