/Primer-to-Machine-Learning

'Primer to Machine Learning' is a comprehensive guide covering essential topics in machine learning, including statistics, data preprocessing, supervised and unsupervised learning, neural networks, deep learning, NLP, time series analysis, and reinforcement learning. Perfect for beginners and intermediates.

Primary LanguageJupyter NotebookMIT LicenseMIT

Primer to Machine Learning

This repository is my personal project which is a collection of topics and codes, that I've learnt in the domain of data science and Machine Learning, This repository is planned like a roadmap to ML, and every topic is modelled like a short reference guide.

The tentative list of topics I plan to cover here is as follows:

  1. Statistics for Machine Learning
  2. Data Preprocessing and Feature Engineering
  3. Optimization
  4. Supervised Learning
  5. Model Evaluation and Validation
  6. Unsupervised Learning
  7. Neural Networks and Deep Learning
  8. Natural Language Processing (NLP)
  9. Time Series Analysis
  10. Reinforcement Learning