Detailed Course Curriculum
Lesson by lesson


Instructions for course access

Inter onboarding meeting

How to use Discussions option

Course access duration

Piracy & infringement warning



1_Introduction

2_What is Python ?

3_Is Python really needed ?

4_Installation

5_Jupyter Notebook

6_Your First Code

7_Lab: Your First Code

8_Data types

9_Lab: Data types

10_Operations

11_Lab: Operations

12_Type Conversion

13_ Lab: Type Conversion

14_Input statement

15_Lab: Input statement

16_IF

17_else

18_Lab: If else

19_While

20_ break and continue

21_Lab: While, break and continue

22_Functions

23_Lab: Function

24_for loop

25_Lab: for loop

26_Practice

Week 1 feedback



1_List

2_Lab: List

3_Slicing

4_Lab: Slicing

5_Dictionaries

6_Lab: Dictionaries

7_Tuples

8_Lab: Tuples

9_Exceptions

10_Module, Package & Library

11_Lab: Numpy Part: 1

12_Lab: Numpy Part: 2

13_Lab: Numpy Part: 3

14_Lab: Numpy Part: 4

Dataset for Pandas

15_Lab: Pandas Part: 1

16_Lab: Pandas Part: 2

17_Lab: Matplotlib

18_pip

Week 2 feedback



1. What is Machine Learning ?

2. Applications of Machine Learning

3. Steps Involved in Machine Learning

4. Types in Machine Learning

5. Types in Machine Learning in Detail

6. Flow of Machine Learning

7. Getting Started

8. Theory: Linear Regression

9. Lab: Mathematical Approach vs Library Approach

Dataset for Linear Regression Project

10. Lab: Linear Regression Project

11. Types of Curve Fittings

Week 3 feedback



1. Theory: Logistic Regression

Dataset for Logistic Regression Project

2. Lab: Logistic Regression Project

3. Theory: Decision Tree

4. Getting Ready for Decision Tree

Dataset for Decision Tree Project

5. Lab: Decision Tree Project

6. Theory: Random Forest

Dataset for Random Forest Project

7. Lab: Random Forest Project

8. Euclidean Distance

9. Theory: K Nearest Neighbor

Dataset for K Nearest Neighbor Project

10. Lab: K Nearest Neighbor Project

11. Theory: K Means

Dataset for K Means Project

12. Lab: K Means Project

Week 4 feedback



Operations Research Dijkstra's Algorithm

Introduction to Reinforcement Learning

OpenAI gym installation

Material transportation in manufacturing facility

Programming Reinforcement Learning for material transportation 1

Programming Reinforcement Learning for material transportation 2

Introduction to Q Learning

Programming Q Learning algorithm 1

Programming Q Learning algorithm 2

Programming Q Learning algorithm 3

Materials mechanical properties

Linear Regression model evaluation based on R2 Score

Programming Linear Regression mechanical properties prediction 1

Programming Linear Regression mechanical properties prediction 2

Programming Linear Regression mechanical properties prediction 4

Results Discussion on material properties prediction at different temperatures

Material Properties prediction assignment briefing

Programming Linear Regression mechanical properties prediction 3

About this course
 124 lessons
 27 hours of video content
 Mentor guided projects
 4 course rating & 1800+ learners
Software tools used
About the course
Machine Learning & Artificial Intelligence (ML & AI) is used in Mechanical engineering sectors such as Product development (Generative shape design, Complex FEA & FVM problem solving, Design optimization), Process efficiency improvement (quality control, utilization, demand & supply prediction), Predictive maintenance (Industrial machines, automotive, aerospace, etc.), Material behavior prediction, Application of subjects such as Operational research & management & many more.
Mechanical engineers are preferred for the abovementioned job opportunities. Skill sets are not only limited to data or the approach to ML/AI But the knowledge of mechanical domains is very important to make sense of the data that we get. Such job roles are offered to engineers who possess skills & exposure to ML/AI & also the core domain.
This 6week online course on ML for Mechanical engineers is outlined to enable learning possibilities for everyone. The content of this course is well organized, easy to learn, and structured with 7 minor projects to understand the concepts of ML & 3 major projects dedicated to the Mechanical engineering stream. By the end of 6 weeks, you will have the confidence to code in Python, learn to use many libraries, understand the math behind ML for supervised & unsupervised learning, and you will transform the theoretical knowledge into practical skills with projects & assignments. You do not need to have any background in coding to learn this course.
Start with Python Fundamentals & motivation to master Python
Week 1
Practice with examples & gain confidence to start projects
Week 2
Start to build amazing realtime projects & gain experience
Week 3
Major project, attention to detail & mastery
Week 4
Application of ML in decision making for material handling robots in a warehouse (This project is an application of Operation Research/Operation Management subject)
Week 5, Major Project 1
Material properties prediction
Week 5, Major Project 2
Machine Learning for Aircraft Engine Predictive Maintenance
Week 6, Major Project 3

Machine Learning for Mechanical Engineers
₹2,999.00
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Reach us for any queries  7411019255 / [email protected]
Sample Certificate
Participants posts on LinkedIn


Frequently Asked Questions

Should I have any prerequisite knowledge to learn this course?
The course will be taught from fundamentals with realworld examples & handson sessions. It should not be a concern if you do not have prior exposure to programming/software used.

Should I need a computer?
Yes, you will need a computer to practice.

How the teaching will happen?
You will get full access to the course upon the completion of payment.

What should I do, if I get a doubt?
You need to report the doubts/issues over the Discussion box available in the course. The course coordinator will clarify your doubts by replying via the discussion box. If needed, we will arrange meetings with the mentor, to clarify your doubts via Zoom.

How many projects I will work on?
You will work on 7 handson minor & major Projects

How will I build projects?
Projects will be guided by the mentor with the help of recorded lectures.

How much time do I have to spend everyday?
We suggest 3 hours per day, an average of 18 hours per week.