AI & Machine Learning Essentials

Kingston Open College

• Module 01: AI Foundations and Realities 10:46

  • 1: Module 1 AI Foundations and Realities - Part 1 02:46
  • 2: Module 1 AI Foundations and Realities - Part 2 08:00

• Module 02: Core Foundations of Machine Learning 10:34

  • 3: Module 2 Core Foundations of Machine Learning - Part 1 02:57
  • 4: Module 2 Core Foundations of Machine Learning - Part 2 07:37

• Module 03: Machine Learning in Action 09:31

  • 5: Module 3 Machine Learning in Action - Part 1 04:41
  • 6: Module 3 Machine Learning in Action - Part 2 04:50

• Module 04: Deep Learning Foundations 09:19

  • 7: Module 4 Deep Learning Foundations Part 1 03:12
  • 8: Module 4 Deep Learning Foundations Part 2 06:07

• Module 05: Tools, Platforms, and Practice 10:12

  • 9: Module 5 Tools, Platforms, and Practice - Part 1 04:38
  • 10: Module 5 Tools, Platforms, and Practice - Part 2 05:34

• Module 06: MLOps and Deployment 08:02

  • 11: Module 6 MLOps and Deployment - Part 1 04:01
  • 12: Module 6 MLOps and Deployment - Part 2 04:01

• Module 07: Applications Across Sectors 09:35

  • 13: Module 7 Applications Across Sectors - Part 1 06:12
  • 14: Module 7 Applications Across Sectors - Part 2 03:23

• Module 08: Ethics, Law, and Responsibility 07:49

  • 15: Module 8 Ethics, Law, and Responsibility - Part 1 03:56
  • 16: Module 8 Ethics, Law, and Responsibility - Part 2 03:53

AI & Machine Learning Essentials starts with AI foundations and realities. It covers intelligent systems, industry adoption, myths, and machine learning relevance. It then explains core foundations of machine learning. It addresses data, algorithms, key models, and statistical predictions. The course moves into machine learning in action. It clarifies training versus testing and explains supervised, unsupervised, and reinforcement learning. Deep learning foundations follow with neural networks and how machines process images and language. It also explores models beyond traditional approaches. The programme then introduces essential tools. It covers Python basics, Scikit-Learn, Keras, TensorFlow, PyTorch, and cloud AI tools. Next, it explains MLOps and deployment. It addresses pipelines, automation, monitoring, maintenance, and scaling. Applications across sectors follow, including healthcare, finance, education, creative industries, and sustainability. The course then explores ethics, bias, fairness, privacy, regulation, and responsible AI development. Finally, the bonus module highlights career pathways, real-world impact, AI at the edge, and human skills that matter.

• Aspiring AI learners
  
• Data and analytics enthusiasts
  
• Software developers
  
• Technology professionals
  
• Business analysts
  
• Students exploring AI careers

• You should have basic computer literacy.
  
• However, no advanced coding experience is required.
  
• In addition, curiosity about AI supports learning.

• Machine Learning Engineer
  
• AI Engineer
  
• Data Scientist
  
• Data Analyst
  
• MLOps Engineer
  
• AI Research Assistant
  
• Business Intelligence Analyst
  
• AI Product Analyst
  
• Cloud AI Specialist
  
• Technology Consultant
  

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