Keep Learning , Keep Growing

AI

  • Applications of AI
  • Building Blocks of AI
  • Generative AI and its Use Cases
  • Machine Learning and Deep Learning
  • NLP and Computer Vision
  • AI frameworks and libraries
  • Programming in Python
  • Python capabilities for AI
  • Pretrained AI Models
  • Using Cloud Capabilities for AI

AI

Master AI techniques to innovate and transform industries worldwide.

  • Applications of AI
  • Building Blocks of AI
  • Generative AI and its Use Cases
  • Machine Learning and Deep Learning
  • NLP and Computer Vision
  • AI frameworks and libraries
  • Programming in Python
  • Python capabilities for AI
  • Pretrained AI Models
  • Using Cloud Capabilities for AI

Data Science

  • Introduction to Data Science
  • Data Collection and Cleaning
  • Data Analysis
  • Statistical Analysis
  • Data Manipulation with Python
  • Data Engineering
  • Machine Learning
  • Tools for Data Visualization and Presentation
  • Case Studies and Applications
  • Data Science Project

Data Science

Unlock the power of data to drive insights, innovation, and impact.

  • Introduction to Data Science
  • Data Collection and Cleaning
  • Data Analysis
  • Statistical Analysis
  • Data Manipulation with Python
  • Data Engineering
  • Machine Learning
  • Tools for Data Visualization and Presentation
  • Case Studies and Applications
  • Data Science Project

Robotics

  • IoT components, Drone and Robotics
  • Sensors and Actuators
  • Communication and Networking
  • Robotics Programming and Control
  • Drone Programming and Control
  • Data Management and Analytics
  • Computer Vision
  • Kinematics
  • Dynamics
  • Project Building

Robotics

Master the art of building intelligent machines for a smarter tomorrow.

  • IoT components, Drone and Robotics
  • Sensors and Actuators
  • Communication and Networking
  • Robotics Programming and Control
  • Drone Programming and Control
  • Data Management and Analytics
  • Computer Vision
  • Kinematics
  • Dynamics
  • Project Building

Cyber Security

  • Cyber Attacks in the World
  • Types of Cyber Threats
  • Cyber Security Frameworks and Standards
  • Securing Protocols
  • Cryptography
  • Network Security
  • Identity and Access Management (IAM)
  • Incident Response and Management
  • Security Operations and Monitoring
  • Cloud Security
  • Endpoint and Application Security

Cyber Security

Learn to defend digital frontiers and secure the future of technology.

  • Cyber Attacks in the World
  • Types of Cyber Threats
  • Cyber Security Frameworks and Standards
  • Securing Protocols
  • Cryptography
  • Network Security
  • Identity and Access Management (IAM)
  • Incident Response and Management
  • Security Operations and Monitoring
  • Cloud Security
  • Endpoint and Application Security
AI

Module 1: Introduction to AI

1.1 What is AI?

  • Definition and scope
  • History and evolution of AI
  • AI vs. Human Intelligence

1.2 Applications of AI

  • Customer Support/CRM
  • Healthcare
  • Finance
  • Transportation
  • Entertainment
  • Robotics
  • Other industries

Module 2: Building Blocks of AI

2.1 Machine Learning (ML)

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Common Algorithms (e.g., Linear Regression, Decision Trees, Neural Networks)

2.2 Deep Learning (DL)

  • Neural Networks
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Applications of Deep Learning

2.3 Natural Language Processing (NLP)

  • Text Processing
  • Sentiment Analysis
  • Language Translation
  • Chatbots

2.4 Computer Vision

  • Image Recognition
  • Object Detection
  • Image Generation

Module 3: Tools and Technologies

3.1 Programming Languages

  • Python

3.2 AI Frameworks and Libraries

  • TensorFlow
  • PyTorch
  • Scikit-Learn

3.3 Data Handling and Preprocessing

  • Data Collection
  • Data Cleaning
  • Feature Engineering

Module 4: Practical AI Projects

4.1 Project 1: Sentiment Analysis

  • Building a business application using Generative AI

4.2 Project 2: Image Classification

  • Use AI to classify images and tag images as a real-world object.
Data Science

Module 1: Data Management

1.1: Introduction to Data Science

  • Overview of Data Science
  • Applications of Data Science
  • Data Science vs. Business Intelligence
  • Data Science vs. Data Analytics

1.2: Data Collection and Cleaning

  • Types of data: structured, unstructured, semi-structured
  • Data sources: databases, web scraping, APIs
  • Handling missing data
  • Data normalization and standardization
  • Data transformation techniques

1.3: Data Analysis

  • NoSQL Databases
  • SQL Databases & Writing Query
  • Descriptive Statistics
  • Data Visualization
  • Identifying patterns and trends
  • Correlation analysis

1.4: Statistical Analysis

  • Probability Theory
  • Inferential Statistics
  • Hypothesis Testing

Module 2: Data Processing & Machine Learning

2.1: Data Manipulation with Python

  • Introduction to Python for Data Science
  • Data Manipulation with Pandas (Python)
  • Data manipulation using cloud tools
  • Python libraries for Big data manipulation
  • Sharding and other optimization strategy

2.2: Data Engineering

  • Data Warehousing and ETL Processes
  • Building Data Pipelines
  • Using SQL and NoSQL Databases

2.3: Machine Learning

  • Introduction to Machine Learning
  • Supervised Learning Algorithms (Linear Regression, Decision Trees, etc.)
  • Unsupervised Learning Algorithms (Clustering, PCA, etc.)
  • Model Evaluation and Validation

2.4: Advanced Machine Learning

  • Ensemble Methods (Random Forest, Gradient Boosting)
  • Neural Networks and Deep Learning
  • Natural Language Processing (NLP)
  • Time Series Analysis

2.5: Model Deployment and Product Development

  • Model deployment strategies
  • Introduction to cloud platforms: AWS, Google Cloud, Azure
  • Using Docker for model deployment
  • Monitoring and maintaining models in production

Module 3: Data Visualization and Data Ethics

3.1: Tools for Data Visualization and Presentation

  • Principles of Effective Data Visualization
  • Tools for Data Visualization (Matplotlib, Seaborn, ggplot2, Tableau, Cloud-Based tools)
  • Creating Dashboards
  • Communicating Data Insights

3.2: Data Ethics and Privacy

  • Ethical considerations in data science
  • Data Privacy laws and regulations (GDPR, DPDPA)
  • Bias and fairness in Algorithms

Module 4: Applied Data Science

4.1: Case Studies and Applications

  • Real-World Data Science Case Studies
  • Industry-Specific Applications (Finance, Healthcare, Retail, etc.)

4.2: Data Science Project

  • Project Selection and Planning
  • Data Collection and Preprocessing
  • Model Building and Evaluation
  • Presenting Results
Robotics

Module 1: Building Blocks of Robotics

1.1 IoT components, Drone and Robotics

  • Sensors, actuators, connectivity and data processing
  • Architecture of IoT, Drone and Robotics

1.2 Sensors and Actuators

  • Types of sensors: temperature, humidity, light, motion, proximity, etc.
  • Actuators: motors, servos, relays, etc.
  • Interfacing sensors and actuators with microcontrollers
  • Data acquisition and signal processing

1.3 Communication and Networking

  • Wired vs. wireless communication
  • Network topologies
  • Bluetooth, Wi-Fi, Zigbee, and other wireless technologies
  • IoT networking protocols and standards
  • Communication protocols: MQTT, HTTP, CoAP, LoRaWAN

1.4 Embedded Systems

  • Basics of embedded systems
  • Microcontrollers and microprocessors
  • Popular boards and development platforms: Arduino, Raspberry Pi, ESP32
  • Programming languages for embedded systems: C/C++, Python

Module 2: Programming & Data Management

2.1 Robotics Programming and Control

  • Introduction to ROS (Robot Operating System)
  • Path planning and navigation
  • Robot control systems: open-loop, closed-loop, PID control

2.2 Drone Programming and Control

  • Identify components required for drone-making
  • Path planning and navigation
  • Drone Programming

2.3 Data Management and Analytics

  • Data collection and storage
  • Data preprocessing and cleaning
  • Data analytics and visualization
  • Introduction to machine learning for IoT

2.4 Cloud Capabilities for IoT & Robotics

  • IoT platforms and cloud services
  • AWS IoT

Module 3: Robot Perception

3.1 Computer Vision

  • Basics of Image Processing
  • Object Recognition
  • Visual Servoing

3.2 Kinematics

  • Forward Kinematics
  • Inverse Kinematics
  • Denavit-Hartenberg Parameters

3.3 Dynamics

  • Newton-Euler Formulation
  • Lagrangian Formulation
  • Robot Dynamics Simulation

Module 4: Hands-on Projects and Practical Applications

4.1 Project Building

  • Building projects: smart home systems, environmental monitoring
  • Building robotics projects: obstacle-avoiding robots, robotic arms, etc.
  • Integration projects: IoT-controlled robots, remote monitoring and control
  • Drone making & programming
Cyber Security

Module 1: Cyber Threats and World Readiness

1.1 Cyber Attacks in the World

  • Recent cyber attacks in the World
  • Implications of cyber attacks
  • Why Cyber security is the World's biggest headache
  • Opportunity in the cyber security domain
  • Risk assessment and management process

1.2 Types of Cyber Threats

  • Malware: viruses, worms, Trojans, ransomware
  • Phishing and social engineering attacks
  • Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks
  • Man-in-the-middle (MITM) attacks
  • Advanced Persistent Threats (APTs)

1.3 Cyber Security Frameworks and Standards

  • NIST Cybersecurity Framework
  • ISO/IEC 27001 and 27002
  • CIS Controls
  • PCI-DSS, GDPR, HIPAA, and other regulatory requirements
  • Security policies and procedures
  • Compliance requirements and audits

Module 2: Protocols and Cryptography

2.1 Securing Protocols

  • HTTPS vs HTTP
  • FTPS vs FTP vs SFTP
  • TLS 1.2 /TLS 1.3 , TCP , IP, IPv6
  • Protocol analysis using WireShark/tcpdump
  • Play with protocols using Scapy

2.2 Cryptography

  • Basics of cryptography: symmetric and asymmetric encryption
  • Cryptographic algorithms: AES, RSA, ECC
  • Hash functions and digital signatures
  • Public Key Infrastructure (PKI)
  • Encryption protocols: SSL/TLS, IPSec

2.3 Network Security

  • Fundamentals of network architecture
  • Firewalls, Intrusion Detection Systems (IDS), and Intrusion Prevention Systems (IPS)
  • Virtual Private Networks (VPNs)
  • Secure network design and segmentation
  • Wireless network security

Module 3: Access Control & Incident Management

3.1 Identity and Access Management (IAM)

  • Authentication vs. authorization
  • Multi-Factor Authentication (MFA)
  • Single Sign-On (SSO)
  • Role-Based Access Control (RBAC)
  • Identity governance and administration

3.2 Incident Response and Management

  • Incident response lifecycle
  • Building an incident response team
  • Tools and techniques for incident detection and analysis
  • Containment, eradication, and recovery
  • Post-incident activities and lessons learned

3.3 Security Operations and Monitoring

  • Security Information and Event Management (SIEM)
  • Log management and analysis
  • Threat hunting and intelligence
  • Vulnerability management and patching
  • Continuous monitoring and improvement

Module 4: Securing Cloud & Applications

4.1 Cloud Security

  • Cloud computing models: IaaS, PaaS, SaaS
  • Cloud security challenges and best practices
  • Data protection in the cloud
  • Secure cloud architecture
  • Cloud compliance and governance

4.2 Endpoint and Application Security

  • Securing endpoints: anti-virus, anti-malware, and endpoint detection and response (EDR)
  • Secure software development lifecycle (SDLC)
  • Web application security: OWASP Top Ten
  • Mobile application security
  • Application testing: static and dynamic analysis