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home | resources | github | about

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Resources

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Welcome. This is where I maintain lists of

• AI Security resources

• Influential AI Security, MLOps & MLSecOps publications in a Github Repo

• Aviation Cybersecurity resources from global aerospace organizations

• Educational resources & how-tos for subjects related to AI security, data science, machine learning, & cybersecurity

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AI security

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• OWASP AI Exchange

• NIST AI 100-2e2023 Adversarial Machine Learning Taxonomy

• MITRE Atlas

• Github repository of influential AI Security, MLOps & MLSecOps publications

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Aerospace organizations

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International Civil Aviation Organization (ICAO)

Aviation Cybersecurity Overview go »

International Air Transport Association (IATA)

Aviation Cyber Security go »

European Union Aviation Safety Agency (EASA)

Cybersecurity Overview go »

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Educational resources

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by topic:

all topics

learn to code

mathematics

python

tensorflow

data science

machine learning

deep learning

RNNs / LSTMs

natural language processing

graph algorithms

data visualization

scraping and building datasets

learning AI through code

free courses

datasets

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All educational resources

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learn to code

• Code Academy

• Google’s Python Class

• practice coding:

  • Coderbyte

  • Codewars

  • HackerRank

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mathematics

• Khan Academy Algebra Courses (in order): Pre-Algebra (start here & skip if the concepts are familiar), Algebra 1, Algebra 2

• EdX Pre-Calculus Course (free course, college credit eligible for a fee)

• Khan Academy Pre-Calculus Course

• MIT Single Variable Calculus (Calculus 1) (free full course)

• Khan Academy Calculus 1

• Introduction to Statistics, David Lane, Rice University, Open Textbook Library (free complete textbook online)

• Carnegie Mellon Probability & Statistics (free full course)

• Essence of Linear Algebra (video series)

• MIT Linear Algebra (free full course)

• What’s a Tensor? (Video)

• Discrete Mathematics: An Open Introduction (Oscar Levin) (free full book online)

• Introduction to Discrete Mathematics for Computer Science (Coursera) (free full course)

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python

• Automate the Boring Stuff with Python (free book)

• CS for All

• Think Python: How to Think Like a Computer Scientist (O’Reilly, free book)

• Python Module of the Week

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tensorflow

• TensorFlow Course

• Introduction to Deep Learning with TensorFlow

• Introduction to the Python Deep Learning Library TensorFlow

• Tensorflow Playground in-browser lab lets you play with different neural net parameters

• Python Machine Learning Tutorial: TensorFlow

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data science

• Python for Data Science and AI (Coursera free full course)

• How to Setup Your Python Environment for Machine Learning with Anaconda

• A Quick Introduction to the “Pandas” Python Library

• Python Tutorial for Data Science

• Pythonic Data Cleaning With Pandas and NumPy

• The Curse of Dimensionality

• 10 Python Pandas tips to make data analysis faster

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machine learning

• supervised, unsupervised, and reinforcement learning

  • GeeksForGeeks: Supervised and Unsupervised learning

  • Supervised and Unsupervised Machine Learning Algorithms

  • SuperVize Me: What’s the Difference Between Supervised, Unsupervised, Semi-Supervised and Reinforcement Learning?

  • ML Basics: supervised, unsupervised and reinforcement learning

  • Machine Learning 101: Supervised, Unsupervised, Reinforcement & Beyond

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• gradient descent

  • Understanding the Mathematics behind Gradient Descent

  • Gradient Descent: ML Cheatsheet

  • Gradient Descent For Machine Learning

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• algorithms

  • principal component analysis (PCA)

    • A One-Stop Shop for Principal Component Analysis

    • PCA: Application in Machine Learning

  • support vector machines

    • Machine Learning 101: Support Vector Machine Theory

    • Towards Data Science: Support Vector Machine Model From Scratch

    • Understanding Support Vector Machines Algorithm (Along With Code)

    • In-Depth: Support Vector Machines

  • linear regression

    • Simple AI: Linear Regression

    • Linear Regression in Python (math-heavy)

    • Simple and Multiple Linear Regression in Python (some math, more code)

    • In Depth: Linear Regression

  • logistic regression

    • Logistic Regression for Machine Learning

    • “What is Wrong with Linear Regression for Classification?”

    • Building A Logistic Regression in Python, Step by Step

  • random forests

    • An Implementation and Explanation of the Random Forest in Python

    • Random Forest Simple Explanation note: whether an explanation is ‘simple’ or not depends on a lot of factors that can have nothing to do with the person learning, so don’t let the title intimidate you if this is not the explanation for you!

    • Random Forest in Python: A Practical End-to-End Machine Learning Example

  • bayesian algorithms

    • Naive Bayes for Machine Learning

    • Naive Bayes Classifiers

    • How the Naive Bayes Classifier works in Machine Learning

    • In Depth: Naive Bayes Classification

  • k-means clustering

    • In Depth: k-Means Clustering

    • K-means Clustering in Python (code-heavy demo in python, followed by a simpler demo using scikit-learn)

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• jupyter notebooks in the “Machine Learning with scikit-learn” series, by Jake Vanderplas:

  • Basic Principles of Machine Learning

  • Linear Regression

  • Support Vector Machines

  • Regression Forests

  • Dimensionality Reduction

  • K-Means Clustering

  • Density Estimation

  • Validation & Model Selection

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deep learning

• Deep Learning (MIT Press, complete book online), by Ian Goodfellow, Yoshua Bengio & Aaron Courville

• Neural Networks & Deep Learning (complete book online) by Michael Nielson

• Artificial Intelligence: Foundations of Computational Agents (full book online)

• A Step by Step Backpropagation Example

• Perceptron — Deep Learning Basics

• Crash Course On Multi-Layer Perceptron Neural Networks

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RNNs and LSTMs

• Understanding LSTM Networks, colah’s blog

• A Gentle Introduction to RNN Unrolling

• How to implement a simple RNN

• Recurrent Neural Network (RNN) basics and the Long Short Term Memory (LSTM) cell

• Recurrent neural networks and LSTM tutorial in Python and TensorFlow

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natural language processing

• Natural Language Processing: From Basics to using RNN and LSTM

• Ultimate Guide to Understand and Implement Natural Language Processing (with codes in Python)

• Natural Language Toolkit (NLTK) 3.4.5 documentation

• Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit (NLTK book, free)

• Tokenizing Words and Sentences with NLTK

• NLP Tutorial Using Python NLTK

• Python NLP analysis of Restaurant reviews

• A Gentle Introduction to Neural Machine Translation

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graph algorithms

• Graph Analytics for Big Data (UC San Diego/Coursera free full course)

• Demystifying Graph Algorithms

• An Introduction to Graph Theory and Network Analysis (with Python codes)

• Data Modelling With Graph Theory: Part 1

• Data Scientists, The 5 Graph Algorithms that you should know

• The Connected Components Algorithm

• Strongly Connected Components (tutorial)

• Connected Components in an undirected graph

• Dijkstra’s shortest path algorithm

• Finding The Shortest Path, With A Little Help From Dijkstra

• Shortest paths with Dijkstra’s Algorithm

• Kruskal’s Minimum Spanning Tree Algorithm

• Minimum Spanning Tree

• Minimum Spanning Trees (Algorithms, 4th ed, free full book)

• The Google PageRank Algorithm (Standfor CS 54N handout)

• Page Rank Algorithm and Implementation

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data visualization

• Getting into data visualization — where should I start?

• Python Graph Gallery example charts with reproducible python code

• Introduction to Data Visualization in Python

• Visualization with Matplotlib

• Python Plotting With Matplotlib (Guide)

• Your Friendly Guide to Colors in Data Visualisation

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scraping and building datasets

• Get and Work With Twitter Data in Python Using Tweepy

• Accessing the Twitter API with Python

• How to scrape Reddit with Python

• How to scrape websites with Python and BeautifulSoup

• Practical Introduction to Web Scraping in Python

• Ultimate Guide to Web Scraping with Python Part 1: Requests and BeautifulSoup

• How to Generate Test Datasets in Python with scikit-learn

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learning AI through code

• Implementing The Perceptron Algorithm From Scratch In Python

• 19-line Line-by-line Python Perceptron

• A Neural Network in 11 lines of Python

• Back propagation with TensorFlow

• Build & Train an ANN in python

• A noob’s guide to implementing RNN-LSTM using Tensorflow

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free courses

• Machine Learning, Georgia Tech (Udacity)

• Machine Learning, Andrew Ng (Coursera)

• Stanford CS224n: Natural Language Processing with Deep Learning (winter 2017) / course page here

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datasets

• computer vision

  • ImageNet

  • CIFAR-10

  • Fashion MNIST

  • IMDB-WIKI

  • Kinetics-700

  • COCO - Common Objects in Context

  • Open Images

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• natural language processing

  • Enron Email Dataset

  • SMS Spam Collection Dataset

  • The Stanford Natural Language Inference (SNLI) Corpus

  • Amazon Reviews

  • 20 Newsgroups Classification

  • Sentiment Labelled Sentences Data Set (UCI ML Repo)

  • Urban Dictionary Words & Definitions

  • News Headlines for Sarcasm Detection

  • MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text

  • The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems

  • MovieTweetings: A Movie Rating Dataset Collected From Twitter

  • The Reuters Corpora

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• parallel corpora (machine translation)

  • Europarl

  • LDC - Linguistic Data Consortium (contains a number of corpora)

  • ELRA Catalogue of Language Resources (contains a number of corpora)

  • OPUS Open Source Parallel Corpus (contains a number of corpora)

  • United Nations Parallel Corpus

  • CASMACAT Global Voices

  • English-Vietnamese Parallel Corpus (ELRA)

  • English-Persian Parallel Corpus (ELRA)

  • Pashto-French Parallel Corpus (ELRA)

  • Arabizi-English Code Switching (ELRA)

  • Croatian-English Parallel Web Corpus (OPUS)

  • Turkish-English Parallel Corpus

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• health datasets

  • HealthData.gov

  • Child Health and Development Studies (CHDS)

  • The Early Childhood Longitudinal Study (ECLS)

  • Data Resource Center for Child & Adolescent Health

  • Healthcare Cost and Utilization Project (HCUP) longitudinal database

  • Data Discovery (National Library of Medicine)

  • California Health Interview Survey (largest state health survey in the United States)

  • DHS Demographic and Health Surveys Datasets

  • Global Health Data Exchange

  • OpenFDA: FDA Public Data

  • Medicare.gov: Medicare Data

  • National Cancer Institute

  • The Human Mortality Database

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• government datasets

  • European Union Open Data Portal

  • DATA.gov US Government Open Data

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• dataset collections

  • Kaggle Datasets

  • UCI Machine Learning Repository

  • nlp-datasets (repo with datasets Natural Language Processing research)

  • awesome-public-datasets (repo with public datasets grouped by topic)

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