Intermediate Track
12-24 Weeks
Deep Learning & Advanced ML
Master neural networks, transformers, and production ML systems. Build real-world applications in NLP, computer vision, and MLOps.
12-24 weeks
68 lessons
8 projects
Prerequisites
Complete the Beginner Track or have equivalent knowledge of Python, NumPy, Pandas, and basic ML algorithms (linear regression, decision trees).
→ Review Beginner TrackCourses
6 weeks
Deep Learning
Neural networks, CNNs, RNNs, and Transformers
4 weeks
Natural Language Processing
Text processing, embeddings, and language models
4 weeks
Computer Vision
Image classification, detection, and segmentation
Weekly Curriculum
- Perceptrons and Multi-layer Networks
- Backpropagation Deep Dive
- Activation Functions Compared
- Weight Initialization Strategies
- Lab: Build Neural Network from Scratch
- Convolution Operations
- Pooling and Stride
- Classic Architectures (LeNet, AlexNet, VGG)
- Modern Architectures (ResNet, Inception, EfficientNet)
- Lab: Image Classification with PyTorch
Week 5-6
Sequence Models
5 lessons
- Recurrent Neural Networks
- LSTM and GRU
- Bidirectional RNNs
- Sequence-to-Sequence Models
- Lab: Text Generation with LSTMs
- Attention Mechanism
- Self-Attention and Multi-Head Attention
- Transformer Architecture
- BERT and GPT
- Lab: Fine-tune BERT for Classification
Week 9-10
Advanced NLP
5 lessons
- Transfer Learning in NLP
- Named Entity Recognition
- Question Answering Systems
- Text Summarization
- Lab: Build a QA System
Week 11-12
Computer Vision Advanced
5 lessons
- Object Detection (YOLO, Faster R-CNN)
- Semantic Segmentation (U-Net, Mask R-CNN)
- Generative Models (VAE, GAN)
- Vision Transformers
- Capstone: End-to-End CV Project
Capstone Projects
🖼️
Image Classifier
CNN-based classification
💬
Chatbot
Transformer-based NLP
📦
Object Detector
Real-time YOLO
🔧
ML Pipeline
End-to-end MLOps