AI - Academy of Tech Masters

Artificial Intelligence

AI Training equips learners with the skills to design, build, and deploy intelligent systems using machine learning, deep learning, and data-driven techniques, enabling automation, advanced decision-making, and innovative solutions across diverse domains.

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Artificial Intelligence Course Curriculum

It stretches your mind, think better and create even better.

Artificial Intelligence

Module 1: Data Science & AI Fundamentals

Topics:

1.1 Introduction to Data Science

The Data Science Ecosystem

Data Science vs Analytics vs Engineering

Career Paths in Data Science and AI

Types of Data and Data Sources

Data Storage and Management Systems

1.2 Artificial Intelligence Foundations

Understanding AI: History and Evolution

Types of AI: Narrow AI vs AGI

Machine Learning Paradigms

Introduction to Neural Networks

AI Applications Across Industries

1.3 Generative AI Revolution

Evolution from Traditional AI to GenAI

Large Language Models Overview

Image Generation Technologies

Business Applications of GenAI

Ethical Considerations in AI

Module 2: Python Programming Mastery

Topics:

2.1 Advanced Python for Data Science

Python Fundamentals Review

Object-Oriented Programming

Functional Programming Concepts

Error Handling and Debugging

Performance Optimization

2.2 Data Structures and Algorithms

Advanced Data Structures

Algorithm Design and Analysis

Time and Space Complexity

Dynamic Programming

Problem-Solving Strategies

Module 3: Data Manipulation & Analysis

Topics:

3.1 NumPy for Numerical Computing

Array Operations and Broadcasting

Linear Algebra Operations

Performance Optimization

Vectorization Techniques

3.2 Pandas for Data Analysis

DataFrames and Series

Data Cleaning and Transformation

GroupBy Operations

Time Series Analysis

Large Dataset Handling

Module 4: Data Visualization & Storytelling

Topics:

4.1 Statistical Visualizations

Matplotlib Advanced Plotting

Seaborn for Statistical Graphics

Plotly for Interactive Visualizations

Altair for Declarative Visualization

4.2 Power BI for Business Intelligence

Power BI Desktop and Service

Data Modeling and DAX

Interactive Dashboard Creation

Integration with Python/R

Publishing and Collaboration

4.3 Dashboard Development

Streamlit for Data Apps
Plotly Dash Applications
Panel for Complex Applications
Best Practices in Data Visualization

Module 5: Computing Infrastructure

Topics:

5.1 Hardware for AI/ML

CPU vs GPU vs TPU

GPU Computing with CUDA

Distributed Computing

Edge Computing for AI

5.2 Cloud Platforms

AWS for Data Science (SageMaker, S3, EC2)
Azure ML and Services
Google Cloud Platform (Vertex AI)
Cloud Cost Optimization

MATHEMATICS & STATISTICS For AI

Module 6: Linear Algebra for ML

Topics:

6.1Matrix Operations

Matrix Multiplication and Properties

Eigenvalues and Eigenvectors

Singular Value Decomposition

Matrix Factorization Techniques

6.2 Applications in ML

Principal Component Analysis (PCA)

Linear Discriminant Analysis

PageRank Algorithm

Recommendation Systems

Module 7: Calculus & Optimization

Topics:

7.1 Differential Calculus

Gradients and Partial Derivatives
Chain Rule for Backpropagation
Jacobian and Hessian Matrices
Taylor Series Approximation

7.2 Optimization Algorithms

Gradient Descent Variants
Newton’s Method
Conjugate Gradient
Stochastic Optimization
Constrained Optimization

Module 8: Probability & Statistics

Topics:

8.1 Probability Theory

Probability Distributions

Joint and Conditional Distributions

Bayesian Probability

Markov Chains

8.2 Statistical Inference

Maximum Likelihood Estimation

Bayesian Inference

Hypothesis Testing

Confidence Intervals

MCMC Methods

Module 9: Statistical Analysis

Topics:

9.1 Advanced Storage Features

Storage Account Security (SAS, RBAC, Firewall)
Lifecycle Management Policies
Azure File Sync Implementation
Storage Migration Service
StorSimple and Data Box

9.2 Data Protection and Compliance

Encryption at Rest and in Transit
Customer-Managed Keys (CMK)
Immutable Storage
Legal Hold and Retention Policies
GDPR and Compliance Features

Module 10: Exploratory Data Analysis

Topics:

10.1 EDA Methodology

Data Profiling
Pattern Discovery
Correlation Analysis
Anomaly Detection

10.2 Feature Engineering

Feature Creation

Feature Selection

Feature Transformation

Dimensionality Reduction

Feature Importance Analysis

MACHINE LEARNING MASTERY

Module 11: ML Fundamentals

Topics:

11.1 ML Pipeline Development

Problem Formulation

Data Preparation

Model Selection

Evaluation Strategies

Deployment Planning

11.2 Model Selection & Validation

Cross-Validation Techniques

Hyperparameter Tuning

Ensemble Methods

AutoML Tools

Module 12: Supervised Learning

12.1 Classification Algorithms

Logistic Regression

Support Vector Machines

Decision Trees & Random Forests

Gradient Boosting (XGBoost, LightGBM)

Neural Networks for Classification

12.2 Regression Analysis

Linear Regression Models

Polynomial Regression

Ridge, Lasso, Elastic Net

Non-linear Regression Models

Time Series Forecasting

Module 13: Unsupervised Learning

Topics:

13.1 Clustering Algorithms

K-Means and Hierarchical Clustering

DBSCAN and Density-Based Methods

Gaussian Mixture Models

Spectral Clustering

13.2 Dimensionality Reduction

PCA and ICA

t-SNE and UMAP

Autoencoders

Feature Selection Methods

Module 14: Deep Learning Foundations

Topics:

14.1 Neural Network Architecture

Feedforward Networks

Activation Functions

Backpropagation Algorithm

Optimizers and Learning Rate Scheduling

14.2 Training Deep Networks

Weight Initialization

Batch Normalization

Dropout and Regularization

Transfer Learning

Module 15: Advanced Deep Learning

Topics:

15.1 Convolutional Neural Networks

CNN Architectures (ResNet, EfficientNet)

Image Classification

Object Detection (YOLO, R-CNN)

Semantic Segmentation

15.2 Recurrent Networks & Transformers

LSTM and GRU Networks

Attention Mechanisms

Transformer Architecture

Vision Transformers

NATURAL LANGUAGE PROCESSING

Module 16: Classical NLP

16.1 Text Preprocessing

Tokenization Strategies

Stemming and Lemmatization

Named Entity Recognition

Part-of-Speech Tagging

16.2 Feature Extraction

Bag of Words and TF-IDF

Word Embeddings (Word2Vec, GloVe)

N-grams

Topic Modeling

Module 17: Deep Learning for NLP

17.1 Sequence Models

RNNs for Text Classification

Seq2Seq Models

Attention in NLP

Bidirectional Models

17.2 Pre-trained Language Models

BERT and Variants

GPT Family Evolution

T5 and BART

Domain-Specific Models

Module 18: Large Language Models

Topics:

18.1 LLM Fundamentals

Pre-training Strategies

Model Architectures (GPT-4, Claude, Gemini)

Scaling Laws

Mixture of Experts

18.2 Fine-tuning & Adaptation

Full Fine-tuning

Parameter-Efficient Methods (LoRA, QLoRA)

Instruction Tuning

RLHF and DPO

Module 19: Prompt Engineering & Applications

Topics:

19.1 Prompt Engineering Mastery

Zero-shot and Few-shot Learning

Chain-of-Thought Prompting

Advanced Prompting Strategies

Prompt Optimization

19.2 LLM Application Development

LangChain Framework

LlamaIndex for Knowledge Management

Memory Systems

Agent Development with LLMs

Module 20: Retrieval-Augmented Generation

Topics:

20.1 RAG Architecture

Document Processing Pipeline

Embedding Models

Vector Database Design

Retrieval Strategies

20.2 Advanced RAG Techniques

Hybrid Search

Query Optimization

Multi-Modal RAG

RAG Evaluation Metrics

Production RAG Systems

GENERATIVE AI & MULTIMODAL

Module 21: Generative Models

Topics:

21.1 Generative Model Foundations

Variational Autoencoders (VAE)
Generative Adversarial Networks (GANs)
Normalizing Flows
Energy-Based Models

21.2 Diffusion Models

DDPM and DDIM
Stable Diffusion Architecture
Controlling Generation
Model Training Strategies

Module 22: Image Generation

Topics:

22.1 Text-to-Image Models

DALL-E Architecture
Stable Diffusion Applications
ControlNet and LoRA
Image Editing Techniques

22.2 Advanced Image AI

Image-to-Image Translation
Style Transfer
Super Resolution
Inpainting and Outpainting

Module 23: Multimodal AI

Topics:

23.1 Vision-Language Models

CLIP and Variants
BLIP and Flamingo
LLaVA Architecture
Visual Question Answering

23.2 Cross-Modal Applications

Image Captioning
Visual Reasoning
Multimodal Search
Document Understanding

Module 24: Audio & Speech AI

Topics:

24.1 Speech Processing

Whisper for Transcription
Text-to-Speech Models
Voice Cloning
Speech Translation

24.2 Audio Generation

Music Generation Models
Sound Effect Generation
Audio Enhancement
Real-time Processing

Module 25: Code Generation & Specialized AI

Topics:

25.1 Code Generation Models

Codex and GitHub Copilot
Code Llama
StarCoder
Code Review and Testing

25.2 Domain-Specific Applications

Medical AI
Financial AI
Scientific Computing
Creative AI Applications

AI AGENTS & PRODUCTION

Module 26: AI Agent Fundamentals

Topics:

26.1 Agent Architecture

Perception and Planning Modules
Memory Systems
Action and Tool Use
Learning and Adaptation

26.2 Agent Frameworks

CrewAI Framework
AutoGen (Microsoft)
LangGraph
Custom Agent Development

Module 27: Advanced Agent Systems

Topics:

27.1 Agent Design Patterns

Planning Strategies
Reasoning Patterns
Tool Integration
Error Recovery

27.2 Multi-Agent Systems

Agent Communication
Coordination Strategies
Collaborative Problem Solving
Agent Evaluation Metrics

Module 28: MLOps Fundamentals

Topics:

28.1 MLOps Pipeline

Version Control for ML
Experiment Tracking
Model Registry
CI/CD for Machine Learning

28.2 Model Management

Model Versioning
A/B Testing
Progressive Deployment
Rollback Strategies

Module 29: Production Deployment

Topics:

29.1 Model Serving

REST APIs with FastAPI
gRPC Services
Batch Inference
Real-time Streaming

29.2 Cloud Deployment

Container Orchestration with Kubernetes
Serverless Deployment
Edge Deployment
Scaling Strategies

Module 30: Monitoring & Optimization

Topics:

30.1 Production Monitoring

Performance Metrics
Data Drift Detection
Model Degradation
Alert Systems

30.2 System Optimization

Model Optimization (Quantization, Pruning)
Caching Strategies
Load Balancing
Cost Optimization

Physical AI (Robotics)

Module 1: Introduction to Physical AI

Topics:

Definition

Physical AI, also known as Embodied AI, integrates AI with physical systems to enable machines to perceive, interpret, and act in real-world environments.

Core Components

Sensors: Devices like LiDAR, cameras, and temperature sensors for environmental data collection.

Actuators: Robotic arms, motors, and other mechanisms to execute physical actions.

AI Algorithms: For real-time decision-making and pattern recognition.

Embedded Systems: Enabling low-latency processing and interaction.

Module 2: Applications of Physical AI

Topics:

Healthcare

Robotic surgery, patient monitoring, and rehabilitation.

Manufacturing

Automation, quality control, and predictive maintenance.

Transportation

Autonomous vehicles and drones.

Service Industry

Customer service robots and automated delivery systems.

Module 3: Foundation of Physical AI from NVIDIA

Topics:

NVIDIA Cosmos Platform

Overview: NVIDIA Cosmos is a platform designed to accelerate the development of physical AI systems such as autonomous vehicles and robots.

World Foundation Models (WFM): State-of-the-art models trained on millions of hours of driving and robotics video data, available under an open model license.

Benefits of Using NVIDIA Cosmos

Accessibility: Open and easy access to high-performance models and data pipelines.

Efficiency: Out-of-the-box optimizations minimize total cost of ownership and accelerate time-to-market.

Safety: Inbuilt guardrails to filter unsafe content and harmful prompts.

Module 4:Physical AI Use Cases

Topics:

Autonomous Vehicles

Enhanced perception and decision-making capabilities.

Robotics

Improved interaction with complex and unpredictable environments.

Augmented Reality

Optimized video sequences for AR applications.

Module 30: Monitoring & Optimization

Topics:

30.1 Production Monitoring

Performance Metrics
Data Drift Detection
Model Degradation
Alert Systems

30.2 System Optimization

Model Optimization (Quantization, Pruning)
Caching Strategies
Load Balancing
Cost Optimization

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