loader image

Machine Learning Course – Online

Learn ML from basics with live training sessions

Machine Learning Course – Online

Learn ML in the new normal way.

Predicting the future isn’t magic, it’s artificial intelligence!

Are you looking for the best Online Course on Machine Learning with trainer interaction?

We have made our Machine Learning Course available online now. This will help participants to learn from basics of Machine Learning, understand the math behind algorithms and get hands-on experience working on ML algorithms. This curriculum was developed in association with HyperVerge. At regular intervals, problem statements will be given for the participants to solve and gain more insights into algorithms.

The course conists of 13 weeks of Live interactive training sessions helping you to learn, work & specialize on ML algorithms.

Join now to learn ML & enhance your hands-on skills with our Online Classes.

Course Highlights

  • Designed for working professionals, engineering students & faculties.
  • Online live classes with expert trainers capable of making learning-technology simple.
  • Work on over 15+ real-time data sets.
  • A seamless transition between theoretical concepts and practical hands-on.
  • Continuous Assessment and mentorship.

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
  • Software/ IT / Data Professionals
  • Engineering students/Professors
  • Hobbyists

Minimum Eligibility

  • Should have knowledge on at least one of the programming languages (C / C++/ Python / Java / R).
  • 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)

Upcoming Batches

Weekend Batch starts on

Oct 31st, 2020 (Sat & Sun Only)

Timing: 9 am to 12 pm

Weekdays Batch starts on

Nov 23rd, 2020 (Mon, Wed & Fri Only)

Timing: 5 pm to 7 pm

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

Only 20 slots per batch. Get yours before it gets filled!

Feedback from our ML Course Classroom program

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

Rating for the training session from the participants

Open chat
How we can help you today?