Path Overview

Master the industry-standard tools and frameworks used in production AI/ML systems. This path focuses on practical application of TensorFlow, PyTorch, Scikit-learn, and Hugging Face.

Phase 1: TensorFlow & Keras Deep Dive (Weeks 1-2)

Module 1.1: TensorFlow Fundamentals

• TensorFlow architecture and computation graphs
• Tensors and tensor operations
• Eager execution vs graph mode
• tf.function for optimization
• Debugging and profiling TensorFlow code

Module 1.2: Advanced Keras

• Custom layers and models
• Custom training loops
• Callbacks for training control
• Model saving and loading
• Distributed training basics

Phase 2: PyTorch for Research & Production (Weeks 3-4)

Module 2.1: PyTorch Fundamentals

• PyTorch tensors and autograd
• Building models with nn.Module
• Training loops and loss functions
• Optimization with torch.optim
• PyTorch vs TensorFlow comparison

Module 2.2: Advanced PyTorch

• Custom layers and networks
• DataLoaders and data pipelines
• GPU training and mixed precision
• Model checkpointing and resuming
• Using torchvision for computer vision

Phase 3: Scikit-learn for Traditional ML (Week 5)

Topics:

• Machine learning fundamentals
• Classification models (SVM, Random Forest)
• Regression models (Linear, Ridge, Lasso)
• Clustering (K-means, hierarchical)
• Model evaluation and cross-validation
• Hyperparameter tuning (Grid Search, Random Search)
• Feature engineering and scaling

Phase 4: Hugging Face Transformers (Weeks 6-7)

Module 4.1: Getting Started with Transformers

• What are transformers and why they matter
• Using pre-trained models from Hugging Face
• Common NLP tasks (classification, summarization)
• Fine-tuning models for specific tasks
• Tokenization and preprocessing

Module 4.2: Building with Transformers

• The Transformers library architecture
• Using different model types (BERT, GPT, T5)
• Training custom models
• Deploying models for inference
• Working with datasets library

Phase 5: Production & Deployment (Week 8)

Topics:

• Model serialization and formats (SavedModel, ONNX)
• Serving models (TensorFlow Serving, TorchServe)
• Docker containerization
• Cloud deployment (AWS, GCP, Azure)
• Model monitoring and versioning
• Performance optimization (quantization, pruning)

Hands-On Projects

Tool Comparison & When to Use

TensorFlow

• Best for: Production ML systems, large-scale training
• Strength: Mature ecosystem, deployment tools
• Learning curve: Moderate

PyTorch

• Best for: Research, experimentation
• Strength: Intuitive API, easy debugging
• Learning curve: Easier than TensorFlow

Scikit-learn

• Best for: Traditional ML, quick prototyping
• Strength: Simple, consistent API
• Learning curve: Easiest

Hugging Face

• Best for: NLP tasks with transformers
• Strength: Pre-trained models, huge community
• Learning curve: Easy with good documentation

Resources

• TensorFlow Tutorials
• PyTorch Official Tutorials
• Scikit-learn Documentation
• Hugging Face Course

After This Path

• Specialize deep in your preferred framework
• Learn about deployment and MLOps
• Explore domain-specific applications
• Transition to industry roles (ML Engineer, Data Scientist)