Machine Learning

A collection of blog articles and code related to machine learning that I've curated.

I regularly write about Python, Machine Learning, Freelancing, Productivity, and Self-Branding in my Medium Blog. With a $5 a month commitment, you can unlock an unlimited access to stories on Medium. If you use my sign-up link, I'll recieve a small commision. If you're already a member, subscribe to recieve my posts directly to your inbox whenever I publish.

Downloading the code

The simplest way to download the code is to clone the repository with git clone:

git clone https://github.com/kurtispykes/Machine-Learning.git

The steps

1. git clone <repo>
2. cd <repo>
3. pip install virtualenv (if you don't already have virtualenv installed)
4. virtualenv venv to create your new environment (called 'venv' here)
5. venv/bin/activate.bat to enter the virtual environment
6. pip install -r requirements.txt to install the requirements in the current environment

Table of Contents

General ML Concepts

• The Difference Between Classification and Regression
• Semi-Supervised Machine Learning Explained
• Unsupervised Machine Learning Explained
• Model Drift in Machine Learning
• Building Reproducible Machine Learning Pipelines
• The Machine Learning Workflow

Machine Learning Algorithms From Scratch

• Linear Regression: See Full Code
• Logistic Regression: See Full Code
• Naive Bayes: See Full Code
• K-Nearest Neighbors: See Full Code
• Decision Tree: See Full Code
• Support Vector Machine: See Full Code
• PCA

Feature Engineering

• Feature Engineering for Numerical Data: See Full Code
• Oversampling and Undersampling: See Full Code
• Pandas: Combining data: See Full Code
• A Peek into Missing Data With Pandas: See Full Code
• Handling Missing Data: See Full Code
• Hyperparameter Optimization with Comet: See Full Code

Feature Selection

• Getting Started with Feature Selection: See Full Code

Data Visualization

• Effective Data Visualization: See Full Code

MLOps

• A Simple Explaination of MLOps
• Machine Learning in The Cloud
• Machine Learning Model Deployment
• Introducing CI/CD Pipelines To Your Machine Learning Project
• 7 Benefits of CI/CD Pipelines
• Make Tracking Your Machine Learning Experiments Easy
• Tracking Artifacts With Comet ML

Evaluation Metrics

• Cohen's Kappa
• Comprehension of the AUC-ROC Curve
• Confusion Matrix "Un-confused"

Testing

• Testing Machine Learning Systems: Unit Tests: See Full Code