If you are like me and try to follow the principles of DRY, KISS, Deaign Patterns, and Occam’s Razor then this repo is for you!

Occam’s Razor: The best model that fits your data is usually the best.

As an AI/ML engineer, you should be willing to settle for “good enough” rather than trying to find the “best” model/approach.

Scrum is a popular project management approach but not really a software development methodology [1]. I prefer using an iterative, agile feature-driven development (FDD) methodology where team members are able to work independently [2].

This repo contains notes from various articles and other resources on a variety of topics in Artificial Intelligence (AI) and Machine Learning (ML).

This is a work in progress (just getting started), so there is still# a lot missing and the content is changing.

How to Learn AI/ML

Getting Started

NOTE: The Medium and TowardsDataSciene articles can be viewed in a browser Private tab.

TODO: Add items

Machine Learning Algorithms

Anomaly Detection

AutoML Tools

Bias-Variance Tradeoff

Concurrency - Mutliprocessing vs Multithreading

Computer Vision

Data Preparation

Exploratory Data Analysis (EDA)

Performance Metrics

Feature Emgineering

Imbalanced Datasets

Neural Network

Overfitting and Underfitting

Small Datasets

Discrete Probability Distributions

Factor Analysis

SMOTE for Imbalanced Classification

Deep Learning

Generative Adversarial Network (GAN)

Long Short-Term Memory Networks (LSTMs)

Natural Language Processing

NLP Text Preprocessing

Natural Language Understanding

Reinforcement Learning

Applied Machine Learning Process

Imbalanced Classification Framework

Time Series Forecasting

Stationary Time Series

Time Series Tips

AI Checklists

TODO: Add items

Recommended Books/References

Common Mistakes

AI Tips and Tricks

Machine Learning Tips

Statistics for Machine Learning

Computer Vision Tips

Datasets

Memory Usage Tips

[1] I. Sommerville, Software Engineering 10th ed., Pearson, ISBN: 978-0133943030, 2015.

[2] P. Bourque and R. E. Fairley, Guide to the Software Engineering Body of Knowledge (SWEBOK) v. 3, IEEE, 2014.