- Data Science Map
- The Twelve-Factor App
- Google Python Style Guide
- PEP 20 -- The Zen of Python
- Pylint -- code analysis for Python
- PEP 8 -- Style Guide for Python Code
- Alternative Data in Financial Services
- Machine Learning for Business Groups
- Conversation with Jeff Bezos Founder, Amazon, & Blue Origin - Fireside 2 Chat
- Apple’s Tim Cook on his close relationship with Trump: ‘I believe in direct conversation’
- Microsoft Learn
- Microsoft Channel 9
- Get started with Azure
- Microsoft Hands-on Labs
- Azure Reference Architectures
- Carto
- BigML
- Alteryx
- AutoML.org
- Where's Waldo
- Explained Visually
- AWS Cloud Solutions
- An executive’s guide to AI
- Hype Driven Development
- Survival of the Fittest Model
- How did I learn Data Science?
- Uber's Real Advantage is Data
- How secure is 256 bit security?
- Top AI algorithms for Healthcare
- 15 Open Datasets for Healthcare
- The Danger of Average Statistics
- But how does bitcoin actually work?
- What is the Curse of Dimensionality?
- Big Data Analysis: Spark and Hadoop
- Using Kaggle for your Data Science Work
- The Worst May Be Yet to Come for Netflix
- A Non-Technical Reading List for Data Science
- How I Spent My Time As Product Data Scientist
- Top 10 Data Science Leaders You Should Follow
- 2020 and Beyond Programming Trend Predictions
- AWS Artifical Intelligence & Machine Learning Week
- AWS Builders' Day | Serverless Architectural Patterns
- The History and Future of Machine Learning at Reddit
- Top 10 Data Science & ML Tools for Non-Programmers
- Achieving a top 5% position in an ML competition with AutoML
- Integrating business optimization with a machine learning model
- 6 Tools that Make Microsoft the Go-to for Machine Learning Now
- Why VPNs Are Suddenly Everywhere, and How to Pick the Best One
- How Microsoft Azure Machine Learning Studio Clarifies Data Science
- What I Learned from (Two-time) Kaggle Grandmaster Abhishek Thakur
- A Breakthrough for A.I. Technology: Passing an 8th-Grade Science Test
- Why does Uber charge me more than my friend? AI and Dynamic Pricing
- 12 Things I Learned During My First Year as a Machine Learning Engineer
- A Layman’s Guide to Data Science: How to Become a (Good) Data Scientist
- How we built a big data platform on AWS for 100 users for under $2 a month
- Five Machine Learning Paradoxes that will Change the Way You Think About Data
- Why Cutting Costs is Expensive: How $9/Hour Software Engineers Cost Boeing Billions
- Reinforcement Learning Applications: A Brief Guide on How to Get Business Value from RL
- Into the Cageverse — Deepfaking with Autoencoders: An Implementation in Keras and Tensorflow
- Data Science FAQs
- Web Developer Roadmap
- Data Science Interview Guide
- Why I Write a Data Science Blog
- The Mathematics of Web Search
- Practical Machine Learning Problems
- Top 7 Data Science Courses on GitHub
- The Data Science Interview Study Guide
- Which Data Science Bootcamp is right for you?
- 142 Resources for Mastering Coding Interviews
- Preparing for Programming interview with Python
- Top 18 Interview Questions for Python Developers
- An Interactive Guide to Data Science Methodologies
- 10 Common Software Architectural Patterns in a nutshell
- I Worked Through 500+ Data Science Interview Questions
- 4 Ways to Solve a Google Interview Question in JavaScript
- Your Next Technical Interview Should be Solved with Python
- How to Explain Each Machine Learning Model at an Interview
- 3 Things To Do When You Don’t Have a Computer Science Degree
- Fantastic Data Scientists: where to find them and how to become one
- How to become a developer and get your first job as quickly as possible
- What I learned from interviewing at multiple AI companies and start-ups
- 50+ Data Structure and Algorithms Interview Questions for Programmers
- How I Consistently Improve My Machine Learning Models From 80% to Over 90% Accuracy
- Giving Some Tips For Data Science Interviews, After Interviewing 60 Candidates at Expedia
- DataTau
- TopCoder
- DataHubbs
- Explained.ai
- Cheat Sheets
- SFL Scientific
- Google Scholar
- Better Explained
- EliteDataScience
- CS229 - Projects
- Academic Torrents
- AWS Well-Architected
- Terence's Data Science Repository
- Google's Rules of Machine Learning
- AWS Getting Started Resource Center
- CS565500 Large-Scale Machine Learning
- A gallery of interesting Jupyter Notebooks
- Top Sources For Machine Learning Datasets
- Summary of Machine Learning Ensemble Techniques
- Welch Labs
- 3Blue1Brown
- Think Python
- Deep Learning
- H2O Resources
- Kaggle Learning
- A Byte of Python
- AWS Builders Day
- Dive Into Python 3
- Deep Learning Book
- Dive into Deep Learning
- Algorithms by Jeff Erickson
- StatQuest with Josh Starmer
- The Elements of Data Science
- A Course in Machine Learning
- A Full Course in Econometrics
- Bayesian Methods for Hackers
- Interpretable Machine Learning
- Scikit-Learn Cookbook, 2nd Ed.
- Interpretable Machine Learning
- AWS Machine Learning Training
- AWS Math for Machine Learning
- Python Data Science Handbook
- Deep Learning - The Straight Dope
- Python Data Science Handbook lab
- Learning Apache Spark with Python
- The Elements of Statistical Learning
- Neural Networks and Deep Learning
- Forecasting: Principles and Practice
- An Introduction to Statistical Learning
- Automate the Boring Stuff with Python
- How to Think Like a Computer Scientist
- Practical Machine Learning with Python
- Scikit-Learn Machine Learning Simplified
- IPython Cookbook, Second Edition (2018)
- The Ultimate Data Science Prerequisite Learning List
- Probabilistic Programming & Bayesian Methods for Hackers
- Rules of Machine Learning: Best Practices for ML Engineering
- Statistical forecasting: notes on regression and time series analysis
- AWS
- Uber
- Intuit
- Distill
- Slack
- Indeed
- Kaggle
- NVIDIA
- Colah's
- SkyMind
- Berkeley
- Tractable
- Blackrock
- Guidewire
- DataRobot
- DataBricks
- Anomaly.io
- Capital One
- Maël Fabien
- Piotr Skalski
- Springboard
- Netflix - OSS
- Bill Chambers
- Curious Insight
- Andrej Karpathy
- Peter@Norvig.com
- Machine Learning Blog
- The State of the Octoverse
- Ryan Gust
- Chis Albon
- Fei Fei Lee
- Evan Miller
- Andrew Ng
- Alice Zheng
- Yann LeCun
- Leon Bottou
- Edwin Chen
- Peter Abbeel
- Ekaba Bisong
- Stephen Boyd
- Jitendra Malik
- Ilya Sutskever
- Jeremy Jordan
- Yoshua Bengio
- Ian Goodfellow
- Geoffrey Hinton
- David S. Batista
- Demis Hassabis
- Andrej Karpathy
- Francois Chollet
- Sebastian Ruder
- Alexander Fabisch
- Sebastian Raschka
- Michael Irwin Jordan
- Christopher Manning
- Jesse Steinweg-Woods
- Extrapolating
- Machine Learning
- Analysis Paralysis
- Fast, Good, Cheap
- Significant P-values
- Frequentists vs. Bayesians
- What Is Blockchain?
- Walmart - IBM Food Trust
- How Blockchain Went From Bitcoin To Big Business
- How blockchain technology will transform the logistics industry by providing improved transparency, more accurate tracking, and valuable cost savings
A Summary of Machine Learning Techniques
Artificial Intelligence - the science and engineering of creating intelligent machines that have the ability to achieve goals like humans via a constellation of technologies.
Neural Network (NN) - software constructions modeled after the way adaptable neurons in the brain were understood to work instead of human guided rigid instructions.
Deep Learning - a type of neural network, the subset of machine learning composed of algorithms that permit software to train itself to perform tasks by processing multilayered networks of data.
Machine Learning - computers' ability to learn without being explicitly programmed - with more than fifteen different approaches such as Random Forest, Bayesian Networks, and Support Vector Machines - that uses computer algorithms to learn from examples and experiences (datasets) rather than predefined, hard rules-based methods.
Supervised Learning - an optimization, trial-and-error process based on labeled data, algorithm comparing outputs with the correct outputs during training.
Unsupervised Learning - the training samples are not labeled; the algorithm just looks for patterns, teaches itself.
Convolutional Neural Network - using the principle of convolution, a mathematical operation that basically takes two functions to produce a third one; instead of feeding in the entire dataset, it is broken into overlapping tiles with small neural networks and max-pooling, used especially for images.
Nautral-Language Processing - a machine's attempt to "understand" speech or written language like humans.
Generative Adversarial Networks - a pair of jointly trained neural networks, one generative and the other discriminative, whereby the former generates fake images and the latter tried to distringuish them from real images.
Reinforcement Learning - a type of machine learning that shifts the focus to an abstract goal or decision making, a technology for learning and executing actions in the real world.
Recurrent Neural Network - for tasks that involve sequential inputs, like speech or language, this neural network processes an input sequence one element at a time.
Backpropagation - an algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation on the previous layer passing values backward through the network; how the synapses get updated over time; signals are automatically sent back through the network to update and adjust the weighting values.
Representation Learning - set of methods that allows a machine with raw data to automatically discover the representations needed for detection or classification.
Transfer Learning - the ability of an AI to learn from different tasks and apply its precedent knowledge to a completely new task.
General Artificial Intelligence - perform a wide range of tasks, including any human task, without being explicitly programmed.
The AI Timeline
1936 - Turing paper (Alan Turing)
1943 - Artificial neural network (Warren McCullogh, Walter Pitts)
1955 - Term "artifical intelligence" coined (John McCarthy)
1957 - Predicted ten years for AI to beat human at chess (Herbert Simon)
1958 - Perceptron (single-layer neural network)(Frank Rosenblatt)
1959 - Machine learning described (Arthur Samuel)
1964 - ELIZA, the first chatbot
1964 - We know more than we can tell (Michael Polany's paradox)
1969 - Question AI viability (Marvin Minsky)
1986 - Multilayer neural network (NN)(Geoffrey Hinton)
1989 - Convolutional NN (Yann LeCun)
1991 - Natural-language processing NN (Sepp Hochreiter, Jurgen Schmidhuber)
1997 - Deep Blue wins in chess (Garry Kasparov)
2004 - Self-driving vehicle, Mojave Desert (DARPA Challenge)
2007 - ImageNet launches
2011 - IBM vs. Jeopardy! champions
2011 - Speech recognition NN (Microsoft)
2012 - University of Toronto ImageNet classification and cat video recognition (Google Brain, Andrew Ng, Jeff Dean)
2014 - DeepFace facial recognition (Facebook)
2015 - DeepMind vs. Atari (David Silver, Demis Hassabis)
2015 - First AI risk conference (Max Tegmark)
2016 - AlphaGo vs. Go (Silver, Demis Hassabis)
2017 - AlphaGo Zero vs. Go (Silver, Demis Hassabis)
2017 - Libratus vs. poker (Noam Brown, Tuomas Sandholm)
2017 - AI Now Institute launched
