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 State of AI Report 2019
This year's "State of AI Report" has been released. Read it to find out about the latest in AI research, talent, industry, and politics form the past 12 months.
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7 Steps to Mastering Data Preparation for Machine Learning with Python — 2019 Edition
Interested in mastering data preparation with Python? Follow these 7 steps which cover the concepts, the individual tasks, as well as different approaches to tackling the entire process from within the Python ecosystem.
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The Machine Learning Puzzle, Explained
Lots of moving parts go into creating a machine learning model. Let's take a look at some of these core concepts and see how the machine learning puzzle comes together.
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The Infinity Stones of Data Science
Do you love data science 3000? Don't want to be embarrassed in front of the other analytics wizards? Aspire to be one of Earth's mightiest heroes, like Kevin Bacon? Help make data science a snap with these simple insights.
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7 Steps to Mastering Intermediate Machine Learning with Python — 2019 Edition
By Matthew Mayo, KDnuggets Managing Editor on June 3, 2019 in 7 Steps, Classification, Cross-validation, Dimensionality Reduction, Feature Engineering, Feature Selection, Image Classification, K-nearest neighbors, Machine Learning, Modeling, Naive Bayes, numpy, Pandas, PCA, Python, scikit-learn, Transfer LearningThis is the second part of this new learning path series for mastering machine learning with Python. Check out these 7 steps to help master intermediate machine learning with Python!
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When Too Likely Human Means Not Human: Detecting Automatically Generated Text
Passably-human automated text generation is a reality. How do we best go about detecting it? As it turns out, being too predictably human may actually be a reasonably good indicator of not being human at all.
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7 Steps to Mastering SQL for Data Science — 2019 Edition
Follow these updated 7 steps to go from SQL data science newbie to practitioner in a hurry. We consider only the necessary concepts and skills, and provide quality resources for each.
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Top Data Science and Machine Learning Methods Used in 2018, 2019
Once again, the most used methods are Regression, Clustering, Visualization, Decision Trees/Rules, and Random Forests. The greatest relative increases this year are overwhelmingly Deep Learning techniques, while SVD, SVMs and Association Rules show the greatest decline.
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Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application?
Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application? Take part in the latest KDnuggets survey and have your say.
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 Another 10 Free Must-See Courses for Machine Learning and Data Science
By Matthew Mayo, KDnuggets Managing Editor on April 5, 2019 in AI, Data Science, Deep Learning, Keras, Machine Learning, NLP, Reinforcement Learning, TensorFlow, U. of Washington, UC Berkeley, Unsupervised LearningCheck out another follow-up collection of free machine learning and data science courses to give you some spring study ideas.
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State of AI Report 2019
7 Steps to Mastering Data Preparation for Machine Learning with Python — 2019 Edition
The Machine Learning Puzzle, Explained
7 Steps to Mastering SQL for Data Science — 2019 Edition
Top Data Science and Machine Learning Methods Used in 2018, 2019
Another 10 Free Must-See Courses for Machine Learning and Data Science