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Machine Learning


30 hours of live instructor-led & interactive training

Upcoming batch | 23 Oct (Sat & Sun)

About The Course

Learn in-demand skills on Data Science and Machine Learning from fundamentals. Work with python programming and industry standard tools such as pandas, matplotlib, scikit and numpy. Work on 10+ real time projects. Start your ML & AI journey with this crash course.

Reasons why you should consider


Instructor-led & Interactive

You will learn from expert trainers who are capable of making learning tech simple, engaging & fun.


Individual attention & Inspiring peers

You will get individual attention from trainers & you get to network with peers from industries.


Most affordable premium course

You get the learning experience of a premium course at a pocket friendly cost.


Active trainer support

Trainers will ensure complete understanding of concepts & help with doubts actively.


Work on 10+ real time problems

You will work on 10+ real world problems. Trainers will assist you with the projects.


IIT Madras Incubated Startup

We are an IIT Madras Incubated startup founded by Alumni of IIT Madras under the guidance of faculty members from IITM.

Course Curriculum

Topics Covered during Live Sessions

Week 1: Linear Regression & Gradient Descent Algorithm

  • What is Artificial Intelligence
  • Machine Learning & Types
  • Fundamentals of Python
  • Data Preprocessing in python
  • Defining a Model
  • Error Calculation
  • Gradient Descent Algorithm

Problem: Predicting Housing Prices based on the size of the house

Week 2: Logistics Regression & Classification

  • Classification problem
  • Data Preprocessing
  • Defining a Model
  • One vs. All
  • Error & Accuracy Calculation
  • Gradient Descent Algorithm
  • Prediction

Problem: Handwritten Digit Recognition

Week 3: Decision Tree & Random Forest

  • What is Decision Tree?
  • Calculating Entropy
  • Calculating Information gain
  • Gini Index
  • Objective Function
  • What is Random Forest & Bagging?
  • Advantages of using Random Forest
  • Pruning
  • Objective Function
  • Prediction & Accuracy

Decision Tree Problem: Depending on the weather predicting the possibility of playing cricket
Random Forest Problem: Behavioral Risk factor Surveillance System

Week 4: Naive Bayes

  • NLP Basics
  • N Gram Model, Bag of Words
  • TF-IDF Vectorisation
  • Bayes Theorem
  • Multinomial & Bernoulli Naive Bayes

Problem: Building a spam classifier

Week 4: Boosting Techniques

  • What is Gradient Boosting?
  • Adaboost | Xgboost 
  • Algorithmic Enhancements
  • System Optimization
  • Objective function
  • Prediction & Accuracy

Problem: Indian Pima Diabetics Prediction

Week 5: Support Vector Machine (SVM)

  • What is SVM?
  • Intuition Behind SVM
  • Defining the Objective function
  • Optimize Objective function
  • Kernels
  • Prediction & Accuracy
  • Sliding Window technique
  • Optimisation

Problem: Solving MNIST dataset, Facial Recognition

Self Learning Content

K Nearest Neighbour & SMOTE

  • What is KNN?
  • Example KNN Problem
  • Defining the Objective function
  • Optimize Objective function
  • Prediction & Accuracy

Problem: Online shoppers’ buying intention

Neural Networks

  • What is Neural network?
  • Defining a Model
  • Back Propagation Algorithm
  • Classification Problem

Problem: Solving Fashion MNIST dataset

Unsupervised Learning & K Means Clustering

  • K Means Clustering
  • Hierarchical Clustering
  • K Means for non-separated clusters
  • Principle Component Analysis

Problem: Movie Recommendations

Hours Live Instructor-led training

Hours Self Learning Content

Course is designed with 30 hours of live interactive hands-on training with trainers to help you learn ML from fundamentals and end of that you will gain very clear intuition on ML models.

You also get 12+ hours of self learning content to learn and work with more concepts in Machine Learning.

Work on 10+ Real Time Projects

Months guidance on projects

During the course, you will work on 10+ real world problems and solve it using machine learning algorithms.

We will also guide you on any projects that you are interested in working after the completion of the course for the next 4 months.


tools used in ML course

Course Details


Who can join?

College students

Faculty members

Working Professionals


Course Duration

Weekend: 5 Weeks

Saturdays @ 5 PM to 8 PM

Sundays @ 10 AM to 01 PM

Course Fees



Inclusive of 18% GST

Upcoming Batch

Starts on October 23rd (Sat & Sun)

Refer a Friend & Save Rs.500

Join along with a friend and save Rs.500 on the program fee! Limited time offer!

Share this course details with friends in your network!


Frequently Asked

Should I have any prior knowledge to join?

Not necesarrily. You will learn python programming during the course from basics. However basic knowledge on anyone of the programming language would help.

I'm a beginner in ML. Can I join?

Yes. This course is designed considering all the participants are beginners. It starts from fundamentals and at the end of the course you will be an expert in solving ML problems.

How to register for this program?

You can register online through our website by paying the workshop fee and get your slot.

How should I pay the course fee?

You can pay the course fee online using the register now button.

What if I miss a class due to emergency?

It is recommended not to miss any class. However if you miss any class due to unavoidable reasons, you can compensate that with the recorded session videos.

Will I get any support even after the course?

Yes. You can get in touch with our trainers & mentors for any projects & competitions. We would be happy to help!

How can we get the guidance?

You can reach out to our trainers through the Whatsapp group & emails.

Do you provide any internships?

You can get project internship at Lema Labs. On successful completion of projects you will get a Letter of Internship / Recommendation.

Still have any questions or need assistance?

You can reach out to us at support@lemalabs.com or call us at +91 8056603335

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