OBJECTIVE OF THE COURSE:
Manufacturing industries are in the process of digitalization of their units and plants. Firms are interested in leveraging the data from the plants to gain insights on plant operations and make informed decisions based on it. The objective of the course is to provide fundamental understanding of data analytics and machine learning techniques for manufacturing and chemical process industries.
ELIGIBILITY CRITERIA:
There is no pre-requisite for any particular educational background. However, it is expected that the participants should have basic understanding of mathematical concepts
Experience in any programming language, preferably python is a plus but not mandatory.
COURSE FEES(INR):
Students (Bachelors, Masters, PhD scholars): 1500/- + 18% GST per person
Faculty, Postdoc: 3000/- + 18% GST per person
Industry Participants: 15000/- +18% GST per person
A special 10% discount for a group of 3 to 10 participants
Special Scheme for Academia (students, faculty and
postdocs): Free registration for 1 participant with a group
of 11 participants (including self) & 10% discount to all 10
participants + letter of appreciation to the institute/
department.
IMPORTANT DATES:
Registration starts on: 05-08-2023.
Registration closes on: 22-09-2023.
Course starting date: 23-09-2023.
Enrollment PROCESS:
Click Here to Sign-up with your Email
Provide Full Name, Email and Password to Sign-up
And OTP will be sent to your registered Email
Use that OTP to verify your Email address
Use your Email and password to Sign-in
For Individual enrollment, click on "Enroll For Course" page, for group of participants enrollment, click on "Group Enrollment" page
Select Department as Chemical Engineering.
Select Course as "Machine Learning: Fundamentals & Applications"
For individual enrollment, provide necessary details and click on button "Submit and Proceed to Pay"
If it is group enrollment, select number of participants and provide partcipants name and provide remaining details and click on button "Submit and Proceed to Pay"
Pay for the course through Payment Gateway and you will be successfully enrolled for the course
HAVE QUSTIONS? CONTACT US
For any queries on the registration and course related matters contact:
Dr. Venkata Reddy P
Mob: 9940330878
Email: venkat_palleti.che@iipe.ac.in
Dr. Hemanth Kumar T
Mob: 9445420495
Email: hemanth.che@iipe.ac.in
Prof. Seshagiri Rao A
Email: seshagiri.che@iipe.ac.in
For any queries on the registration and course related matters contact:
Dr. Venkata Reddy P
Mob: 9940330878
Email: venkat_palleti.che@iipe.ac.in
Dr. Hemanth Kumar T
Mob: 9445420495
Email: hemanth.che@iipe.ac.in
Prof. Seshagiri Rao A
Email: seshagiri.che@iipe.ac.in
Summary
| Course Status : | Registrations Started |
| Course Duration : | 13 Weeks |
| Course Timings : | 2hours-Every Saturday (6.00-8.00 P.M.) |
| Delivery Mode : | Online (Cisco-WebEx Meetings) |
| Level : | Undergraduate/Postgraduate/Industrial |
| Registration Start Date: | 05 Aug 2023 |
| Registration End Date: | 22 Sep 2023 |
| Classes Start Date : | 23 Sep 2023 |
Topics Covered
| No. of hours | Topic and contents |
|---|---|
| 2 | Introduction to Data science, Data analytics, Types of Data and Data analytics |
| 2 | Descriptive Statistics: Frequency tables, Bar charts, Univariate and Multivariate Descriptive statistics |
| 2 | Linear Algebra: Data matrix, Vectors, Matrices, Rank, Eigen values and Eigen vectors, Singular Value Decomposition |
| 2 | Introduction to Machine Learning, Relation between AI, ML and DL, Approaches, Supervised, Unsupervised, classification, Function approximation, Machine Learning Workflow. |
| 2 | Supervised Learning-Linear regression |
| 2 | Supervised Learning-Logistic regression |
| 2 | Unsupervised Learning: Use cases, K-means clustering |
| 2 | Interpretability Analysis - SHAP, ALE |
| 2 | Introduction to Python, Control structures, Functions, Pandas library, Scikit-learn library. |
| 2 | Hands-on Session-Linear regression |
| 2 | Hands-on Session-Logistic regression |
| 2 | Hands-on Session-K-means clustering |
| 2 | Capstone Project: Problem Definition, Description and Ideas to solve, Discussion |