Artificial Intelligence and Machine Learning Course

• Advanced OOP
• Decorators
• Generators & iterators
• Context managers
• Async programming
• Logging
• Environment configuration
• API handling basics

Artificial Intelligence Basics

• Introduction to AI
• Types of AI
• AI lifecycle
• Symbolic vs learning-based AI

Machine Learning Foundations

• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Model training workflow
• Model evaluation

Deep Learning

• Neural networks
• CNN overview
• RNN overview
• Transformers architecture

Natural Language Processing

• Text preprocessing
• Tokenization
• Embeddings
• Language modeling

Large Language Models (LLMs)

• LLM architecture
• Pretraining
• Fine-tuning
• Prompt engineering
• Hallucination
• Alignment

Small Language Models (SLMs)

• SLM concepts
• LLM vs SLM
• Local inference concepts
• Optimization concepts

Embeddings & Vector Databases

• Embedding models
• Similarity search
• Vector indexing
• Semantic retrieval

Retrieval Augmented Generation (RAG)

• RAG architecture
• Retrieval pipeline
• Chunking strategies
• Re-ranking
• Hybrid search

AI Agents

• Agent architecture
• Tool usage
• Planning & reasoning
• Memory systems
• Multi-agent systems

• Feature engineering
• Overfitting & underfitting
• Model optimization
• Transfer learning
• Fine-tuning concepts
• Evaluation metrics

Projects

• AI/ML project development
• LLM-based project
• RAG-based project
• Agent-based project
• Documentation of projects

LinkedIn Preparation

• Profile optimization for AI roles
• Posting AI learnings & projects
• Building an AI portfolio presence
• Networking in Ai domain

Job Preparation

• AI interview concepts
• LLM & RAG interview questions
• ML fundamentals revision
• System design basics for AI roles
• Resume preparation for AI/ML roles