Pymatrix Advanced Core Learning
(40 Hours Program)
- Duration: 40 Hours
- Format: Instructor-Led + Hands-on Coding
- Level: Intermediate to Advanced
- Outcome: AI & Deep Learning Developer
- Focus: Advanced Python, NumPy & PyTorch for AI Development
- Projects: 6+ Hands-on Labs & 1 Industry Capstone Project
- Tools Covered: Python, NumPy, PyTorch, CUDA, TensorBoard
- Certificate: Professional Certification with 16-Digit Verification ID
- Includes: Coding Exercises, Downloadable Resources, Source Code, Model Deployment & Career-Oriented Projects
Module-wise Curriculum (40 Hours)
Module 1: Advanced Python Programming (6 Hours)
- Advanced Object-Oriented Programming
- Iterators & Generators
- Decorators & Context Managers
- Lambda, map(), filter() & reduce()
- Dataclasses & Type Hinting
- Exception Handling & Logging
- Virtual Environments & Packaging
- Performance Optimization Techniques
- Writing Clean & Maintainable Code
- Hands-on Lab: Build a reusable Python utility package
Master modern Python programming for scalable AI development.
Module 2: NumPy Mastery (6 Hours)
- ndarray Internals & Memory Layout
- Data Types & Broadcasting
- Advanced Indexing & Slicing
- Vectorization & Universal Functions
- Matrix Operations & Linear Algebra
- Statistical Analysis
- Random Module & Simulations
- Memory & Performance Optimization
- Scientific Computing Best Practices
- Hands-on Lab: Build an image-processing toolkit
Perform high-performance numerical computing using NumPy.
Module 3: PyTorch Fundamentals (6 Hours)
- Tensor Operations & Manipulation
- CUDA & GPU Programming Basics
- Autograd & Computational Graphs
- Dataset & DataLoader APIs
- Loss Functions & Optimizers
- Training & Validation Loops
- Checkpoint Saving
- Model Evaluation Metrics
- Inference Pipeline
- Hands-on Lab: Train a handwritten digit classifier
Learn the core building blocks of modern Deep Learning using PyTorch.
Module 4: Deep Learning with PyTorch (8 Hours)
- Artificial Neural Networks (ANN)
- Convolutional Neural Networks (CNN)
- Transfer Learning
- Fine-tuning Pretrained Models
- Batch Normalization
- Dropout & Regularization
- Learning Rate Scheduling
- Early Stopping Techniques
- Hyperparameter Tuning
- Project: Build an Image Classification Model
Create accurate and optimized deep learning models for real-world AI applications.
Module 5: Modern PyTorch & Deployment (6 Hours)
- Custom Dataset Classes
- Custom Neural Network Modules
- Mixed Precision Training (AMP)
- Model Saving & Loading
- TorchScript
- TensorBoard Visualization
- Model Profiling & Benchmarking
- Inference Optimization
- Model Deployment Best Practices
- Hands-on Lab: Optimize & Export a Trained Model
Deploy AI models efficiently for production environments.
Module 6: AI Capstone Project (8 Hours)
- Complete End-to-End AI Application Development
- Data Collection & Preprocessing
- Model Development & Training
- Performance Evaluation
- Visualization of Results
- Inference Script Development
- Model Export & Deployment
- Technical Documentation
- GitHub Portfolio Preparation
- Final Project Presentation
Build a complete portfolio-ready AI application from scratch.
Suggested Capstone Projects
- Image Classification System
- Face Mask Detection
- Plant Disease Detection
- Handwritten Digit Recognition
- Sentiment Analysis using PyTorch
- Object Detection (YOLO Introduction)
- Medical Image Classification
- AI Recommendation System
- Custom Computer Vision Application
- Real-world Deep Learning Portfolio Project
Learning Outcomes
- Write advanced, maintainable and production-ready Python code.
- Master NumPy for scientific computing and numerical optimization.
- Build, train and evaluate deep learning models using PyTorch.
- Apply transfer learning and modern deep learning techniques.
- Optimize training using mixed precision, schedulers and profiling tools.
- Save, export and deploy PyTorch models for real-world applications.
- Develop an end-to-end AI solution suitable for professional portfolios.
- Gain industry-ready skills for AI Engineer, ML Engineer and Deep Learning Developer roles.
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