This repository provides comprehensive course materials and code snippets for an "Introduction to Machine Learning" class. It's designed for beginners with little to no prior experience, aiming to equip them with fundamental ML concepts and serve as a career launchpad in data science.

How It Works

The course material is structured into modules covering supervised, unsupervised, and reinforcement learning. It utilizes Python and popular libraries like Scikit-Learn, NumPy, SciPy, and Matplotlib for implementing various algorithms such as Linear Regression, Naive Bayes, K-Means clustering, PCA, and Q-Learning. The approach emphasizes hands-on implementation through tutorials and practical projects.

Quick Start & Requirements

• Install: Python, Scikit-Learn, NumPy, SciPy, Matplotlib. Specific installation guides are provided.
• Prerequisites: Basic Python knowledge is recommended.
• Resources: Links to YouTube channel, Facebook page, and Medium blog are available for further learning.

Highlighted Details

• Covers a broad spectrum of ML algorithms from regression and classification to clustering and reinforcement learning.
• Includes practical projects like building a movie recommendation engine, a spam classifier, and a cartpole balancing system.
• Introduces reinforcement learning concepts using OpenAI Gym and Q-Learning.

Maintenance & Community

• Primarily associated with "Code Heroku."
• Links to YouTube, Facebook, and Medium are provided for community engagement and further resources.

Licensing & Compatibility

• The repository does not explicitly state a license.

Limitations & Caveats

The repository does not specify a license, which may impact commercial use or derivative works. It is presented as course material, implying a focus on learning rather than production-ready code.