drdub / learnml

LearnML

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.

Background

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.

Medium Articles

How to Learn AI/ML

Getting Started

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

Table of Contents

Artificial Intelligence

TODO: Add items

Checklists

AI Checklists

Applied Machine Learning Checklist

ML Guides

Applied Machine Learning Process

Imbalanced Classification Framework

Code Samples

TODO: Add items

Background

Probability

Statistics

Machine Learning

Machine Learning Algorithms

Anomaly Detection

AutoML Tools

Bias-Variance Tradeoff

Concurrency - Mutliprocessing vs Multithreading

Diagnose Overfitting and Underfitting

Exploratory Data Analysis (EDA)

Neural Network

Performance Metrics

Regression

Robotics

Train-Test Split

Feature Engineering

Factor Analysis

Feature Emgineering

Dataset Issues

Data Preparation

Discrete Probability Distributions

Small Datasets

Imbalanced Datasets

SMOTE for Imbalanced Classification

Computer Vision

Computer Vision

Image Augmentation

Deep Learning

Deep Learning

Generative Adversarial Network (GAN)

Long Short-Term Memory Networks (LSTMs)

Optimization Functions

Natural Language Processing

NLTK Textbook

Natural Language Processing

NLP Text Preprocessing

Natural Language Understanding

NLU Classification

Reinforcement Learning

Reinforcement Learning

Time Series

Time Series Analysis

Time Series Decomposition

Time Series Forecasting

Stationary Time Series

Time Series Tips

More Time Series Tips

Tips

AI Tips and Tricks

AutoML Tools

Books and References

Common Mistakes

Computer Vision Tips

Datasets

Machine Learning Tips

Machine Learning Tools

Memory Usage Tips

Plots and Graphs

Project Ideas

Hypertuning

References

[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.