In one form or the other, AI is going to be infused in all the tech products. Meaning, that in order to stay a relevant leader, it has become essential to have a solid, broad understanding of AI. While you may not be implementing the solution, you need to speak the language of AI.

Whether you’re leading a team on creating AI solutions, or you just want a better understanding of AI, then this course is for you. In this course, I have deconstructed the concepts most relevant for you.

You will start by learning the basics of AI, its connection with machine learning, and the different types of algorithms you should be familiar with. You’ll then learn the rules, best practices, and infrastructure for creating great AI products that users trust. Then, you will go through some real case studies of AI application and do a deep dive into Ethical AI. You’ll end by getting an outlook into the current status and future of AI. In a nutshell, this course is your no-fuss, comprehensive guide to the essentials of AI.

Course Content

1.The Fundamentals

Learn how to use AI, ML, and DL principles to enhance automation and prediction.

• Basics of AI I
• Basics of AI II
• The Highs and Lows of AI
• Understanding Machine Learning
• Supervised Learning
• Supervised Learning: Algorithms and Business Use Cases I
• Supervised Learning: Algorithms and Business Use Cases II
• Supervised Learning: Algorithms and Business Use Cases III
• Unsupervised Learning
• Unsupervised Learning: Algorithms and Business Use Cases
• Reinforcement Learning
• Deep Learning
• Convolutional Neural Networks: What, When, How and Where
• Recurrent Neural Networks: What, When, How and Where
• Natural Language Processing: Word Embeddings
• Natural Language Processing: BERT
• Recommendation Systems
• Transfer Learning
• Evaluation Metrics I
• Evaluation Metrics II
• Quiz

2.AI in Practice

Walk through AI’s role in trust, product development, machine learning insights, and infrastructure.

• Creating AI Solutions That Users Trust
• Creating Great AI Products: The Rules
• Key ML Lessons
• AI Infrastructure: Overview
• AI Infrastructure: AI Frameworks
• AI Infrastructure: Cloud Services to Create AI Solutions I
• AI Infrastructure: Cloud Services to Create AI Solutions II
• Quiz

3.Real Case Studies

Go hands-on with AI applications in retail, entertainment, finance, and wildlife conservation.

• Starbucks: The Millions of AI Infused Cups of Coffee
• Netflix: Using AI To Give Us Better Entertainment
• American Express: AI and Credit Cards
• AI for Wildlife Conservation
• Quiz: Test Scenario

4.Responsible AI

Build a foundation in managing AI risks and fostering ethical, transparent AI practices.

• Responsible AI Practices: Introduction
• Responsible AI Practices: Fairness
• Responsible AI Practices: Interpretability
• Responsible AI Practices: Privacy
• Responsible AI Practices: Security
• Responsible AI Practices: Addendum
• AI and Ethics: Case Studies

5.What’s Next

Deepen your knowledge of AI’s current state, future potential, and continued learning in AI.

• Current State of AI in Numbers I
• Current State of AI in Numbers II
• Future of AI
• Where to Next?
• Final thoughts