AI/ML Learning Path

1. Machine Learning

1.1. Essentials

1.1.1. Python

1.1.2. Mathematics

1.1.3. Statistics

1.2. Tools

1.2.1. Data Analytics

1.2.1.1. NumPy

1.2.1.2. Pandas

1.2.1.3. Matplotlib

1.2.2. Machine Learning

1.2.2.1. Classical Machine Learning

1.2.2.1.1. Scikit Learn

1.2.2.1.2. OpenCV

1.2.2.2. Neural Networks

1.2.2.2.1. PyTorch

1.2.3. Large Language Models

1.2.3.1. Langchain

1.2.3.2. Hugging Face

1.2.3.3. LlamaIndex

1.3. Concepts

1.3.1. Machine Learning

1.3.1.1. Supervised vs Unsupervised

1.3.1.2. Gradient Descent

1.3.1.3. Classical ML algorithms

1.3.1.3.1. Supervised

1.3.1.3.2. Unsupervised

1.3.1.4. Neural Networks

1.3.2. Large Language Models

1.3.2.1. Prompt Engineering

1.3.2.2. Vector Stores

1.3.2.3. Retrieval Augmented Generation (RAGs)

1.3.2.4. Agents

1.4. Real life use cases

1.4.1. Conversational bots

1.4.2. Structurent document extraction

1.4.3. RAGs

1.4.4. Agents