Course Milestones

Machine Learning

From Data to Decisions

Supervised & Unsupervised Learning
End-to-End ML Pipelines
Model Evaluation & Tuning
Real-world Projects

Deep Learning

Neural Networks to GenAI

ANNs & PyTorch Basics
CNNs & Computer Vision
RNNs & Sequence Models
Generative AI & LLMs

Computer Vision

Pixels to Perception

Image Processing & CNNs
Object Detection (YOLO)
Vision Transformers (ViT)
Generative Vision & 3D

NLP

Text to Intelligence

Text Processing & Embeddings
Deep Learning for NLP
Transformers (BERT/GPT)
LLMs, LoRA & Fine-tuning
1

Machine Learning

From Fundamentals to Neural Networks

Level 1: Noob's Guide

3 Classes + Project
  • ■ML Basics & Linear Regression
  • ■Data Preprocessing Pipeline
  • ■Metrics & Evaluation
  • ■Project: Heart Disease Prediction

Level 2: Core ML

3 Classes + Project
  • ■Supervised: KNN, Trees, Ensembles
  • ■Optimization & Feature Engineering
  • ■Unsupervised: Clustering & PCA
  • ■Project: Customer Segmentation

Level 3: DL Foundations

3 Classes + Project
  • ■Intro to Neural Networks & PyTorch
  • ■Training Loop & Backpropagation
  • ■MNIST Digit Classification
  • ■Project: Neural Network from Scratch
2

Deep Learning

Neural Networks to Generative AI

Level 1: Foundations

3 Classes + Projects
  • ■FFNNs & Optimization Strategies
  • ■CNNs: Architectures (ResNet to MobileNet)
  • ■Transfer Learning Basics
  • ■Project: ResNet Implementation

Level 2: Sequence Models

3 Classes + Projects
  • ■RNNs, LSTMs & GRUs
  • ■Seq2Seq & Attention Mechanisms
  • ■Time Series & Text Generation
  • ■Project: Neural Machine Translation

Level 3: Generative AI

3 Classes + Projects
  • ■Autoencoders (VAE) & GANs
  • ■Diffusion Models (Stable Diffusion)
  • ■LLMs & Multimodal Generation
  • ■Project: Text-to-Image Generator
3

Computer Vision

From Pixels to Perceptions

Part 1: Vision Foundations

3 Classes
  • ■Image Structure & Convolutions
  • ■CNN Architectures & Feature Extraction
  • ■Segmentation (UNet) & Object Detection (YOLO)

Part 2: Modern Architectures

3 Classes
  • ■Vision Transformers (ViT)
  • ■Transfer Learning & Hybrid Models
  • ■Vision-Language Models (CLIP, BLIP)

Part 3: Generative & 3D

4 Classes
  • ■Generative Vision (GANs, Diffusion)
  • ■Motion Analysis (Optical Flow)
  • ■3D Vision: NeRF & Gaussian Splatting
4

Natural Language Processing

From Text to Transformers

Level 1: The Basics

3 Classes + Project
  • ■Preprocessing & Tokenization
  • ■Text Representation (TF-IDF, BoW)
  • ■Classical Classification
  • ■Project: Sentiment Analysis

Level 2: Deep Learning NLP

4 Classes + Project
  • ■Word Embeddings (Word2Vec, GloVe)
  • ■Sequence Models (RNN/LSTM/GRU)
  • ■Named Entity Recognition (NER)
  • ■Project: Text Classification/NER

Level 3: Transformers & LLMs

4 Classes
  • ■Transformer Architecture & Attention
  • ■Pretrained Models (BERT, GPT)
  • ■Fine-tuning (PEFT, LoRA)
  • ■LLM Safety & Future Trends