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