The Entire #Python Language in a Single Image; Cartoon: Thanksgiving, #BigData, and Turkey #DataScience; 50% of Data Scientists have under 10 GB databases, not #BigData; Machine Learning Algorithms: A Concise Technical Overview
Given the ongoing explosion in interest for all things Data Science, Artificial Intelligence, Machine Learning, etc., we have updated our Amazon top books lists from last year. Here are the 10 most popular titles in the AI & Machine Learning category.
New KDnuggets Poll is asking: What are the Industries/Fields where you applied Analytics, Data Science, Data Mining in 2016? Please vote and we will publish the analysis and trends.
Top 20 Python Machine Learning Open Source Projects, updated; Continuous improvement for IoT through AI; Top 10 Facebook Groups for Big Data, Data Science, and Machine Learning; Linear Regression, Least Squares & Matrix Multiplication: A Concise Technical Overview
Top 20 #Python #MachineLearning #OpenSource Projects; Shortcomings of #DeepLearning; What is the Difference Between #DeepLearning and Regular #MachineLearning?; Questions To Ask When Moving #MachineLearning From Practice to Production; How to Choose the Right #Database System
How Bayesian Inference Works; Data Science and Big Data, Explained; Trump, Failure of Prediction, and Lessons for Data Scientist; Combining Different Methods to Create Advanced Time Series Prediction; Questions To Ask When Moving Machine Learning From Practice to Production
#Trump, limits of #prediction, and lessons for #DataScience of #polls; A #TensorFlow implementation of French-to-English machine translation using @DeepMindAI ByteNet; 18 top women in #DataScience to follow on Twitter; A complete daily plan for studying to become a #MachineLearning #Engineer
Trump, Failure of Prediction, and Lessons for Data Scientists; Top 10 Amazon Books in Data Mining; Data Science Basics: An Introduction to Ensemble Learners; Parallelism in Machine Learning: GPUs, CUDA, and Practical Applications; 5 Free Machine Learning EBooks
Given the ongoing explosion in interest for all things Data Mining, Data Science, Analytics, Big Data, etc., we have updated our Amazon top books lists from last year. Here are the 10 most popular titles in the Data Mining category.
21 Must-Know #DataScience Interview Questions with Answers; Big Data Science: Expectation vs. Reality; Big #DataScience: Expectation vs. Reality; The 10 Algorithms #MachineLearning Engineers Need to Know.
The majority (57%) of respondents only worked with Gigabyte range data. More junior Data Scientists enter the market, but Petabyte Big Data Scientists still stand apart.
Agilience developed a new way to find authorities in social media across many fields of interest. In previous post we reviewed the top authorities in Data Mining and Data science; in this post we review top authorities in Artificial Intelligence and Machine Learning which includes Vineet Vashishta, Kirk D. Borne, KDnuggets, James Kobielus, Kaggle and more.
We recognize KDnuggets Bloggers who had the most popular blogs by views or shares in October 2016. They wrote about ebooks to read for Machine Learning, Data Science Venn Diagrams, 10 Data Science Videos on Youtube, and more.
Machine Learning: A Complete and Detailed Overview; Learn Data Science for Excellence; 5 EBooks to Read Before Getting into A Machine Learning Career; Eight Things an R user Will Find Frustrating When Trying to Learn Python
PhD/Postdoc at KU Leuven, Postdoc at Northeastern, Data Science Fellowship program at NYU, Asst. Prof. in ML at Cal State Long Beach, Data Science Faculty at UMBC, Faculty Business Analytics at USF, and more.
Agilience developed a new way to find authorities in social media across many fields of interest. We review the top authorities in Data Mining and Data science, which include KDnuggets, Kirk. D. Borne, Kaggle, Vincent Granville, and more.
NSFW Image Recognition, Differentiable Neural Computers, Hinton's Neural Networks for Machine Learning Coursera course; Introducing the AI Open Network; Making a Self-driving RC Car
#BigData Science: Expectation vs. Reality; Stanford CS 229: #MachineLearning Course material; Google - Decoding the micro-moments of #baseball via #BigData; Is your Code Good Enough to call Yourself a #DataScientist?
In this report you will find a concise look at how CDOs view their nascent role in high-profile organizations, focusing on guidelines and best practices for organizations looking to add their own CDO.
Top 10 Amazon Books in Data Mining, 2016 Edition
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