Detailed Course Curriculum
Lesson by lesson
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Instructions for course access
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Inter onboarding meeting
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How to use Discussions option
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Course access duration
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Piracy & infringement warning
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1_Introduction
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2_What is Python ?
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3_Is Python really needed ?
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4_Installation
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5_Jupyter Notebook
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6_Your First Code
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7_Lab: Your First Code
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8_Data types
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9_Lab: Data types
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10_Operations
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11_Lab: Operations
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12_Type Conversion
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13_ Lab: Type Conversion
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14_Input statement
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15_Lab: Input statement
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16_IF
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17_else
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18_Lab: If else
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19_While
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20_ break and continue
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21_Lab: While, break and continue
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22_Functions
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23_Lab: Function
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24_for loop
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25_Lab: for loop
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26_Practice
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Week 1 feedback
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1_List
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2_Lab: List
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3_Slicing
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4_Lab: Slicing
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5_Dictionaries
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6_Lab: Dictionaries
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7_Tuples
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8_Lab: Tuples
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9_Exceptions
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10_Module, Package & Library
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11_Lab: Numpy Part: 1
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12_Lab: Numpy Part: 2
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13_Lab: Numpy Part: 3
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14_Lab: Numpy Part: 4
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Dataset for Pandas
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15_Lab: Pandas Part: 1
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16_Lab: Pandas Part: 2
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17_Lab: Matplotlib
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18_pip
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Week 2 feedback
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1. What is Machine Learning ?
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2. Applications of Machine Learning
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3. Steps Involved in Machine Learning
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4. Types in Machine Learning
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5. Types in Machine Learning in Detail
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6. Flow of Machine Learning
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7. Getting Started
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8. Theory: Linear Regression
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9. Lab: Mathematical Approach vs Library Approach
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Dataset for Linear Regression Project
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10. Lab: Linear Regression Project
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11. Types of Curve Fittings
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Week 3 feedback
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1. Theory: Logistic Regression
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Dataset for Logistic Regression Project
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2. Lab: Logistic Regression Project
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3. Theory: Decision Tree
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4. Getting Ready for Decision Tree
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Dataset for Decision Tree Project
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5. Lab: Decision Tree Project
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6. Theory: Random Forest
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Dataset for Random Forest Project
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7. Lab: Random Forest Project
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8. Euclidean Distance
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9. Theory: K- Nearest Neighbor
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Dataset for K- Nearest Neighbor Project
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10. Lab: K- Nearest Neighbor Project
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11. Theory: K Means
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Dataset for K Means Project
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12. Lab: K Means Project
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Week 4 feedback
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Operations Research Dijkstra's Algorithm
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Introduction to Reinforcement Learning
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OpenAI gym installation
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Material transportation in manufacturing facility
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Programming Reinforcement Learning for material transportation 1
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Programming Reinforcement Learning for material transportation 2
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Introduction to Q Learning
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Programming Q Learning algorithm 1
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Programming Q Learning algorithm 2
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Programming Q Learning algorithm 3
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Materials mechanical properties
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Linear Regression model evaluation based on R2 Score
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Programming Linear Regression mechanical properties prediction 1
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Programming Linear Regression mechanical properties prediction 2
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Programming Linear Regression mechanical properties prediction 4
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Results Discussion on material properties prediction at different temperatures
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Material Properties prediction assignment briefing
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Programming Linear Regression mechanical properties prediction 3
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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 above-mentioned 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 6-week 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
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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
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Frequently Asked Questions
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Should I have any prerequisite knowledge to learn this course?
The course will be taught from fundamentals with real-world examples & hands-on sessions. It should not be a concern if you do not have prior exposure to programming/software used.
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Should I need a computer?
Yes, you will need a computer to practice.
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How the teaching will happen?
You will get full access to the course upon the completion of payment.
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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.
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How many projects I will work on?
You will work on 7 hands-on minor & major Projects
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How will I build projects?
Projects will be guided by the mentor with the help of recorded lectures.
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How much time do I have to spend everyday?
We suggest 3 hours per day, an average of 18 hours per week.