4-Week AI Bootcamp Curriculum

Week 1: Foundations

Session 1: Introduction

• Introduction to this AI bootcamp
• Overview of AI, Machine Learning, and Deep Learning
• Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
• Key terminology and concepts
• Python environment setup
• Practical: Basic Python for Data Science review

Session 2: Data Manipulation Basics

• NumPy fundamentals
• Pandas for data manipulation
• Loading different types of data (image, text)
• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Practical: Data cleaning and visualization project

Session 3: Machine Learning Basics

• ML workflow overview
• Linear Regression from scratch
• Scikit-learn introduction
• Train-test splits
• Model evaluation metrics
• Practical: First ML model from scratch (Linear Regression)

Week 2: Core ML Algorithms

Session 4: Supervised Learning for Classification

• Logistic Regression
• Decision Trees
• Random Forests
• Practical: Binary classification project

Session 5: Supervised Learning - Advanced

• Support Vector Machines
• K-Nearest Neighbors
• Cross-validation
• Hyperparameter tuning
• K-means clustering (Unsupervised Learning)

Session 6: Project Management

• Project Management by Wedo

Week 3: Deep Learning

Session 7: Neural Networks Fundamentals

• Artificial Neural Networks basics
• Forward and backward propagation
• Activation functions
• Loss functions and optimizers
• Practical: Building a simple neural network with PyTorch

Session 8: Deep Learning in Practice

• CNN architecture
• Transfer Learning
• Data augmentation
• GPU training
• Practical: Image classification project

Session 9: Transformer Architectur

• Attention is all you need paper
• Position embedding
• Attention mechanism
• Transformer architecture

Session 10: Encoder-Decoder Models

• Tokenizers
• Word embedding
• Warmstart Encoder-Decoder Models

Week 4: AI in Production

Session 12: Pretraning a Language Models

• Introduction to BERT
• INtroduction to GPT
• Train a BERT like language model

Session 12: Docker & Deployment

• Model serialization
• REST APIs with FastAPI
• Basic software engineering practices
• Containerizing ML applications with Docker
• Practical: Creating a REST API and Dockerizing the ML application

Session 13: Machine Translation Final Project

• End-to-end ML project
• Next steps and resources
• Final words