Detailed Course Curriculum

Lesson by lesson

    1. Instructions for course access

    2. Inter onboarding meeting

    3. How to use Discussions option

    4. Course access duration

    5. Piracy & infringement warning

    1. 1_Introduction

    2. 2_What is Python ?

    3. 3_Is Python really needed ?

    4. 4_Installation

    5. 5_Jupyter Notebook

    6. 6_Your First Code

    7. 7_Lab: Your First Code

    8. 8_Data types

    9. 9_Lab: Data types

    10. 10_Operations

    11. 11_Lab: Operations

    12. 12_Type Conversion

    13. 13_ Lab: Type Conversion

    14. 14_Input statement

    15. 15_Lab: Input statement

    16. 16_IF

    17. 17_else

    18. 18_Lab: If else

    19. 19_While

    20. 20_ break and continue

    21. 21_Lab: While, break and continue

    22. 22_Functions

    23. 23_Lab: Function

    24. 24_for loop

    25. 25_Lab: for loop

    26. 26_Practice

    27. Week 1 feedback

    1. 1_List

    2. 2_Lab: List

    3. 3_Slicing

    4. 4_Lab: Slicing

    5. 5_Dictionaries

    6. 6_Lab: Dictionaries

    7. 7_Tuples

    8. 8_Lab: Tuples

    9. 9_Exceptions

    10. 10_Module, Package & Library

    11. 11_Lab: Numpy Part: 1

    12. 12_Lab: Numpy Part: 2

    13. 13_Lab: Numpy Part: 3

    14. 14_Lab: Numpy Part: 4

    15. Dataset for Pandas

    16. 15_Lab: Pandas Part: 1

    17. 16_Lab: Pandas Part: 2

    18. 17_Lab: Matplotlib

    19. 18_pip

    20. Week 2 feedback

    1. 1. What is Machine Learning ?

    2. 2. Applications of Machine Learning

    3. 3. Steps Involved in Machine Learning

    4. 4. Types in Machine Learning

    5. 5. Types in Machine Learning in Detail

    6. 6. Flow of Machine Learning

    7. 7. Getting Started

    8. 8. Theory: Linear Regression

    9. 9. Lab: Mathematical Approach vs Library Approach

    10. Dataset for Linear Regression Project

    11. 10. Lab: Linear Regression Project

    12. 11. Types of Curve Fittings

    13. Week 3 feedback

    1. 1. Theory: Logistic Regression

    2. Dataset for Logistic Regression Project

    3. 2. Lab: Logistic Regression Project

    4. 3. Theory: Decision Tree

    5. 4. Getting Ready for Decision Tree

    6. Dataset for Decision Tree Project

    7. 5. Lab: Decision Tree Project

    8. 6. Theory: Random Forest

    9. Dataset for Random Forest Project

    10. 7. Lab: Random Forest Project

    11. 8. Euclidean Distance

    12. 9. Theory: K- Nearest Neighbor

    13. Dataset for K- Nearest Neighbor Project

    14. 10. Lab: K- Nearest Neighbor Project

    15. 11. Theory: K Means

    16. Dataset for K Means Project

    17. 12. Lab: K Means Project

    18. Week 4 feedback

    1. Operations Research Dijkstra's Algorithm

    2. Introduction to Reinforcement Learning

    3. OpenAI gym installation

    4. Material transportation in manufacturing facility

    5. Programming Reinforcement Learning for material transportation 1

    6. Programming Reinforcement Learning for material transportation 2

    7. Introduction to Q Learning

    8. Programming Q Learning algorithm 1

    9. Programming Q Learning algorithm 2

    10. Programming Q Learning algorithm 3

    11. Materials mechanical properties

    12. Linear Regression model evaluation based on R2 Score

    13. Programming Linear Regression mechanical properties prediction 1

    14. Programming Linear Regression mechanical properties prediction 2

    15. Programming Linear Regression mechanical properties prediction 4

    16. Results Discussion on material properties prediction at different temperatures

    17. Material Properties prediction assignment briefing

    18. 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

Course Highlights

  • Duration

    6 Weeks & 30+ hours

  • Mode of delivery

    Online with recorded lectures

  • Applicable for

    This course is open for engineers from all departments of engineering.

  • Projects in this course

    Material Properties Prediction, Aircraft Engine Predictive Maintenance & Path Planning for warehouse robots

  • Prerequisites

    Taught from basics (No prior coding knowledge required)

Software tools used

Algorithms covered

Supervised & unsupervised learning

  • Linear Regression

  • Decision Tree

  • Logistic Regression

  • K-Nearest Neighbour (KNN)

  • Random Forest

  • Euclidian Distance

  • K Means

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

Kickstart the Python learning, and lay a solid foundation as a beginner. We will start with understanding the wide range of applications. Python is being used in Industries, startups & Governments, etc., We shall build a clear motive & motivation to learn the language during this course & the outcomes you can get. You will begin to learn the Python programming with live coding sessions on topics such as operators, data types, Operations in Python (Integers and floats, Strings, Type Conversions & Booleans), Statements and Loops ( IF, IF - else & elif), Functions (Arguments, Return & In-built functions), Loops ( for & while), break & continue.

Practice with examples & gain confidence to start projects

Week 2

During Week 2, we will be exploring the vast and fabulous domains of Intermediate and Advanced Python programming with hands-on coding. You will go through solid theory as well as practical sessions for each new topic to gain confidence & expertise to build the real-world projects ahead of weeks 3 & 4. Topics such as Lists (Operations, Functions, Slices & Comprehensions), Dictionaries, Tuples, Sets, Arrays, Exceptions, Advanced File Handling, Modules & Packages, Libraries in Python (Numpy, Pandas & Matplotlib), Plots & Matrix Operations will be covered during the week 2.

Start to build amazing realtime projects & gain experience

Week 3

Week 3 is dedicated to providing project-based, hands-on learning exposure on the Machine learning topic “Supervised learning”. You will learn & practice 3 projects on concepts such as Linear Regression, Decision Tree & Logistic Regression. We will be taking up projects such as Salary prediction & Advertising, Movie and Budget prediction & Social Network prediction. Week 3 will lay the perfect foundation to begin your interest in projects, you get to set the right approach to problem-solving & you will start to wear the analytical cap to think like a Machine learning engineer. By the end of week 3, you will be confident to use the real-world examples and data with which we can model and predict the required quantities.

Major project, attention to detail & mastery

Week 4

During week 4, we will continue with due concepts of supervised learning, K-Nearest Neighbour (KNN) & Random Forest with Iris flower prediction project. The further part of week 2 will be focused on “Unsupervised learning”. Concepts such as Euclidean's distance & K means models will be taught in detail to gain a clear understanding & application. Week 4 will help you to practice more projects, topics of unsupervised learning & advanced mathematics to gain detailed clarity & confidence. By the end of this 6 weeks program, you will have solid exposure to models of machine learning & the confidence to take the real-time data from any source & solve to build models.

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

Introduction to machine learning for mechanical engineers and applications. Introduction to reinforcement learning (States, Actions, Rewards, Agent) and Q-Learning. Introduction to material movement robots using machine learning. Reinforcement learning parameters definition (Rewards or penalties, state space, Destinations, Actions - (south, north, east, west, pickup material, drop-off material). Programming material movement robots to make decisions on actions to reach the drop-off locations with high rewards and low penalties. Analysis of the machine learning algorithm's performance for different values of several episodes, learning rate, and exploration rate.

Material properties prediction

Week 5, Major Project 2

Introduction to mechanical properties of materials (Stress, Strain, Stress & Strain curve, proportional limit, yield point, Yield point, Ultimate tensile strength, and Fracture point). Programming Linear Regression algorithm to predict proportional limit of the material at a given temperature.

Machine Learning for Aircraft Engine Predictive Maintenance

Week 6, Major Project 3

Week 6, Major Project 3 Introduction to aircraft engine, predictive maintenance, time to failure, introduction to sensors in aircraft engines, and sensor data and data wrangling. Programming machine learning algorithm to predictive time to failure of aircraft engine and analysis of model’s accuracy based on precision and f1-score. Introduction to Machine Learning model evaluation and f1-score accuracy check (precision, recall). Assignment on predictive maintenance to calculate to predict the time to failure of aircraft engine based on a machine learning algorithm.

Reviews from Participants

AKSHAT MISRA

5 star rating

“A unique blend of concise material with good references and hands-on projects, it's much better than courses in Coursera and gives a similar experience as edx with more interactive sessions with the course leads.”

“A unique blend of concise material with good references and hands-on projects, it's much better than courses in Coursera and gives a similar experience as edx with more interactive sessions with the course leads.”

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Ardra V

5 star rating

“I absolutely enjoyed every second of this course! Indeed, it was a good decision from my side to get enrolled. I was able to understand why each and every line of code was important for the entire program to work well. Now, I am quite confident of...”

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“I absolutely enjoyed every second of this course! Indeed, it was a good decision from my side to get enrolled. I was able to understand why each and every line of code was important for the entire program to work well. Now, I am quite confident of my foundation in python. Thank you so much, Decibels for giving me the opportunity!”

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Kousalya Kalaparthi

5 star rating

“A well structured program within the set time frame, helped me gain enough understanding to know about and use Scilab Xcos software, and work on projects which were amply challenging to help me understand about the model, go back and correct my mi...”

Read More

“A well structured program within the set time frame, helped me gain enough understanding to know about and use Scilab Xcos software, and work on projects which were amply challenging to help me understand about the model, go back and correct my mistakes and understand the analysis of the output. What I learnt through this internship serves as the perfect foundation and the skills I've gained will help me work on future projects with confidence. Apart from the technical part itself, you take us through the industry persepctive and what could we be doing to improve ourselves (work on more projects etc), delve more into what we've learnt, what to showcase in CVs which wholly rounded out my experience ”

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Nishidh Naik Burye

5 star rating

“1. Best mentoring 2. Excellent doubt solving 3. Content and videos are extremely good in terms of technology and software-based learning 4. Easy access for LMS and self-scheduling of lectures is one of the best features of this internship ”

“1. Best mentoring 2. Excellent doubt solving 3. Content and videos are extremely good in terms of technology and software-based learning 4. Easy access for LMS and self-scheduling of lectures is one of the best features of this internship ”

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Pratik Tembhurne

5 star rating

“As a student I try to learn something new everyday and this internship offered all the knowledge of MBD and gave hands on experience on Scilab Xcos. Looking forward to use this skills in future.”

“As a student I try to learn something new everyday and this internship offered all the knowledge of MBD and gave hands on experience on Scilab Xcos. Looking forward to use this skills in future.”

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Sabreesh S

5 star rating

“It was great doing this course for 4 weeks and I'm happy that I've learnt so much and frankly it was really interesting, I was waiting for the next class right from the previous day as the way of teaching makes us want to learn more.”

“It was great doing this course for 4 weeks and I'm happy that I've learnt so much and frankly it was really interesting, I was waiting for the next class right from the previous day as the way of teaching makes us want to learn more.”

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Rahul K P -

5 star rating

“The program is well designed and its pretty much useful for guys like me who are interested in Mechatronics like me as we are gonna build more complex models.”

“The program is well designed and its pretty much useful for guys like me who are interested in Mechatronics like me as we are gonna build more complex models.”

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Rahul Sen Gupta

5 star rating

“Loved the deep concepts of drones. It was an amazing experience as always. This was my second internship from Decibels lab”

“Loved the deep concepts of drones. It was an amazing experience as always. This was my second internship from Decibels lab”

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Ananthu T Mani

4 star rating

“The course was wonderfully organized, with keen attention to detail. The topics covered were of sufficient depth for the duration of the course, and the 8th session covering hand calculation was excellent exposure for the attendees. One con howeve...”

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“The course was wonderfully organized, with keen attention to detail. The topics covered were of sufficient depth for the duration of the course, and the 8th session covering hand calculation was excellent exposure for the attendees. One con however is that the videos seem to be very lengthy, and since this was posted as a self paced course, giving shorter videos of 1/2 hour durations would have been much better for attendee satisfaction.”

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Bharadwaz KDS

5 star rating

“At the end of 4 weeks, I had gained an ample amount of knowledge in designing the dynamic control system for a Drone which includes Pitch and Stability controls. Apart from these flight controls, the course covered in-depth knowledge on Transfer F...”

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“At the end of 4 weeks, I had gained an ample amount of knowledge in designing the dynamic control system for a Drone which includes Pitch and Stability controls. Apart from these flight controls, the course covered in-depth knowledge on Transfer Functions, solving real-time problems on Race Car Resistive Forces, Plotting 3D graphs, data visualization, etc. The mentors are well experienced in the field of Electronics and control systems and the way they handled the course deserves appreciation.”

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Shubhangi Balajirao Pimpalgaonkar

4 star rating

“It was a great learning experience. I enjoyed the learning. The projects are also concept-oriented, I learn many new things. Thank you Decibels for such a great internship, it helps me build new skills. ”

“It was a great learning experience. I enjoyed the learning. The projects are also concept-oriented, I learn many new things. Thank you Decibels for such a great internship, it helps me build new skills. ”

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Sample Certificate

Learners will receive a digitally verifiable & LinkedIn shareable certificate by the completion of this course/educational internship with a unique Certificate ID. Decibels Lab Pvt Ltd (Recognized as Start-up by the Department for Promotion of Industry and Internal Trade Ministry of Commerce & Industry Government of India) (Certificate Number: DIPP45372)

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 real-world examples & hands-on 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 hands-on 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.