What will you learn?
- Understand the difference between AL, ML & Deep Learning
- Learn the principles and application of Machine Learning
- Decipher the role of Big Data and capitalize as an enabler of AI
- Derive value with analytics through applications of AI & ML
- Apply the science of Robotics to technology enabled business context
Pre-requisites
Description
Course curriculum
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1
Chapter 1 : Overview of AI as a contemporary Discipline
- Faculty Introduction & Areas of Expertise
- Learning Objective & Skill Outcomes
- Deciphering Artificial Intelligence
- Transformational Impact of AI on Human Lifes
- Chapter 1 Quiz
- Additional Learning Resources
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2
Chapter 2 : Enabling Role of AI across Decision Contexts.
- Application of AI & Robotics
- Role of Big Data as an enabler of AI
- Navigating the AI Maturity Framework
- Chapter 2 Quiz
- Additional Learning Resources
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3
Chapter 3 : The Interdisciplinary interlocks of AI, ML & Deep Learning.
- Unravelling Machine Learning as a Discipline
- Differentiating Artificial Intelligence, Machine Learning and Deep Learning
- Varied types of Machine Learning
- Chapter 3 Quiz
- Additional Learning Resources
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4
Chapter 4 : Relevance & Roles of Data Science, AI & ML
- Disciplines of Data Science
- Drivers and Roles of AI & ML
- Artificial Intelligence & Machine Learning derived roles & skills
- Sample Tech Stack of AI & ML
- Chapter 4 Quiz
- Additional Learning Resources
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5
Chapter 5 : Leveraging Data Science AI & Analytics as Value Drivers.
- The Process Approach to Data Science
- Challenges in the realm of AI
- Traits & Compof Data Professional
- Exploring and familiarizing with the Discipline of Analytics
- Chapter 5 Quiz
- Additional Learning Resources
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6
Resource Library
- IPL Resource Library
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7
Recommended Readings and Books
- Recommended Books