Topics:

• 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

• 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

• Generative AI Revolution

  Evolution from Traditional AI to GenAI

  Large Language Models Overview

  Image Generation Technologies

  Business Applications of GenAI

  Ethical Considerations in AI

Topics:

• Advanced Python for Data Science

  Python Fundamentals Review

  Object-Oriented Programming

  Functional Programming Concepts

  Error Handling and Debugging

  Performance Optimization

• Data Structures and Algorithms

  Advanced Data Structures

  Algorithm Design and Analysis

  Time and Space Complexity

  Dynamic Programming

  Problem-Solving Strategies

Topics:

• NumPy for Numerical Computing

  Array Operations and Broadcasting

  Linear Algebra Operations

  Performance Optimization

  Vectorization Techniques

• Pandas for Data Analysis

  DataFrames and Series

  Data Cleaning and Transformation

  GroupBy Operations

  Time Series Analysis

  Large Dataset Handling

Topics:

• Statistical Visualizations

  Matplotlib Advanced Plotting

  Seaborn for Statistical Graphics

  Plotly for Interactive Visualizations

  Altair for Declarative Visualization

• Power BI for Business Intelligence

  Power BI Desktop and Service

  Data Modeling and DAX

  Interactive Dashboard Creation

  Integration with Python/R

  Publishing and Collaboration

• Dashboard Development

  Streamlit for Data Apps

  Plotly Dash Applications

  Panel for Complex Applications

  Best Practices in Data Visualization

Topics:

• Hardware for AI/ML

  CPU vs GPU vs TPU

  GPU Computing with CUDA

  Distributed Computing

  Edge Computing for AI

• Cloud Platforms

  AWS for Data Science (SageMaker, S3, EC2)

  Azure ML and Services

  Google Cloud Platform (Vertex AI)

  Cloud Cost Optimization

• Development Environments

  Local Development Setup

  Jupyter Ecosystem

  Version Control with Git

  Containerization with Docker

Topics:

• Matrix Operations

  Matrix Multiplication and Properties

  Eigenvalues and Eigenvectors

  Singular Value Decomposition

  Matrix Factorization Techniques

• Applications in ML

  Principal Component Analysis (PCA)

  Linear Discriminant Analysis

  PageRank Algorithm

  Recommendation Systems

Topics:

• Differential Calculus

  Gradients and Partial Derivatives

  Chain Rule for Backpropagation

  Jacobian and Hessian Matrices

  Taylor Series Approximation

• Optimization Algorithms

  Gradient Descent Variants

  Newton’s Method

  Conjugate Gradient

  Stochastic Optimization

  Constrained Optimization

Topics:

• Probability Theory

  Probability Distributions

  Joint and Conditional Distributions

  Bayesian Probability

  Markov Chains

• Statistical Inference

  Maximum Likelihood Estimation

  Bayesian Inference

  Hypothesis Testing

  Confidence Intervals

  MCMC Methods

Topics:

• Hypothesis Testing

  Parametric Tests

  Non-parametric Tests

  Multiple Testing Correction

  Power Analysis

• Advanced Statistical Methods

  Time Series Analysis (ARIMA, SARIMA)

  Survival Analysis

  Causal Inference

  Bayesian Statistics

Topics:

• EDA Methodology

  Data Profiling

  Pattern Discovery

  Correlation Analysis

  Anomaly Detection

• Feature Engineering

  Feature Creation

  Feature Selection

  Feature Transformation

  Dimensionality Reduction

  Feature Importance Analysis

Topics:

• ML Pipeline Development

  Problem Formulation

  Data Preparation

  Model Selection

  Evaluation Strategies

  Deployment Planning

• Model Selection & Validation

  Cross-Validation Techniques

  Hyperparameter Tuning

  Ensemble Methods

  AutoML Tools

Topics:

• Classification Algorithms

  Logistic Regression

  Support Vector Machines

  Decision Trees & Random Forests

  Gradient Boosting (XGBoost, LightGBM)

  Neural Networks for Classification

• Regression Analysis

  Linear Regression Models

  Polynomial Regression

  Ridge, Lasso, Elastic Net

  Non-linear Regression Models

  Time Series Forecasting

Topics:

• Clustering Algorithms

  K-Means and Hierarchical Clustering

  DBSCAN and Density-Based Methods

  Gaussian Mixture Models

  Spectral Clustering

• Dimensionality Reduction

  PCA and ICA

  t-SNE and UMAP

  Autoencoders

  Feature Selection Methods

Topics:

• Neural Network Architecture

  Feedforward Networks

  Activation Functions

  Backpropagation Algorithm

  Optimizers and Learning Rate Scheduling

• Training Deep Networks

  Weight Initialization

  Batch Normalization

  Dropout and Regularization

  Transfer Learning

Topics:

• Convolutional Neural Networks

  CNN Architectures (ResNet, EfficientNet)

  Image Classification

  Object Detection (YOLO, R-CNN)

  Semantic Segmentation

• Recurrent Networks & Transformers

  LSTM and GRU Networks

  Attention Mechanisms

  Transformer Architecture

  Vision Transformers

Topics:

• Text Preprocessing

  Tokenization Strategies

  Stemming and Lemmatization

  Named Entity Recognition

  Part-of-Speech Tagging

• Feature Extraction

  Bag of Words and TF-IDF

  Word Embeddings (Word2Vec, GloVe)

  N-grams

  Topic Modeling

Topics:

• Sequence Models

  RNNs for Text Classification

  Seq2Seq Models

  Attention in NLP

  Bidirectional Models

• Pre-trained Language Models

  BERT and Variants

  GPT Family Evolution

  T5 and BART

  Domain-Specific Models

Topics:

• LLM Fundamentals

  Pre-training Strategies

  Model Architectures (GPT-4, Claude, Gemini)

  Scaling Laws

  Mixture of Experts

• Fine-tuning & Adaptation

  Full Fine-tuning

  Parameter-Efficient Methods (LoRA, QLoRA)

  Instruction Tuning

  RLHF and DPO

Topics:

• Prompt Engineering Mastery

  Zero-shot and Few-shot Learning

  Chain-of-Thought Prompting

  Advanced Prompting Strategies

  Prompt Optimization

• LLM Application Development

  LangChain Framework

  LlamaIndex for Knowledge Management

  Memory Systems

  Agent Development with LLMs

Topics:

• RAG Architecture

  Document Processing Pipeline

  Embedding Models

  Vector Database Design

  Retrieval Strategies

• Advanced RAG Techniques

  Hybrid Search

  Query Optimization

  Multi-Modal RAG

  RAG Evaluation Metrics

  Production RAG Systems

Topics:

• Generative Model Foundations

  Variational Autoencoders (VAE)

  Generative Adversarial Networks (GANs)

  Normalizing Flows

  Energy-Based Models

• Diffusion Models

  DDPM and DDIM

  Stable Diffusion Architecture

  Controlling Generation

  Model Training Strategies

Topics:

• Text-to-Image Models

  DALL-E Architecture

  Stable Diffusion Applications

  ControlNet and LoRA

  Image Editing Techniques

• Advanced Image AI

  Image-to-Image Translation

  Style Transfer

  Super Resolution

  Inpainting and Outpainting

Topics:

• Vision-Language Models

  CLIP and Variants

  BLIP and Flamingo

  LLaVA Architecture

  Visual Question Answering

• Cross-Modal Applications

  Image Captioning

  Visual Reasoning

  Multimodal Search

  Document Understanding

Topics:

• Speech Processing

  Whisper for Transcription

  Text-to-Speech Models

  Voice Cloning

  Speech Translation

• Audio Generation

  Music Generation Models

  Sound Effect Generation

  Audio Enhancement

  Real-time Processing

Topics:

• Code Generation Models

  Codex and GitHub Copilot

  Code Llama

  StarCoder

  Code Review and Testing

• Domain-Specific Applications

  Medical AI

  Financial AI

  Scientific Computing

  Creative AI Applications

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

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

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

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

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

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.

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.

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.

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.