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Machine Learning Course – Online

Learn ML from basics with live training sessions

Machine Learning Course – Online

Learn ML in the new normal way.

This course is designed for anyone who is interested in Machine Learning but not sure how to get started.

All the concepts will be taught from the fundamentals level to advanced level with practical implementation at every stage. There are no prerequisites and the course is designed in a way that every participant will master the skills irrespective of their background by solving over 15+ real time problems.

The course consists of 13 weeks of Live instructor-led & interactive training sessions helping you to learn, work & specialize on ML algorithms. You will also get support to solve real world problems.

The curriculum was developed in association with HyperVerge

Join now to learn ML & enhance your hands-on skills.

Course Highlights

  • Designed for working professionals, executives, engineering students & faculty members
  • Live Classes with 1:1 support
  • 13 weeks comprehensive course starting with fundamentals
  • Work on over 15+ real-time data sets
  • Support for solving real world problems
  • Curriculum designed in association with Hyperverge

Skills you’ll learn

  • Develop skills required to be an ML Developer
  • Python programming.
  • Clear understanding of ML algorithms & math behind them.
  • Data cleaning and Pre-Processing of numerical and text data.
  • Predictive Analytics and statistics.
  • Fine-tuning performance of algorithms
  • Become equipped to develop ML Models for real-time Business-use cases


Week 1: Linear Regression

– 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 using Gradient Descent Algorithm

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 using Gradient Descent Algorithm

Week 3: Multi variate Linear Regression

– Introduction to Scikit Learn
– Label Encoding
– Data Preprocessing
– Gradient Descent Algorithm, TNC
– Regularization Parameter
– Hyperparameter Grid Search
– Bias, Variance, Accuracy, Precision

Problem: Solve Kaggle Datasets

Week 4: K Nearest Neighbour

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

Problem: Online shoppers’ buying intention.

Week 5: 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 6: Naive Bayes

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

Problem: Building a spam classifier

Week 7: Ensembles

– What is Gradient Boosting?


– Algorithmic Enhancements
– System Optimization
– Objective function
– Prediction & Accuracy


–  Objective function
– Prediction & Accuracy

Problem: Indian Pima Diabetics Prediction

Week 8 & 9: Support Vector Machine (SVM)

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

Problem: Solving MNIST dataset, Facial Recognition

Week 10: Assessment & Project

– Telecom Customer Churn Prediction
– Insurance Claim Fraud Detection
– Gold Price Prediction
– Credit Card Fraud Detection
– Natural Scene Text Detection
– IDB – Income Qualification Prediction

Week 11: Neural Networks

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

Problem: Solving Fashion MNIST dataset

Week 12: Unsupervised Learning & K Means

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

Problem: Movie Recommendations

Week 13: Projects

– Capstone Project

– Kaggle Competitions

– Carrer Guidance & Mentorship

Who can attend?

  • Engineers & Executives
  • Software/ IT / Data Professionals
  • Engineering students/Professors
  • Hobbyists


  • No Prerequisites. All programming & math concepts will be taught from basics.
  • Should be ready to commit to rigorous training and learning

How Classes will happen?

  • Our trainers will take live classes online
  • Weekdays – 2 hours each session on 3 days a week for 13 weeks.
  • Weekends – 3 hours each session & 2 session per week for 13 weeks.
  • Assignments, Handouts / Quiz will be done through our e-Learning portal

Course Fees

Rs. 17,000/-

(Fee is inclusive of 18% GST)

Early Bird Offer

Register before 20th Dec 2020 & get Rs.2000/- discount

Upcoming Batches

Weekend Batch starts on

Jan 23rd, 2021 (Sat & Sun Only)

Fee Inclusions
  • 13 weeks of online training
  • Access to our e-Learning portal
  • Project Guidance & Mentorship
  • Certification
  • 18% GST
Infrastructure Requirements
  • Good Internet Facility
  • Windows or Mac with decent specification

Limited slots per batch. Get yours before it gets filled!

Hear from our learners

“Honestly, I didn’t have a lot of expectations when I signed up for the course, but things changed after the 1st two sessions. Just as it was picking up the momentum, we all were hit by the pandemic and we had to turn to the online option after a brief hiatus. I was skeptical as I wasn’t sure how effective the classes would be. To my surprise again, the classes were made so interesting that I didn’t find it difficult to follow at all. The course starts with the basics laying a strong foundation on AI & ML including topics on stats, python, mathematics before getting into the details. I find this course a definite eye opener. 

A special shout-out to Pawan who takes his job very seriously. I’m totally impressed. You can get instructors who are knowledgeable but this guy goes way beyond that. He ensures that all your questions are answered and no one in the class is left behind. 

I would strongly recommend Pawan and this course of course, to anyone who is interested in learning ML. “


Head - QA Practice, Mastech Info Trellis

“ This ML course is a complete package that one wants to take up and add the skillset to your resume. Although I have done course in Udemy, this course no way near comparable to it. The fine blend of the math and code is a pure bliss and the assignments of basic python just gives you a head start to understand the code used inside the course. The partial offline and online due to pandemic didn’t affect much rather reduced the travel burden for me, but the class quality was not compromised at all. Everyone at the end of this course can easily and confidently say that you can solve ML problems on your own. Happy coding guys… “

Abhishek Karthik

Full Stack Developer, Sanmina

“ I took Lema Labs Machine Learning program in March 2020, just before the pandemic, and I would wholeheartedly say I was very impressed. In the midst of lots of online options, Lema Labs stands out and excels in their interactive content delivery and truly personalised attention to each and every participant. I personally feel very energised interacting with their staff’s radiating enthusiasm, passion to teach and willing to go extra mile to help participants. 

I highly recommend Lema Labs programs for people who want interactive learning and friendly atmosphere to learn. Their commitment to not just meet expectation, but go over and beyond makes them unique and special. “

Rajesh Rajendran

Chief Business Officer, GNR Power Pvt Ltd

How well participants were able to understand the concepts explained by the trainer

Rating for the training session from the participants

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