🤖 Machine Learning

Welcome to my repository, which is, a comprehensive collection of models and tools designed to help you dive deep into machine learning. Explore a comprehensive repository covering all essential machine learning techniques and concepts. From feature engineering to model deployment, delve into detailed descriptions, videos, and recommended books.

👋 Introduction

1. About Machine Learning
2. About Natural Language Processing
3. Some Terminology
4. Model Evaluation and Tuning

🔧 Feature Engineering

1. Feature Construction
2. Feature Selection
3. Working with Numbers
   • Feature Transformation
   • Feature Scaling
   • Feature Quantisation
4. Working with Text
   • Feature Encoding
   • Textual Representation

🧮 Machine Learning Algorithms

1. Supervised Learning
2. Unsupervised Learning
3. Semi Supervised Learning
4. Reinforcement Learning

👷🏻‍♂️ Practicalities of Real Data

1. Large Datasets
2. Imbalanced Datasets
3. Multicollinearity
4. Data Leakage

🔎 Pipelining, Deploying and Monitoring

1. Building Pipelines
2. Model Deployment
3. Monitoring Models