What is Data Science? Role in Modern Business

Data Science vs. Data Analytics vs. Data Engineering

The Data Science Pipeline: Collection to Deployment

Career Paths in Data Science and AI

Structured, Semi-structured, and Unstructured Data

Data Sources: Databases, APIs, Web Scraping, IoT Sensors

Big Data Concepts: Volume, Velocity, Variety, Veracity

Data Quality and Governance

Relational Databases (PostgreSQL, MySQL)

NoSQL Databases (MongoDB, Cassandra)

Data Warehouses vs. Data Lakes

Cloud Storage Solutions (S3, Azure Blob, GCS)

History and Evolution of AI

Types of AI: Narrow AI vs. AGI

Machine Learning vs. Deep Learning vs. Generative AI

Current State and Future of AI

Supervised Learning: When and Why

Unsupervised Learning: Discovery and Patterns

Reinforcement Learning: Learning from Interaction

Semi-supervised and Self-supervised Learning

Biological Inspiration

Perceptron and Multi-layer Networks

Why Deep Learning Works

Applications Across Industries

Evolution from Traditional AI to GenAI

Large Language Models: GPT-4, Claude, Gemini, LLaMA

Image Generation: DALL-E, Stable Diffusion, MidJourney

Multimodal Models: Vision-Language Models

Content Creation and Marketing

Code Generation and Software Development

Customer Service and Support

Research and Analysis Automation

Healthcare and Drug Discovery

What Makes an AI System an “Agent”?

Agent Components: Perception, Planning, Action

Single vs. Multi-Agent Systems

Autonomous Decision Making

Customer Service Automation

Trading and Financial Agents

Research Assistants

Software Development Agents

Personal AI Assistants

CPU vs. GPU vs. TPU for AI Workloads

Memory Requirements for Large Models

Distributed Computing Concepts

Edge Computing for AI

CUDA Programming Basics

GPU Memory Management

Parallel Processing for Deep Learning

Cloud GPU Options (A100, V100, T4)

SageMaker for ML Development

EC2 for Compute

S3 for Data Storage

Bedrock for GenAI

Azure Machine Learning Studio

Azure Databricks

Cognitive Services

Azure OpenAI Service

Vertex AI Platform

BigQuery for Analytics

Cloud TPUs

AutoML Solutions

Python Environment Management (conda, venv)

Jupyter Lab and Notebooks

Cursor Setup

Git for Version Control

Google Colab Pro

Kaggle Kernels

AWS SageMaker Studio

Set up a complete data science development environment

Matrix Multiplication and Properties

Eigenvalues and Eigenvectors

Singular Value Decomposition (SVD)

Matrix Factorization Techniques

Principal Component Analysis (PCA)

Linear Discriminant Analysis (LDA)

PageRank Algorithm

Recommendation Systems

Gradients and Partial Derivatives

Chain Rule for Backpropagation

Jacobian and Hessian Matrices

Taylor Series Approximation

Gradient Descent Variants

Newton’s Method

Conjugate Gradient

Stochastic Optimization

Discrete Distributions (Binomial, Poisson, Multinomial)

Continuous Distributions (Normal, Exponential, Beta)

Joint and Conditional Distributions

Bayesian Probability

Maximum Likelihood Estimation

Bayesian Inference

Markov Chain Monte Carlo (MCMC)

Variational Inference

Parametric Tests (t-test, ANOVA, Chi-square)

Non-parametric Tests (Mann-Whitney, Kruskal-Wallis)

Multiple Testing Correction

Power Analysis

Time Series Analysis (ARIMA, SARIMA)

Survival Analysis

Causal Inference

Bayesian Statistics

Univariate Analysis

Bivariate Analysis

Multivariate Analysis

Automated EDA Tools (Pandas Profiling, Sweetviz)

Correlation Analysis

Distribution Analysis

Anomaly Detection

Trend Analysis

Geospatial Visualizations

Network Graphs

Sankey Diagrams

Tree Maps and Sunburst Charts

Interactive Exploration

Drill-down Analysis

Real-time Visualizations

Storytelling with Data

Permutation Importance

SHAP Values

LIME Explanations

Partial Dependence Plots

Problem Formulation

Data Collection and Preparation

Model Selection and Training

Evaluation and Validation

Deployment and Monitoring

Cross-Validation Techniques

Hyperparameter Tuning (Grid Search, Random Search, Bayesian Optimization)

AutoML Tools (AutoGluon, H2O.ai, TPOT)

Ensemble Strategies

Logistic Regression with Regularization

Support Vector Machines (Linear and Kernel)

Linear Discriminant Analysis

Decision Trees (CART, C4.5, ID3)

Random Forests

Gradient Boosting (XGBoost, LightGBM, CatBoost)

Extra Trees

Multi-class and Multi-label Classification

Imbalanced Learning (SMOTE, ADASYN)

Cost-Sensitive Learning

One-Class Classification

Ordinary Least Squares

Ridge, Lasso, and Elastic Net

Polynomial Regression

Quantile Regression

Support Vector Regression

Gaussian Process Regression

Neural Network Regression

Isotonic Regression

Partitioning Methods (K-Means, K-Medoids)

Hierarchical Clustering

Density-Based (DBSCAN, OPTICS, HDBSCAN)

Model-Based (Gaussian Mixture Models)

Linear Methods (PCA, ICA, Factor Analysis)

Non-linear Methods (t-SNE, UMAP, Isomap)

Autoencoders for Dimensionality Reduction

Feature Selection vs. Extraction

Label Propagation

Self-Training

Co-Training

Graph-Based Methods

Statistical Methods

Isolation Forest

Local Outlier Factor

One-Class SVM

Deep Learning for Anomaly Detection

Feedforward Networks

Activation Functions (ReLU, GELU, Swish)

Weight Initialization Strategies

Batch Normalization and Layer Normalization

Backpropagation Algorithm

Optimizers (SGD, Adam, AdamW, RMSprop)

Learning Rate Scheduling

Gradient Clipping and Regularization

Classic Architectures (LeNet, AlexNet, VGG)

Modern Architectures (ResNet, DenseNet, EfficientNet)

Vision Transformers (ViT)

Object Detection (YOLO, R-CNN family)

Image Classification

Semantic Segmentation

Instance Segmentation

Face Recognition Systems

Medical Image Analysis

Vanilla RNN

LSTM Networks

GRU Networks

Bidirectional RNNs

Time Series Forecasting

Natural Language Processing

Speech Recognition

Video Analysis

Self-Attention

Cross-Attention

Multi-Head Attention

Positional Encoding

Variational Autoencoders (VAE)

Generative Adversarial Networks (GANs)

Normalizing Flows

Diffusion Models

Dynamic Computation Graphs

Custom Datasets and DataLoaders

Distributed Training

Model Optimization

Model Subclassing

Custom Layers and Losses

TensorFlow Serving

TensorFlow Lite for Mobile

Implement a state-of-the-art deep learning model from research paper

Tokenization Strategies

Stemming and Lemmatization

Named Entity Recognition

Part-of-Speech Tagging

Bag of Words and TF-IDF

N-grams

Word Embeddings (Word2Vec, GloVe, FastText)

RNNs for Text Classification

Seq2Seq Models

Attention in NLP

Transformer Architecture

BERT and Variants (RoBERTa, ALBERT, ELECTRA)

GPT Family Evolution

T5 and BART

Domain-Specific Models (BioBERT, FinBERT)

Sentiment Analysis

Text Classification

Question Answering

Text Summarization

Machine Translation

Self-Attention Mechanism Mathematics

Multi-Head Attention Implementation

Position Encoding Strategies

Feed-Forward Networks

Residual Connections and Layer Normalization

Encoder-Only (BERT Family)

Decoder-Only (GPT Family)

Encoder-Decoder (T5, BART)

Efficient Transformers (Linformer, Performer, Reformer)

Causal Language Modeling

Masked Language Modeling

Denoising Objectives

Contrastive Learning

GPT-4 Architecture Insights

Claude and Constitutional AI

Gemini and Multimodal Training

Open Source Models (LLaMA, Mistral, Falcon)

Compute-Optimal Training

Chinchilla Scaling

Mixture of Experts

Sparse Models

Full Fine-tuning

Parameter-Efficient Fine-tuning (PEFT)

LoRA and QLoRA

Prefix Tuning and Prompt Tuning

Adapter Layers

Dataset Preparation

Instruction Following

RLHF (Reinforcement Learning from Human Feedback)

DPO (Direct Preference Optimization)

Quantization (INT8, INT4, GPTQ)

Model Pruning

Knowledge Distillation

Flash Attention

KV Cache Optimization

Model Serving (vLLM, TGI)

Batch Processing

Streaming Generation

Edge Deployment

Perplexity and Loss Metrics

Benchmark Suites (MMLU, HellaSwag, TruthfulQA)

Human Evaluation

Task-Specific Metrics

Prompt Injection Prevention

Content Filtering

Bias Detection and Mitigation

Hallucination Reduction

Zero-shot Prompting

Few-shot Learning

Chain-of-Thought Prompting

Self-Consistency

Tree-of-Thoughts

ReAct (Reasoning + Acting)

Constitutional AI Prompting

Meta-Prompting

Automatic Prompt Optimization

Chains and Sequential Processing

Agents and Tools

Memory Systems

Document Loaders and Splitters

Output Parsers

Custom Chains

Multi-Agent Systems

Callbacks and Streaming

LangGraph for Stateful Applications

Document Processing

Metadata Extraction

Chunking Strategies

Index Structures

Vector Store Index

List Index

Tree Index

Knowledge Graph Index

Build a production LLM application with LangChain

Document Processing Pipeline

Embedding Models Selection

Vector Database Design

Retrieval Strategies

Context Integration

Sentence Transformers

OpenAI Embeddings

Cohere Embeddings

Custom Embedding Training

Pinecone: Managed Vector Database

Weaviate: Open-Source with Hybrid Search

Chroma: Lightweight and Developer-Friendly

Qdrant: High-Performance Vector Search

FAISS: Facebook’s Similarity Search

Hybrid Search (Dense + Sparse)

Metadata Filtering

Namespace Management

Index Optimization

Query Expansion

Re-ranking Strategies

Semantic Caching

Recursive Retrieval

Multi-Query Retrieval

Conversational RAG

Multi-Modal RAG

Graph RAG

Agentic RAG

Self-RAG

Retrieval Metrics (Precision, Recall, MRR)

Generation Quality (BLEU, ROUGE, BERTScore)

End-to-End Evaluation

Human Evaluation Frameworks

Build a production-ready RAG system for enterprise knowledge management

Perception Module: Understanding Environment

Memory Systems: Short-term and Long-term

Planning Module: Goal Setting and Task Decomposition

Action Module: Execution and Tool Use

Learning Module: Adaptation and Improvement

Reactive Agents

Deliberative Agents

Hybrid Agents

Learning Agents

Collaborative Agents

Agent Definition and Roles

Task Assignment and Management

Crew Composition

Inter-Agent Communication

Workflow Orchestration

Conversational Agents

Code Execution Capabilities

Human-in-the-Loop Design

Multi-Agent Conversations

Agent Customization

Graph-Based Agent Design

State Management

Conditional Routing

Checkpointing and Persistence

Parallel Execution

Goal-Oriented Planning

Hierarchical Task Networks

Means-Ends Analysis

Backward Chaining

Monte Carlo Tree Search

ReAct (Reasoning + Acting)

Chain-of-Thought Agents

Self-Reflection and Critique

Multi-Step Reasoning

Causal Reasoning

Web Search and Browsing

Database Access

API Integration

Code Execution

File System Operations

Tool Selection Strategies

Error Handling and Recovery

Tool Chaining

Custom Tool Development

Security Considerations

Working Memory

Episodic Memory

Semantic Memory

Procedural Memory

Storage Strategies

Retrieval Mechanisms

Memory Consolidation

Forgetting Mechanisms

Memory-Augmented Generation

Build an autonomous agent for complex task automation

Centralized vs. Decentralized

Hierarchical Organizations

Market-Based Systems

Team-Based Structures

Message Passing

Blackboard Systems

Contract Net Protocol

Publish-Subscribe

Task Allocation

Resource Sharing

Conflict Resolution

Consensus Building

Cooperative Problem Solving

Competitive Agents

Negotiation Protocols

Coalition Formation

Customer Service Systems

Supply Chain Management

Financial Trading Systems

HR Automation

Code Review Agents

Testing Automation

Documentation Generation

DevOps Automation

Task Completion Rate

Efficiency Measures

Collaboration Effectiveness

Adaptability Score

Design a multi-agent system for enterprise automation

Code Versioning with Git

Data Versioning with DVC

Model Versioning

Experiment Tracking with MLflow

Automated Testing

Model Validation

Continuous Training

Progressive Deployment

Model Cataloging

Metadata Management

Model Lineage

Approval Workflows

REST APIs with FastAPI

gRPC Services

Batch Inference

Real-time Streaming

Performance Degradation

Data Drift Detection

Concept Drift

Prediction Drift

Resource Utilization

Latency Tracking

Error Rates

Throughput Metrics

AWS SageMaker Endpoints

Azure ML Managed Endpoints

Google Vertex AI Prediction

Kubernetes Deployment

Horizontal Scaling

Vertical Scaling

Auto-scaling Policies

Load Balancing

Model Optimization

Caching Strategies

Rate Limiting

Cost Management

Index Management

Update Strategies

Performance Optimization

Quality Assurance

Deploy a complete AI system with monitoring and CI/CD