Python with deep learning

Python Basics

Core Python syntax, indentation rules, comments and writing clean readable code for AI development.

Functions & Data Structures

Defining functions, working with lists, tuples, sets and dictionaries for data manipulation in AI workflows.

OOP Concepts

Classes, objects, inheritance, encapsulation and polymorphism applied to AI and deep learning codebases.

Python for AI Development

Setting up Python environment for AI, working with scientific libraries and writing production-ready Python code.

AI Fundamentals

Core concepts of Artificial Intelligence, types of AI systems and the history of intelligent machines.

AI Applications & Use Cases

Real-world AI applications across healthcare, finance, retail, robotics and natural language processing.

Machine Learning vs Deep Learning

Key differences between ML and DL, when to use each approach and how they relate to broader AI.

Intelligent Systems Overview

Understanding how intelligent systems perceive, reason and act using data-driven models.

Artificial Neural Networks (ANN)

Structure of ANNs, neurons, layers, weights and biases. Building and training basic neural networks.

Activation Functions

ReLU, sigmoid, tanh and softmax functions. Understanding their role in neural network learning.

Forward & Backpropagation

How data flows forward through a network and how gradients are computed during backpropagation.

Optimization Techniques

Gradient descent, SGD, Adam and RMSProp optimizers for minimizing loss during training.

TensorFlow Fundamentals

Introduction to TensorFlow, tensors, computation graphs and building models with the TensorFlow API.

Keras Model Development

Building sequential and functional models with Keras. Compiling, training and evaluating deep learning models.

PyTorch Fundamentals & Workflow

Introduction to PyTorch tensors, autograd, dynamic computation graphs and the PyTorch training loop.

Building Deep Learning Models

Designing multi-layer neural networks for classification and regression tasks using TensorFlow and PyTorch.

Neural Network Training Techniques

Batch training, learning rate scheduling, dropout regularization and early stopping strategies.

Model Validation & Performance Evaluation

Using validation sets, cross-validation and evaluation metrics to measure deep learning model performance.

Data Pipeline & Training Optimization

Building efficient data pipelines using TensorFlow Data API and PyTorch DataLoader for fast model training.

GPU Acceleration & Model Execution

Leveraging GPU hardware for accelerated deep learning training using CUDA and cloud GPU instances.

Computer Vision Fundamentals

Image representation, pixel manipulation, color spaces and basic image processing operations using OpenCV.

Convolutional Neural Networks

CNN architecture, convolutional layers, pooling, padding and building image recognition models.

Image Classification

Training CNN models to classify images using benchmark datasets like CIFAR-10 and ImageNet.

Feature Detection Techniques

Extracting meaningful features from images using filters, edge detection and feature maps in CNNs.

Object Detection Basics

Introduction to YOLO, SSD and Faster R-CNN for detecting and localizing objects in images.

Image Processing Workflows

End-to-end image preprocessing, augmentation and normalization pipelines for computer vision projects.

NLP Fundamentals

Introduction to Natural Language Processing, text representation and the NLP processing pipeline.

Text Processing Techniques

Tokenization, stemming, lemmatization, stop word removal and text vectorization using TF-IDF and word embeddings.

Sentiment Analysis Basics

Building sentiment classifiers to detect positive, negative and neutral opinions from text data.

Sequence Modeling

Using RNNs, LSTMs and GRUs to model sequential data and capture long-range dependencies in text.

Language Understanding Concepts

Transformer architecture, attention mechanisms and how models like BERT understand language context.

NLP Application Development

Building real-world NLP applications including chatbots, text summarizers and named entity recognizers.

Introduction to MCP Architecture

Overview of Model Context Protocol and how it standardizes communication between AI models and tools.

Model Context Protocol Fundamentals

Core MCP concepts including context passing, message formatting and protocol-level interactions.

Context Management Techniques

Managing conversation history, context windows and memory in MCP-based AI systems.

Tool Calling & Workflow Integration

Enabling AI models to call external tools, APIs and services through MCP tool definitions.

AI Communication Standards

Standards and protocols governing how AI systems exchange data, instructions and results.

Multi-Model Interaction Concepts

Orchestrating multiple AI models to collaborate on complex tasks using MCP communication layers.

Real-Time MCP Implementation

Building and testing real-time MCP-based workflows connecting LLMs with external data sources and tools.

Introduction to AI Agents

What AI agents are, how they perceive environments, make decisions and take autonomous actions.

Agent-to-Agent Communication Concepts

How multiple AI agents communicate, share context and coordinate to complete multi-step tasks.

Multi-Agent System Architecture

Designing systems where multiple specialized agents collaborate, delegate and interact to solve complex problems.

AI Workflow Orchestration

Using orchestration frameworks like LangGraph and AutoGen to manage multi-agent pipelines and task flows.

Autonomous Task Execution

Building agents that independently plan, execute and verify tasks without continuous human intervention.

Agent Collaboration Strategies

Patterns for agent coordination including supervisor, hierarchical and peer-to-peer collaboration models.

Real-Time A2A Applications

Deploying multi-agent systems for real-time use cases including research assistants and automated workflows.

Introduction to RAG Systems

What RAG is, how it combines retrieval with generation and why it improves LLM response accuracy.

Vector Databases & Embeddings

Storing and querying vector embeddings using Pinecone, Chroma and FAISS for semantic search.

Semantic Search Techniques

Building semantic search pipelines that retrieve contextually relevant documents using embedding similarity.

Context-Aware Response Generation

Injecting retrieved context into LLM prompts to generate accurate, grounded and relevant responses.

Knowledge Base Integration

Connecting RAG systems to internal knowledge bases, PDFs and documentation for domain-specific AI.

Document Retrieval Pipelines

Building end-to-end document ingestion, chunking, embedding and retrieval pipelines for RAG applications.

Real-Time RAG Application Development

Deploying production RAG systems with real-time document retrieval and LLM response generation.

Introduction to Fine-Tuning

What model fine-tuning is, when to use it over prompting and the trade-offs involved.

Working with Pretrained Models

Loading and adapting pretrained models from Hugging Face for custom downstream tasks.

Dataset Preparation Techniques

Collecting, cleaning, formatting and splitting datasets for supervised fine-tuning of language models.

Prompt Engineering Fundamentals

Designing effective prompts for zero-shot, few-shot and chain-of-thought reasoning with LLMs.

Hyperparameter Optimization

Tuning learning rate, batch size, epochs and weight decay for optimal fine-tuning performance.

Parameter-Efficient Fine-Tuning

Using LoRA, QLoRA and adapter methods to fine-tune large models efficiently with limited compute.

Fine-Tuning Real-Time AI Models

End-to-end fine-tuning workflow on real datasets using Hugging Face Trainer and PEFT library.

AI Model Evaluation Metrics

Accuracy, precision, recall, F1 score, BLEU, ROUGE and perplexity for evaluating AI model performance.

Accuracy & Performance Analysis

Analyzing model accuracy across different data splits and identifying performance bottlenecks.

Validation & Testing Techniques

Cross-validation, holdout testing and using benchmark datasets to validate AI model generalization.

Bias & Error Analysis

Detecting and mitigating bias in AI models. Analyzing error patterns and failure modes systematically.

Model Benchmarking

Comparing AI models against industry benchmarks and leaderboards to assess relative performance.

Optimization & Performance Tuning

Quantization, pruning and distillation techniques to improve model speed and reduce inference cost.

Real-Time Evaluation Workflows

Setting up automated evaluation pipelines that continuously monitor AI model performance in production.

AI Deployment Fundamentals

Overview of AI deployment lifecycle, from model export to serving predictions in production environments.

API-Based Model Integration

Wrapping AI models in REST APIs using FastAPI and Flask for integration with web and mobile applications.

Cloud Deployment Concepts

Deploying AI models on AWS, GCP and Azure using managed services like SageMaker and Vertex AI.

Scalable AI Infrastructure

Designing horizontally scalable AI serving infrastructure to handle high-traffic production workloads.

Containerization & Deployment Pipelines

Packaging AI models with Docker and automating deployment using CI/CD pipelines and Kubernetes.

Production Monitoring Techniques

Monitoring model drift, latency, error rates and data quality in live AI production systems.

Real-Time AI Application Deployment

End-to-end deployment of real-time AI applications including APIs, dashboards and streaming inference.

Hyperparameter Tuning

Grid search, random search and Bayesian optimization for finding optimal model hyperparameters.

Model Evaluation

Comprehensive evaluation of deep learning models using loss curves, confusion matrices and ROC analysis.

Performance Optimization

Techniques to speed up training and inference including mixed precision, caching and batching strategies.

Deployment Fundamentals

Exporting trained models, managing model versions and serving predictions via APIs and cloud platforms.

AI-Based Applications

Building complete AI-powered applications including image classifiers, chatbots and recommendation systems.

Industry-Oriented Projects

Working on real-world projects from healthcare, finance and retail domains using deep learning and LLMs.

Case Studies & Implementation

Analyzing and reimplementing published AI research and industry case studies end-to-end.

Practical Assessments

Hands-on assignments and capstone projects to demonstrate mastery of deep learning and AI deployment skills.