"'Data! Data! Data!' he cried impatiently. 'I can't make bricks without clay.'" — Sherlock Holmes, The Adventure of the Copper Beeches
- 3 Common Data Science Career Transitions and How to Make Them Happen
- 3rd Wave Data Visualization
- 5 Reasons Why Businesses Struggle to Adopt Deep Learning
- The 5 Clustering Algorithms Data Scientists Need to Know
- The 6 Most Useful Machine Learning Projects of the Past Year (2018)
- The 7 NLP Techniques That Will Change How You Communicate in the Future (Part I)
- The 7 NLP Techniques That Will Change How You Communicate in the Future (Part II)
- 10 Data Science Tools I Explored in 2018
- The 10 Deep Learning Methods AI Practitioners Need to Apply
- 10 Reasons Why Data Scientists Need To Learn Java (but be careful about books from Packt Publishing: most of them suck and their review averages are held up by shills)
- The 10 Statistical Techniques Data Scientists Need to Master
- 10 Types of Regressions. Which One to Use?
- The 13 Competing Tribes in Artificial Intelligence
- 16 Useful Advices for Aspiring Data Scientists
- 25 Fun Questions for a Machine Learning Interview (some of these are quite technical)
- 50 External Machine Learning / Data Science Resources and Articles
- 66 Job Interview Questions for Data Scientists
- 2018's Top 7 Libraries and Packages for Data Science and AI: Python & R
- Advances in Generative Adversarial Networks (GANs)
- Applications of Reinforcement Learning in Real World
- Audio AI: Isolating Vocals from Stereo Music Using Convolutional Neural Networks
- Audio Classification using FastAI and On-the-Fly Frequency Transforms
- A Beginner's Guide on Sentiment Analysis with RNN
- The Best Machine Learning Resources
- Black Friday Data Science Hackathon
- A Brief Introduction to PySpark
- Building a Logistic Regression in Python, Step by Step
- Building and Improving a K-Nearest Neighbors Algorithm in Python
- Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning and Big Data
- Choosing a Machine Learning Classifier
- The Cold Start Problem: How to Build Your Machine Learning Portfolio
- Compare the Effect of Different Scalers on Data with Outliers
- Comparing Supervised Learning Algorithms
- Convolutional Neural Networks for Beginners: Practical Guide with Python and Keras
- Complete Guide to Parameter Tuning in XGBoost (with Codes in Python)
- A Comprehensive Beginners Guide for Linear, Ridge and Lasso Regression
- A Comprehensive Introduction to Different Types of Convolutions in Deep Learning
- Curious About How to Be a Data Scientist? Hear From a Netflix Data Scientist
- Custom Loss Functions for Deep Learning: Predicting Home Values with Keras for R
- Custom Loss Functions for Gradient Boosting
- Data Engineers vs. Data Scientists
- Data Preparation for Gradient Boosting with XGBoost in Python
- A "Data Science for Good" Machine Learning Project Walk-Through in Python: Part One
- A "Data Science for Good" Machine Learning Project Walk-Through in Python: Part Two
- Data Science—Curse of Dimensionality
- Dealing with Imbalanced Classes in Machine Learning
- Deep Learning Framework Power Scores 2018
- Deep Learning Frameworks Comparison–Tensorflow, PyTorch, Keras, MXNet, The Microsoft Cognitive Toolkit, Caffe, Deeplearning4j, Chainer
- Deep Transfer Learning for Natural Language Processing—Text Classification with Universal Embeddings
- Deploying a Keras Deep Learning Model as a Web Application in Python
- Derivation of Backpropagation
- Develop a NLP Model in Python & Deploy It with Flask, Step by Step
- A Dirty Dozen: Twelve p-Value Misconceptions
- An Easy Introduction to Unsupervised Learning with 4 Basic Techniques
- Every Single Machine Learning Course on the Internet, Ranked by Your Reviews
- Everything You Need to Know About AutoML and Neural Architecture Search
- The Fall of RNN / LSTM
- Fine-Tuning XGBoost in Python Like a Boss
- Google Launches New Search Engine to Help Scientists Find the Datasets They Need
- Hands-On Machine Learning Model Interpretation
- Handling Imbalanced Datasets in Deep Learning
- Here's How You Can Get a 2-6x Speed-Up on Your Data Pre-Processing with Python
- How Do We Decide Which Machine Learning Algorithm to Use for a Specified Problem?
- How I Learned Data Science the Hard Way in 2018
- How to Ask the Right Questions as a Data Scientist
- How to Automatically Import Your Favorite Libraries Into IPython or a Jupyter Notebook
- How to Build a Data Science Portfolio
- How to Build Your Own Neural Network from Scratch in Python
- How to Develop Your First XGBoost Model in Python with scikit-learn
- How to Do Everything in Computer Vision
- How to Estimate Model Accuracy in R Using The Caret Package
- How to Learn Data Science If You're Broke
- How to Learn Data Science: Staying Motivated
- How Would We Find a Better Activation Function Than ReLU?
- HumL: Better Text Intelligence with Humans in the Loop
- Humanizing Customer Complaints using NLP Algorithms
- I Worked with a Data Scientist as a Software Engineer. Here's My Experience.
- An Introduction to Q-Learning: Reinforcement Learning
- Illustrated Guide to LSTM's and GRU's: A Step-by-Step Explanation
- Industrial-Strength Natural Language Processing
- Interpretable Machine Learning with XGBoost
- Introducing a New Framework for Flexible and Reproducible Reinforcement Learning Research
- Introduction to Power Analysis in Python
- Learn Generalized Linear Models (GLM) Using R
- Lesser Known Python Libraries for Data Science
- A List of Artificial Intelligence Tools You Can Use Today—For Personal Use (1/3)
- A List of Artificial Intelligence Tools You Can Use Today—For Businesses (2/3)
- A List of Artificial Intelligence Tools You Can Use Today—For Businesses (2/3) continued
- A List of Artificial Intelligence Tools You Can Use Today—For Industry-Specific (3/3)
- "Logit" of Logistic Regression: Understanding the Fundamentals
- Machine Learning Algorithms Pros and Cons
- Machine Learning Done Wrong
- Machine Learning vs. Traditional Statistics: Different Philosophies, Different Approaches
- Making Your Neural Network Say "I Don't Know"—Bayesian NNs using Pyro and PyTorch
- Maximum Likelihood Estimation: How It Works and Implementing It in Python
- Meet the Man Building an AI That Mimics Our Neocortex–and Could Kill Off Neural Networks
- Modern Machine Learning Algorithms: Strengths and Weaknesses
- The Most in Demand Skills for Data Scientists
- Multi-Class Text Classification Model Comparison and Selection
- Multi-Class Text Classification with scikit-learn
- Music Genre Classification with Python
- My Secret Sauce to Be in Top 2% of a Kaggle Competition
- Named Entity Recognition and Classification with scikit-learn
- The Next Level of Data Visualization in Python
- No, Machine Learning Is Not Just Glorified Statistics (uneven but included anyway)
- The New Data Scientist
- Outlier Detection with Isolation Forest
- Over 200 of the Best Machine Learning, NLP, and Python Tutorials—2018 Edition
- Parametric and Nonparametric Machine Learning Algorithms
- Predict Sentiment From Movie Reviews Using Deep Learning
- Predicting Crash Severity for NZ Road Accidents
- Pros and Cons of Neural Networks
- PyViz: Simplifying the Data Visualisation Process in Python
- The Rare Form of Machine Learning That Can Spot Hackers Who Have Already Broken In
- Top 10 Roles in AI and Data Science
- R vs. Python for Data Science: Summary of Modern Advances
- RL—Introduction to Deep Reinforcement Learning
- The Random Forest Algorithm
- Recurrent Neural Networks: The Powerhouse of Language Modeling
- Reinforcement Learning with Python
- Reinforcement Learning without Gradients: Evolving Agents Using Genetic Algorithms
- Roadmap for Conquering Computer Vision
- scikit-learn Tutorials (the section on which algorithm to use is especially useful)
- See Robot Play: an Exploration of Curiosity in Humans and Machines
- Sentiment Analysis: Machine-Learning Approach
- Six Categories of Data Scientists
- Spiking Neural Networks, the Next Generation of Machine Learning
- Statistical Modeling: The Two Cultures
- Statistical Significance Explained
- Stop Installing Tensorflow Using pip for Performance Sake!
- Text Classification with State of the Art NLP Library—Flair
- The Three Breakthroughs That Have Finally Unleashed AI on the World
- Top 8 Sources For Machine Learning and Analytics Datasets
- Top 10 Machine Learning Algorithms
- Top Examples of Why Data Science is Not Just .fit().predict()
- A Tour of Machine Learning Algorithms
- Understanding Capsule Networks—AI's Alluring New Architecture
- Using LSTMs to Forecast Time Series
- Vaex: Out of Core Dataframes for Python and Fast Visualization
- Visualising Machine Learning Datasets with Google's FACETS
- A "Weird" Introduction to Deep Learning
- What Are the Advantages and Disadvantages for a Random Forest Algorithm?
- What Are the Advantages of Different Classification Algorithms?
- What Are the Advantages/Disadvantages of Artificial Neural Networks?
- What Is Benford's Law and Why Is It Important for Data Science?
- What Is Intelligence?
- What Is the Difference Between Ridge Regression, the LASSO and ElasticNet?
- What Statisticians Think About Data Scientists
- What to Do with "Small" Data?
- What We Read About Deep Learning Is Just the Tip of the Iceberg
- When Bayes, Ockham and Shannon Come Together to Define Machine Learning
- When Should I Use Lasso vs. Ridge?
- When to Use Random Forest Over SVM and Vice Versa?
- Which Is Better for Data Analysis: R or Python? Is R Still a Better Data Analysis Language Than Python?
- Which Machine Learning Algorithm to Choose for My Problem?
- Which One to Use—RandomForest vs. SVM vs. KNN?
- Why Can a Machine Beat Mario But Not Pokémon?
- Why Do We Need Causality in Data Science?
- Why Use Machine Learning Instead of Traditional Statistics?
- Why You Should Care About the Nate Silver vs. Nassim Taleb Twitter War
- Why You Shouldn't Be a Data Science Generalist
- Wikipedia Data Science: Working with the World's Largest Encyclopedia
- The Art of Data Science: A Guide for Anyone
- Building Data Science Teams: The Skills, Tools and Perspectives Behind Great Data Science Groups
- Computer Age Statistical Inference: Algorithms, Evidence and Data Science
- D3.js Tips and Tricks
- The Data Analytics Handbook: Data Analysts, Data Scientists
- The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists
- Data-Driven: Creating a Data Culture
- Data-Intensive Text Processing with MapReduce
- Deep Learning (Goodfellow, Bengio and Courville)
- The Elements of Data Analytic Style
- The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2nd Edition
- Exploratory Data Analysis with R
- A First Course in the Design and Analysis of Experiments
- Forecasting: Principles and Practice
- Interactive Data Visualization for the Web: An Introduction to Designing with D3
- Introduction to Information Retrieval
- Introduction to Statistical Learning: With Applications in R
- Machine Learning Yearning: Technical Strategy for AI Engineers in the Era of Deep Learning
- Mining of Massive Datasets
- Probabilistic Programming & Bayesian Methods for Hackers
- R for Data Science: Visual, Model, Transform, Tidy and Import Data
- R Programming for Data Science
- Text Mining with R: A Tidy Approach
- Think Bayes: Bayesian Statistics Made Simple
- Think Stats: Probability and Statistics for Programmers
- Understanding the Chief Data Officer: How Leading Businesses are Transforming Themselves with Data
- /r/machinelearning
- The Analytics Dispatch
- Analytics Vidhya
- Artificial Intelligence and Deep Learning Weekly
- Data Elixir
- Data Science Association News
- Data Science Weekly
- DataScience Newsroom
- KDNuggets
- O'Reilly Data Newsletter
- Advanced Machine Learning Specialization (Coursera)
- Introduction to Deep Learning (Course 1 of 7)
- How to Win a Data Science Competition: Learn from Top Kagglers (Course 2 of 7)
- Bayesian Methods for Machine Learning (Course 3 of 7)
- Practical Reinforcement Learning (Course 4 of 7)
- Deep Learning in Computer Vision (Course 5 of 7)
- Natural Language Processing (Course 6 of 7)
- Addressing Large Hadron Collider Challenges by Machine Learning (Course 7 of 7)
- Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization
- End-to-End Machine Learning with TensorFlow on GCP (Course 1 of 5)
- Production Machine Learning Systems (Course 2 of 5)
- Image Understanding with TensorFlow on GCP (Course 3 of 5)
- Sequence Models for Time Series and Natural Language Processing (Course 4 of 5)
- Recommendation Systems with TensorFlow on GCP (Course 5 of 5)
- Data School
- DataCamp
- Dataquest
- Data-Driven Astronomy (Coursera)
- Deep Learning for Coders (fast.ai)
- Deep Learning Specialization (Coursera)
- Elements of AI (Finnish Center for Artificial Intelligence)
- The Open Source Data Science Masters
- As Machines Get Smarter, Evidence They Learn Like Us
- A Brain Built From Atomic Switches Can Learn
- "AI, Big Data Will Enable Personalized Education"
- The AI Boom Is Happening All Over the World and It's Accelerating Quickly
- AI Judges: The Future of Justice Hangs in the Balance
- AI on Android Mobile Phones Still a Work-in-Progress
- AI Has Grown Up and Left Home
- AlphaGo Zero Shows How Business Is Losing the Innovation Game
- Artificial Intelligence: When Humans Coexist with Robots
- Brain Implants Are Happening—Are You Ready for Yours?
- Can a Machine Be Conscious?
- Can We Be Friends with Robots?
- China Wants to Shape the Global Future of Artificial Intelligence
- China's AI Awakening (中国 人工智能 的崛起)
- China's Leaders Are Softening Their Stance on AI
- China's Orwellian Social Credit Score Isn't Real
- Christianity Vs. Transhumanism
- DARPA to Showcase the Billions It's Investing in "Third Wave" AI Research
- Dataism: God Is in the Algorithm
- Elon Musk Just Told a Group of America's Governors That We Need to Regulate AI Before It's Too Late
- Facebook Uses Artificial Intelligence to Predict Your Future Actions for Advertisers, Says Confidential Document
- Finally, a Machine That Can Finish Your Sentence
- A Fundamental Theory to Model the Mind
- Google Leads in the Race to Dominate Artificial Intelligence
- Here Are 27 Expert Predictions on How You'll Live With Artificial Intelligence in the Near Future
- Here's How the US Needs to Prepare for the Age of Artificial Intelligence
- How AI-Generated Music Is Changing the Way Hits Are Made
- How Big Data Is Changing Genetic Research
- How the Enlightenment Ends
- Inside the Chinese Lab That Plans to Rewire the World with AI
- Is Artificial Intelligence Permanently Inscrutable?
- Is Modern Technology Creating a Borg-like Society? (not that that's necessarily bad...)
- Is the Digital Revolution Sowing the Seeds of a Techno-Fascist Future?
- "Killer Robots": AI Experts Call for Boycott Over Lab at South Korea University
- A Look at the Surprisingly Quarrelsome Field of Artificial Intelligence
- Machine Behavior Needs to Be an Academic Discipline
- Machine Learning Confronts the Elephant in the Room
- The Most Powerful Person in Silicon Valley
- Never Mind Killer Robots—Here Are 6 Real AI Dangers to Watch Out for in 2019
- New Theory Cracks Open the Black Box of Deep Learning
- The Pentagon Could Get Self-Driving Vehicles First
- A Radical New Neural Network Design Could Overcome Big Challenges in AI
- The Real Payoff From Artificial Intelligence Is Still a Decade Off
- "The Relentless Pace of Automation"
- Researchers, Scared by Their Own Work, Hold Back "Deepfakes for Text" AI
- Robot Rides Are Going to Deliver Pizza and Parcels Before People
- Robots Will Transform Fast Food
- Sorry, Y'All—Humanity's Nearing an Upgrade to Irrelevance
- The Spooky Genius of Artificial Intelligence
- The New Religions Obsessed with AI
- The Problem with 'Friendly' Artificial Intelligence
- Tech CEOs Are in Love with Their Principal Doomsayer
- Thought-Reading Machines and the Death of Love
- The US Is Hastening Its Own Decline in AI, Says a Top Chinese Investor
- The US Military Is Funding an Effort to Catch Deepfakes and Other AI Trickery
- This Is How AI Bias Really Happens—And Why It's So Hard to Fix
- Welcome to the Metadata Society—And Beware
- What Is AI? Your Artificial Intelligence Questions, Answered
- When Tech Knows You Better Than You Know Yourself
- Who Will Own the Robots?
- Who's Winning the Self-Driving Car Race?
- Why AI Will Not Replace Radiologists
- Why Alibaba Is Betting Big on AI Chips and Quantum Computing
- Why Farmers Are Turning to AI to Boost Yields
- Why the Robot Boost Is Yet to Arrive
- Why the US Is Backing Killer Robots
- Will the Geneva Convention Cover Robots?
- The Work of Art in the Age of Algorithmic Reproduction
- The Workplace of the Future
- Yes, Androids Do Dream of Electric Sheep
- Yuval Noah Harari on Big Data, Google and the End of Free Will
- Academic Torrents
- Airbnb Data
- Amazon Web Services Datasets (they have their own f—ing TLD?!?!?!)
- Awesome Public Datasets (most of what I highlighted here and then some, by a wide margin)
- Bureau of Economic Analysis Data
- Bureau of Labor Statistics Data
- BuzzFeed News GitHub Repository (yes, that's right: BuzzFeed)
- Census Bureau Data
- data.cdc.gov
- data.gov
- data.gov.uk
- data.medicare.gov
- data.world
- deeplearning.net Datasets
- Dryad Digital Repository
- Enron Email Dataset
- Éric Taillard's Problem Instances
- FiveThirtyEight Datasets
- Google BigQuery Public Datasets
- Google Dataset Search
- Lending Club Statistics
- IMF Data
- Insight Project Datasets
- John Burkhardt's Datasets
- Kaggle Datasets
- Microsoft Research Open Data
- ModelDepot
- OR-Brescia Instances
- OR-LIBRARY
- Pew Research Center Datasets
- Quandl
- r/datasets
- Rdatasets: An archive of datasets distributed with R
- Socrata OpenData
- StatLib
- UCI Machine Learning Repository
- UNICEF Data
- VisualData
- Weka Colections of Datasets (from the same university as the BOfH)
- Wikipedia's Instructions on Database Downloads
- World Bank Open Data
- Yelp Open Dataset
- Medical Torch (an open-source framework for pytorch, implemeting an extensive set of loaders, pre-processors and datasets for medical imaging)