6 Sections
90 Lessons
52 Weeks
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Automation
15
1.1
Automation in Modern Industries
1.2
Fixed, Programmable & Flexible
1.3
Control Systems and PLCs
1.4
Sensors and Actuators in Automation
1.5
Robotics and Automated Systems
1.6
Automation in Manufacturing
1.7
Process Automation : MPC & PID Control
1.8
Software Automation Tools
1.9
Robotics Process Automation (RPA)
1.10
Automation in Cloud and DevOps
1.11
Networking and Automation Protocols
1.12
Ethical considerations in Automation
1.13
Home & Hospitalities Automations
1.14
Future Trends in Automation
1.15
Robots & CoBots: The Dual
Data Science
15
2.1
Introduction to Data Science
2.2
Data Collection and Data Wrangling
2.3
Exploratory Data Analysis (EDA)
2.4
Principles of Data Visualization
2.5
Probability Theory and Statistics
2.6
Tokenization & Stemming
2.7
Text Classification & Sentiment Analysis
2.8
Time Series Models: MAPE, RMSE, MAE
2.9
Data Engineering Basics: ETL
2.10
Database systems: SQL and NoSQL
2.11
Big Data and Distributed Computing
2.12
Model Deployment and Production
2.13
Algorithms in Machine Learning
2.14
Working Large Datasets: PySpark
2.15
Real World Problem Solving with DS
Machine Learning
15
3.1
Introduction to Machine Learning
3.2
Linear Algebra and Calculus for ML
3.3
Exploratory Data Analysis (EDA)
3.4
Supervised Learning: Regression
3.5
Supervised Learning: Classification
3.6
Neural Networks: Percept & Activate
3.7
Advanced NLP Techniques: ChatBots
3.8
Train-Test Split and Cross-Validation
3.9
Q-Learning and Deep Q-Networks (DQN)
3.10
Neural Networks for Time Series
3.11
Techniques for Detecting Anomalies
3.12
Feature Engineering and Selection
3.13
Model Deployment Strategies
3.14
Ethical Considerations in ML
3.15
Real-World Machine Learning
Artificial Intelligence
15
4.1
Introduction to Artificial Intelligence
4.2
Semantic Networks and Ontologies
4.3
Genetic & Evolutionary Algorithms
4.4
Sensors, Vision & Object Detection
4.5
Computer Vision: Mask R-CNN, U-Net
4.6
Case Studies: MYCIN, DENDRAL
4.7
Al: Causes, Effects & Solutions
4.8
Al-Driven Data Mining Techniques
4.9
Al for Healthcare: Predictive Analytics
4.10
Al for Genomics and Bioinformatics
4.11
Al for Trading & Fraud Detection
4.12
Al Autonomous: Vehicles & Robots
4.13
Al for Simulating Human Cognition
4.14
Future of Internet of Things & Al
4.15
Al Tools & Platform Frameworks
Cyber Security
15
5.1
Introduction to Cyber Security
5.2
Encryption and Cryptography
5.3
Authentication and Access Control
5.4
Web Application Firewalls (WAF)
5.5
Ethical Hacking & Penetration Testing
5.6
Cyber Forensics: Digital Evidence
5.7
Scanning Tools: Nessus & OpenVAS
5.8
Cloud Security Solutions: CASBs
5.9
Internet of Things (loT) Security
5.10
Risk Management and Governance
5.11
Phishing, Pretexting, Baiting & Tailgating
5.12
Cybersecurity Frameworks & Standards
5.13
Data Privacy and Protection: GDPR
5.14
CyberSecurity Laws and Ethics
5.15
Vulnerability Scan & Penetration Test
Block Chains
15
6.1
Cryptography: Hash, Encrypt & Decrypt
6.2
Consensus Mechanisms: Proof of Work
6.3
Blockchain Platforms and Ecosystems
6.4
Basics of Crypto Mining: How It Works
6.5
Crypto Mining Difficulty & Adjustment
6.6
Security in Blockchain & Crypto’s
6.7
Hyperledger and Private Blockchain
6.8
NFTs – Non-Fungible Tokens
6.9
NFT Creation and Minting Process
6.10
NFT Marketplaces & Ecosystem
6.11
NFTs in Art and Digital Content
6.12
NFTs in Gaming and Virtual Worlds
6.13
Smart Contracts & DeFi (DEXs)
6.14
Legal, Regulatory & Ethical Issues
6.15
Blockchain Scaling Solutions
Xtreme AI Programming
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