23+
Experience (Years)
How will it be when it is possible for machines to learn from experience, adjust to new inputs and perform human-like tasks?! Artificial intelligence and machine learning strategies are becoming critical for making differentiation and sometimes survival of the organization in the Industry. AI hopes to produce some of this century’s most important and revolutionary inventions. The products of the new AI revolution are self-driven vehicles, robot assistance, and digital disease diagnostics that will affect how we live and function.
Machine learning is a specific form of AI that allows computers to learn and grow after they are introduced to scenarios in the form of data. Both of these areas offer promising career opportunities. The demand for qualified AI professionals has more than doubled in recent years. Those who want to take a lead in research and development in AI are provided with endless career opportunities.
Machine learning is a subset of artificial intelligence that is at the forefront of digital transformation in the world. Thanks to machine learning, it is now possible to detect diseases, know the defaulters of a loan and know the future sales of a product. All this information can be had proactively and not as an after the fact scenario. Machine learning and artificial intelligence-based roles are in great demand in the job market and such roles offer a higher salary than traditional programming roles.
This course covers the concepts of machine learning as well as the application of these concepts using case studies and examples, along with a walk through of the python codes. Python programming is also covered for the benefit of those who are new to python and those who want to refresh some of the topics in python.
Simple and multiple linear regression
Logistic regression
Decision tree, Random forest and XG boost
Unsupervised algorithms - Cluster (kNN based) and Hierarchical.
Learners will also understand how to develop the above machine learning in a cloud environment. They will learn not just to code in cloud but also to access the data stored in cloud. This will be particularly helpful to learners since many organizations are adopting cloud at a fast pace.
A key aspect of the course is the coverage of Exploratory Data Analysis (EDA). EDA covers the set of activities that you do before you start the ML project. This course is taught by an industry veteran, who brings his vast experiences and practical perspectives into the program.
| 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: Python Programming. Operators, Conditions, Loops, Functions, Numpy, Pandas, Matplotlib, Seaborn. |
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| Week 3 |
Unit 3: Math behind AI & DS. Dependent Vs Independent Variables, Accuracy in classification & regression, Hypothesis Testing, Type 1 & 2 Errors. |
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| Week 4 |
Unit 4: Supervised & Unsupervised Learning Algorithms. 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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