Curriculum
- 6 Sections
- 90 Lessons
- Lifetime
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- Automation15
- 1.0Automation in Modern Industries
- 1.1Fixed, Programmable & Flexible
- 1.2Control Systems and PLCs
- 1.3Sensors and Actuators in Automation
- 1.4Robotics and Automated Systems
- 1.5Automation in Manufacturing
- 1.6Process Automation: MPC & PID Control
- 1.7Software Automations Tools
- 1.8Robotics Process Automation (RPA)
- 1.9Automation in Cloud and DevOps
- 1.10Networking and Automation Protocols
- 1.11Ethical considerations in Automation
- 1.12Home & Hospitalities Automations
- 1.13Future Trends in Automation
- 1.14Robots & CoBots: The Dual
- Data Science15
- 2.0Introduction to Data Science
- 2.1Data Collection and Data Wrangling
- 2.2Exploratory Data Analysis (EDA)
- 2.3Principles of Data Visualization
- 2.4Probability Theory and Statistics
- 2.5Tokenization & Stemming
- 2.6Text Classification & Sentiment Analysis
- 2.7Time Series Models: MAPE, RMSE, MAE
- 2.8Data Engineering Basics: ETL
- 2.9Database systems: SQL and NoSQL
- 2.10Big Data and Distributed Computing
- 2.11Model Deployment and Production
- 2.12Algorithms in Machine Learning
- 2.13Working Large Datasets: PySpark
- 2.14Real World Problem Solving with DS
- Machine Learning15
- 3.0Introduction to Machine Learning
- 3.1Linear Algebra and Calculus for ML
- 3.2Exploratory Data Analysis (EDA)
- 3.3Supervised Learning: Regression
- 3.4Supervised Learning: Classification
- 3.5Neural Networks: Percept & Activate
- 3.6Advanced NLP Techniques: ChatBots
- 3.7Train-Test Split and Cross-Validation
- 3.8Q-Learning and Deep Q-Networks (DQN)
- 3.9Neural Networks for Time Series
- 3.10Techniques for Detecting Anomalies
- 3.11Feature Engineering and Selection
- 3.12Model Deployment Strategies
- 3.13Ethical Considerations in ML
- 3.14Real-World Machine Learning
- Artificial Intelligence15
- 4.0Introduction to Artificial Intelligence
- 4.1Semantic Networks and Ontologies
- 4.2Genetic & Evolutionary Algorithms
- 4.3Sensors, Vision & Object Detection
- 4.4Computer Vision: Mask R-CNN, U-Net
- 4.5Case Studies: MYCIN, DENDRAL
- 4.6AI: Causes, Effects & Solutions
- 4.7AI-Driven Data Mining Techniques
- 4.8AI for Healthcare: Predictive Analytics
- 4.9AI for Genomics and Bioinformatics
- 4.10AI for Trading & Fraud Detection
- 4.11AI Autonomous: Vehicles & Robots
- 4.12AI for Simulating Human Cognition
- 4.13Future of Internet of Things & AI
- 4.14AI Tools & Platform Frameworks
- Cyber Security15
- 5.0Introduction to Cyber Security
- 5.1Encryption and Cryptography
- 5.2Authentication and Access Control
- 5.3Web Application Firewalls (WAF)
- 5.4Ethical Hacking & Penetration Testing
- 5.5Cyber Forensics: Digital Evidence
- 5.6Scanning Tools: Nessus & OpenVAS
- 5.7Cloud Security Solutions: CASBs
- 5.8Internet of Things (IoT) Security
- 5.9Risk Management and Governance
- 5.10Phishing, Pretexting, Baiting & Tailgating
- 5.11Cybersecurity Frameworks & Standards
- 5.12Data Privacy and Protection: GDPR
- 5.13CyberSecurity Laws and Ethics
- 5.14Vulnerability Scan & Penetration Test
- BlockChains15
- 6.0Cryptography: Hash, Encrypt & Decrypt
- 6.1Consensus Mechanisms: Proof of Work
- 6.2Blockchain Platforms and Ecosystems
- 6.3Basics of Crypto Mining: How It Works
- 6.4Crypto Mining Difficulty & Adjustment
- 6.5Security in Blockchain & Crypto’s
- 6.6Hyperledger and Private Blockchain
- 6.7NFTs – Non-Fungible Tokens
- 6.8NFT Creation and Minting Process
- 6.9NFT Marketplaces & Ecosystem
- 6.10NFTs in Art and Digital Content
- 6.11NFTs in Gaming and Virtual Worlds
- 6.12Smart Contracts & DeFi (DEXs)
- 6.13Legal, Regulatory & Ethical Issues
- 6.14Blockchain Scaling Solutions
Legal, Regulatory & Ethical Issues
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