• Introduction to AI: History, Types, and Applications

• Machine Learning vs Deep Learning vs Traditional Programming

• AI Ethics: Bias, Fairness, and Responsible AI Development

• AI in Business: ROI, Implementation Strategies, and Use Cases

• Text Processing: Tokenization, Lemmatization, and Vectorization

• Language Models: From N-grams to Transformers

• Sentiment Analysis and Text Classification

• Building Chatbots and Conversational AI Systems

• Image Fundamentals: Pixels, Color Spaces, and Filters

• Convolutional Neural Networks (CNNs) for Image Recognition

• Object Detection: YOLO, R-CNN, and Real-time Applications

• Image Generation: GANs, Style Transfer, and Creative AI

• Model Deployment: APIs, Containers, and Cloud Platforms

• Model Monitoring: Performance Tracking and Drift Detection

• AI Pipeline Automation: CI/CD for Machine Learning

• Scalable AI Systems: Microservices and Distributed Computing

• Linear Regression: From Theory to Implementation

• Classification Algorithms: Logistic Regression, SVM, Decision Trees

• Model Evaluation: Cross-validation, Metrics, and Hyperparameter Tuning

• Feature Engineering: Selection, Scaling, and Transformation

• Ensemble Methods: Random Forests, Gradient Boosting, XGBoost

• Neural Networks: Perceptrons, Backpropagation, and Deep Learning

• Support Vector Machines: Kernel Methods and Advanced Applications

• Model Interpretability: SHAP, LIME, and Explainable AI

• Clustering: K-means, Hierarchical, and DBSCAN Algorithms

• Dimensionality Reduction: PCA, t-SNE, and Autoencoders

• Association Rules: Market Basket Analysis and Recommendation Systems

• Anomaly Detection: Isolation Forests and One-Class SVM

• RL Fundamentals: Agents, Environments, and Markov Decision Processes

• Q-Learning and Deep Q-Networks (DQN)

• Policy Gradient Methods: REINFORCE and Actor-Critic

• Multi-agent Systems and Game Theory Applications

• Database Design: ER Diagrams, Normalization, and Schema Design

• SQL Mastery: Queries, Joins, Subqueries, and Advanced Functions

• Indexing Strategies: B-trees, Hash Indexes, and Query Optimization

• Transaction Management: ACID Properties and Concurrency Control

• Stored Procedures, Triggers, and Database Programming

• Database Security: Authentication, Authorization, and Encryption

• Backup and Recovery: Point-in-time Recovery and Disaster Planning

• Database Performance Tuning: Query Optimization and Monitoring

• Document Databases: MongoDB, CouchDB, and Document Modeling

• Key-Value Stores: Redis, DynamoDB, and Caching Strategies

• Column-Family Databases: Cassandra, HBase, and Big Data Storage

• Graph Databases: Neo4j, GraphQL, and Relationship Modeling

• Database Installation and Configuration

• User Management and Access Control

• Monitoring and Maintenance: Health Checks and Performance Metrics

• Database Migration and Version Control Strategies

• Data Warehouse Design: Star Schema, Snowflake Schema, and Fact Tables

• ETL/ELT Processes: Data Extraction, Transformation, and Loading

• Data Modeling: Dimensional Modeling and Business Intelligence

• Data Quality: Profiling, Cleansing, and Governance

• Hadoop Ecosystem: HDFS, MapReduce, and Distributed Computing

• Apache Spark: RDDs, DataFrames, and Streaming Applications

• Data Lakes: Storage, Cataloging, and Lakehouse Architecture

• Real-time Processing: Kafka, Storm, and Stream Analytics

• OLAP Cubes: Multidimensional Analysis and Drill-down Capabilities

• Data Visualization: Tableau, Power BI, and Interactive Dashboards

• Predictive Analytics: Forecasting Models and Statistical Analysis

• Self-Service BI: Empowering Business Users with Data Access

• AWS Redshift, Azure Synapse, and Google BigQuery

• Data Pipeline Orchestration: Apache Airflow and Cloud Workflows

• Data Governance: Privacy, Compliance, and Data Lineage

• Cost Optimization: Storage, Compute, and Query Performance

• OSI Model: Layer-by-layer Network Architecture

• TCP/IP Protocol Suite: IP Addressing, Subnetting, and Routing

• Network Topologies: LAN, WAN, and Network Design Principles

• Network Security: Firewalls, VPNs, and Access Control

• Switching and Routing: VLANs, STP, and Dynamic Routing Protocols

• Wireless Networks: WiFi Standards, Security, and Enterprise Deployment

• Network Monitoring: SNMP, NetFlow, and Performance Analysis

• Network Troubleshooting: Tools, Techniques, and Best Practices

• Software-Defined Networking (SDN): Controllers and Programmability

• Network Virtualization: VXLAN, GRE, and Overlay Networks

• Load Balancing: Algorithms, Health Checks, and High Availability

• Network Automation: Ansible, Python, and Infrastructure as Code

• AWS VPC, Azure Virtual Network, and Google Cloud VPC

• Container Networking: Docker, Kubernetes, and Service Mesh

• Microservices Networking: API Gateways and Service Discovery

• Network Security in Cloud: Security Groups, NACLs, and Zero Trust

• Operating System Fundamentals: Kernel, Shell, and System Calls

• Process Management: Creation, Scheduling, and Inter-process Communication

• Memory Management: Virtual Memory, Paging, and Memory Allocation

• File Systems: Structure, Access Methods, and File Operations

• System Calls and API Programming

• Multi-threading: Thread Creation, Synchronization, and Deadlocks

• Inter-process Communication: Pipes, Sockets, and Shared Memory

• Device Drivers: Hardware Interface and Kernel Module Development

• Linux/Unix Administration: User Management, Permissions, and Services

• Windows Server Administration: Active Directory and Group Policy

• System Monitoring: Performance Metrics, Logs, and Alerting

• Backup and Recovery: System Images, Snapshots, and Disaster Recovery

• Virtual Machines: Hypervisors, Resource Allocation, and Migration

• Container Technology: Docker, Containerd, and Container Orchestration

• Kubernetes: Pods, Services, and Cluster Management

• Infrastructure as Code: Terraform, Ansible, and Automated Deployment

• Time Value of Money: Present Value, Future Value, and Annuities

• Probability and Statistics for Finance: Distributions and Risk Metrics

• Stochastic Processes: Random Walks, Brownian Motion, and Martingales

• Financial Calculus: Derivatives, Integrals, and Differential Equations

• Portfolio Theory: Modern Portfolio Theory and Asset Allocation

• Risk Management: VaR, Expected Shortfall, and Risk Metrics

• Options Pricing: Black-Scholes Model and Greeks

• Fixed Income: Bond Pricing, Yield Curves, and Duration Analysis

• Trading Strategies: Mean Reversion, Momentum, and Statistical Arbitrage

• Market Microstructure: Order Types, Market Making, and Liquidity

• High-Frequency Trading: Latency, Co-location, and Market Impact

• Backtesting: Strategy Validation, Performance Metrics, and Risk Analysis

• Blockchain and Cryptocurrencies: Smart Contracts and DeFi

• Financial APIs: Market Data, Trading Platforms, and Integration

• Machine Learning in Finance: Credit Scoring, Fraud Detection, and Trading

• Regulatory Compliance: KYC, AML, and Financial Regulations

• System Design Principles: Scalability, Reliability, and Maintainability

• Architecture Patterns: Monolithic, Microservices, and Event-Driven

• Data Flow Design: APIs, Message Queues, and Data Pipelines

• System Integration: Service-Oriented Architecture and API Design

• Load Balancing: Algorithms, Health Checks, and Global Distribution

• Caching Strategies: CDN, Application Cache, and Database Caching

• Database Scaling: Read Replicas, Sharding, and Distributed Databases

• Microservices: Service Discovery, Circuit Breakers, and Resilience

• Fault Tolerance: Redundancy, Failover, and Disaster Recovery

• Monitoring and Observability: Logging, Metrics, and Distributed Tracing

• Performance Optimization: Profiling, Bottleneck Analysis, and Tuning

• Security Architecture: Authentication, Authorization, and Data Protection

• Container Orchestration: Kubernetes, Service Mesh, and Cloud Platforms

• Serverless Architecture: Functions, Event-Driven Computing, and FaaS

• Infrastructure as Code: Terraform, CloudFormation, and GitOps

• DevOps Practices: CI/CD, Automated Testing, and Deployment Strategies

• Data Structures: Arrays, Linked Lists, Trees, and Graphs

• Searching and Sorting: Binary Search, Quick Sort, and Merge Sort

• Dynamic Programming: Memoization, Tabulation, and State Transitions

• Greedy Algorithms: Optimal Substructure and Greedy Choice Property

• Graph Algorithms: DFS, BFS, Shortest Path, and Minimum Spanning Trees

• String Algorithms: Pattern Matching, Suffix Arrays, and String Processing

• Number Theory: Prime Numbers, GCD, LCM, and Modular Arithmetic

• Combinatorics: Permutations, Combinations, and Probability

• Problem Analysis: Understanding Constraints and Edge Cases

• Algorithm Design: Time Complexity Analysis and Space Optimization

• Implementation Strategies: Clean Code, Debugging, and Testing

• Competition Strategies: Time Management and Problem Prioritization

• Segment Trees and Binary Indexed Trees for Range Queries

• Network Flow: Maximum Flow, Minimum Cut, and Matching Algorithms

• Game Theory: Nim Games, Grundy Numbers, and Strategic Analysis

• Geometry: Computational Geometry and Geometric Algorithms