23+
Experience (Years)
AI is driving innovations and transforming business across the work. Many companies are developing solutions to make life easier with the help of big data, digital assistants and machine learning. The need for business process automation is leading the data scientists to new frontiers. AI will leave no stone unturned and bring unprecedented development. Do you have the aptitude to step in and lead AI operations from the front?
AI Everywhere! If you don’t do something about AI, you will be left behind or disrupted into extinction – this is a message that executives, Managers, Team Leaders and CXOs often hear. Today, companies are faced with some compelling new choices, like robotic process automation (RPA), artificial intelligence (AI), machine learning (ML), blockchain and Industrial Internet of Things (IIoT), to name a few. Corporate Leaders have started working on understanding what these buzzwords mean and its relevance to their business and determining which technology to invest.
It is essential that Leaders have a foundational knowledge of digital transformation because they will rely on digital business to make their numbers. It will be hard for Leaders to lead digital initiatives if they don’t understand digital clearly. A lack of understanding can result in misdirection of efforts and painful experiences, and ultimately place the organizational transformation goals in jeopardy.
This course presents the concepts and technologies behind AI in a simple manner through examples and case studies. A comparison of AI with other technologies like Big Data, RPA, Cloud and Industry 4.0 is also provided.
In general terms, digital transformation can be thought of as integration of digital technology into all areas of a business resulting in fundamental changes to how businesses operate and how they deliver value to their stakeholders (employees, vendors, customers, etc.) to help the organizations compete effectively in an increasingly digital world.
In many ways, digital transformation is a misnomer, because digital is not all about technology. Digital transformation is about solving a business problem or developing a new approach where the technology is an enabler and never the driver. It is about how a technology can help a company rethink the way in which it conducts business and change the stakeholders' (customers, vendors, employees) experience, and it’s about adaptation. This sometimes means walking away from longstanding business processes that companies were built upon in favour of relatively new practices that are still being defined. Think Uber, Lyft, Netflix, Airbnb.
Another key point to note with digital transformation is that it is not a one and done exercise; rather, it is a mindset, a paradigm shift that allows the organizations to continually improve and ultimately develop a level of digital maturity in order to keep up with the rapidly evolving technological advances in the Market.
| Week | Content | ||
|---|---|---|---|
| Week 1 |
Unit 1: AI, Data Science and Machine Learning Concepts. What is AI, History of AI, Predictive Vs Descriptive Vs Prescriptive Analytics, Weak AI Vs Strong AI, Classification Vs Regression, Case Studies |
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| Week 2 |
Unit 2: Business Intelligence Implementing 3 Types Of Analytics. Descriptive, Diagnostic, Prescriptive. |
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| Week 3 |
Unit 3: Math behind ML. Dependent Vs Independent Variables, Accuracy in classification & regression, Hypothesis Testing, Type 1 & 2 Errors. |
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| Week 4 |
Unit 4: Augmented Analytics & Auto ML. Simple Linear regression, Multiple Linear regression, Logistic regression, Decision tree, kNN. |
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| The units 2 to 4 are hands on sessions. All students will be expected to implement the programming code during the lecture itself. | |||
Demonstrates a high Level of performance and outstanding mastery of the domain area.
Demonstrates excellent mastery of subject matter and overall commendable performance and achievement.
Very good mastery of subject matter and excellent knowledge and understanding.
Average performance and achievement. Not sufficient for working independently.
Not completed the required number of assessments.
B. Tech., MBA
23 years of experience in MNCs and startups. Authored 5 books. Three books are related to AI and ML – Comprehensive Machine Learning, Python Programming, De-Mystifying Math & Stats for Machine Learning. Research interests in Fraud Risk Analytics and Customer AI.
Experience (Years)
Authored books
Students Trained
BE, M. Tech
Data science and machine learning professional with signal processing background having 8 years of research experience in Artificial Intelligence and Machine Learning. 4 research publications. Research interests in Healthcare AI and Biomedical Signal Processing.
Experience (Years)
Research Publications
Courses Completed
These will be spot quizzes, mostly unscheduled/surprise tests.
Assignments will be given every week.
There will be a test at the beginning of every week.
Final comprehensive exam.
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