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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study, Virtual Classroom	Video and Text Based

Course Overview

The Post Graduate Certificate in Data Science & Machine Learning Training Course is a 100% online course that explores the fundamental aspects of data science and machine learning. The course offered on the Coursera learning platform covers the foundation of data analytics and AI.

Post Graduate Certificate in Data Science & Machine Learning Live Course is provided by the experts at the Indian Institute of Technology Roorkee, one of the topmost technical institutes in India. The course can be accessed by anyone without prior programming knowledge.

After completion of the course through video lectures, projects and group assignments, learners will receive a professional. Post Graduate Certificate in Data Science & Machine Learning.

The Highlights

Certificate of completion
100% online course
Live online classes
Industry oriented curriculum
6 months duration
6 to 8 weekly study hours
Self-paced learning
Faculty feedback
Real world projects
Group assignments

Programme Offerings

Certificate of completion
6 months duration
6 to 8 Weekly Study
Industry Oriented Curriculum
live online classes
Real World Case Studies
Projects
Group assignments

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	Coursera

The Post Graduate Certificate in Data Science & Machine Learning Course total fee is $1,500 (Rs.1,12,862). You can explore different payment options while enrolling.

Post Graduate Certificate in Data Science & Machine Learning Course Fee Structure

Course	Amount in USD	Amount in INR
Post Graduate Certificate in Data Science & Machine Learning	$1,500	Rs. 1,12,500

Eligibility Criteria

No prior coding experience is required for the course.
Candidates are required to have basic knowledge of high school mathematics including linear algebra, calculus, and statistics.
Experience in using spreadsheets is recommended.

What you will learn

Machine learningData science knowledge

After completing the Post Graduate Certificate in Data Science & Machine Learning Classes, you will gain knowledge about the following topics:

Applications in ML and ML algorithms
Vector spaces and subspaces
Statistical techniques for data analysis
Data analytics, Python and R
Optimization algorithms in machine learning
Testing of hypothesis
Machine learning techniques for AI

Who it is for

The course can be opted by anyone interested in the field of data science and machine learning and aspires to become data scientists or data analysts.

Admission Details

Given below are the steps to enroll in the Post Graduate Certificate in Data Science & Machine Learning Training Course:

Step 1: Go to the official website by clicking on the URL given below -

https://www.coursera.org/certificates/data-science-machine-learning-iitr

Step 2: Click on the Enroll Now' option on the page.

Step 3: Enter your name, email id, and other credentials to proceed.

The Syllabus

Creating visualization of various linear transformations
Developing clear understanding of eigenvalues and eigenvectors with applications in ML
How projection operators work to project data in various ML algorithms
Special types of matrices with their important properties
Vector spaces
Vector subspaces
Symmetric and orthogonal matrices

Statistical techniques required to analyze data
Statistical thinking needed for data analysis
Techniques from multivariable calculus useful in data analytics and machine learning
Optimization algorithms required in machine learning
Gradient Calculus
Testing of hypothesis

Writing code using common Python & R functionality
Writing code using Pandas and R to work with data and perform data exploration tasks
Preparing, analyzing, and interpreting basic inferential statistics results
Developing code to create insightful visualization using Matplotlib, Pandas, Seaborn, and R

Machine learning concepts and lifecycle of an ML project
Dimensionality reduction: Principal component analysis (PCA)
Linear and logistic regressions
Linear and kernel Support Vector Machines (SVM)
Clustering: K-means
Decision Trees
Neural Networks

Adjust to team work environments and gain industry-like experience in a machine learning project
Familiarize oneself with the Kaggle platform and understand the objective of the analytics problem in the project
Download data and produce first prototype of the ML model
Experiment with their ML model to improve its performance
Submit the project and share the learnings as a team