Deep Learning with Pytorch

6 months duration

9 modules

Updated Jul 30, 2026

Data & Analytics

Course Overview

Get to know what this course is all about and what you'll learn

Course Description

Master neural networks, CNNs, Transformers, and Large Language Models from scratch. A comprehensive 6 months program that takes you from Python basics to building production-ready AI systems with PyTorch.

What You'll Learn

This comprehensive program takes you from Classical Machine Learning through to cutting-edge Large Language Models. Starting with traditional ML (scikit-learn), you'll understand why deep learning exists before diving into neural networks with PyTorch. The curriculum spans 9 phases: Classical ML Foundations, Math & PyTorch Basics, Core Deep Learning Mechanics, PyTorch Abstractions, Core Architectures (CNNs, RNNs, Transformers), Practical Deployment, NLP, Generative Networks (VAEs, GANs, Diffusion), and LLMs.

By completion, you'll build and train ML models from scratch, implement computer vision and NLP solutions, fine-tune Large Language Models using LoRA and RAG, and deploy models to production.

Ideal for software developers, data analysts, and Python programmers entering AI. Weekend classes (online) suit working professionals. Prerequisites: Basic Python and high school math.

Course Curriculum

9 modules • Instructor-led • Hands-on projects

Course Modules

Module 1: Classical Machine Learning

This foundational module covers traditional machine learning before diving into deep learning. You'll master the complete ML workflow using scikit-learn: data preparation, model building, evaluation, and improvement. Topics include supervised vs unsupervised learning, regression models (Linear, Polynomial), classification models (Logistic Regression, Decision Trees, Random Forests, SVM), clustering (K-Means), and essential evaluation metrics (Accuracy, Precision, Recall, F1, ROC-AUC). By understanding classical ML first, you'll appreciate why and when deep learning is needed.

Understand the ML landscape: supervised vs unsupervised learning, regression vs classification
Prepare data properly: train/validation/test splits, cross-validation, feature scaling
Build and train regression models: Linear Regression, Polynomial Regression, Ridge, Lasso
Build and train classification models: Logistic Regression, Decision Trees, Random Forests, SVM
Evaluate models using appropriate metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC
Diagnose and fix overfitting/underfitting using regularization and hyperparameter tuning
Master the scikit-learn workflow: pipelines, preprocessing, and model persistence

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