Offered By in collaboration with

offer
INSTITUTIONAL BUNDLE OFFER PER STUDENT
elective1
Course 1 ₹ 17,000/-
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elective
Course 2 ₹ 17,000/-
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elective
Course 3 ₹ 17,000/-
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elective
Course 4 ₹ 17,000/-
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Choose any 4 courses
Total Price
₹ 68,000/-
Offer Price
₹ 42,000/-
Offer available only for Institutions

Course Details

Credits & LTP
Each course offers 5 credit points.
Lecture
3
Tutorial
1
Practical (Hands-on)
1
Credit
5
Recommended For:
Graduation Programme - B.E./B.Tech
Branch - Applicable across all engineering branches
Semester -
5 th,  6 th,  7 th  and  8 th

Course Summary

Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine Learning focusses on the development of computer programs that can access data and learn from it.

The Machine Learning market size is expected to grow from $1.03 billion in 2016 to $8.81 billion worldwide by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period owing to a large number of new job openings.

Machine Learning for Real-World Application is a course that teaches the basic and advanced concepts of Machine Learning supported by industry relevant business case studies. The course uses Python as the programming environment. Datasets required for the case studies can be obtained from the internet. This state-of-the-art course is available for engineering students who aspire to take part in knowledge building and make computers capable of taking intelligent decisions in several areas such as smart home applications, providing bank loans to customers, business analytics including share market predictions, sentiment analysis, security and surveillance, real estate businesses and more.

Hands-On

A virtual hands-on environment is integrated within the course.

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RECOMMENDED PRIOR KNOWLEDGE
1. Mathematics and Statistics
  • Linear Algebra: Matrices and Vectors
  • Calculus: Differentiation, Partial Derivatives and Gradient
  • Statistics: Descriptive Statistics, Normal Distribution, Probability
2. Python
  • Basic Programming
  • Data Processing using NumPy, SciPy, Matplotlib and Pandas
  • Basic usage of Scikit, Scikit-learn packages in Python

Course Syllabus

The course syllabus will be delivered through a combination of eLearning resources, digital lectures, community based digital classrooms as applicable.
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Course Components

Digital Learning
Resources
Digital course accessible anytime and anywhere View Sample
Digital
Lectures
Digital lectures delivered by industry and academic experts View Sample
Academic Connect
Community
Focussed on building conceptual clarity View Sample
Industry Connect
Community
Focussed on building industry oriented applied knowledge View Sample
Industry
Assignment
Access to two industry related mini projects View Sample
Practice
Assessment
Access to two practice assessments for self-evaluation View Sample
Summative
Assessment
Test of theoretical and applied knowledge View Sample
Verifiable Digital
Certificate
Verifiable certificate on successful completion View Sample
Internship
Opportunity
For top percentile, subject to vacancies in corporates and their hiring policies View Sample
Job
Opportunity
Visibility to job vacancies with leading corporate recruiters, subject to vacancies and their hiring policies View Sample

Available Locations

!~StateCity~!
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FAQs

1. How will this programme help students in building their proficiency? +
  • This programme is envisaged to help build a strong foundation across various streams through digital lectures, practice and summative assessments, mini projects, mentorship from renowned industry and academic experts. Students will also get an opportunity to showcase their skills to potential recruiters.