Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us selfdriving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards humanlevel AI.
Learn about tomorrow’s technology today with our 13 weeks Machine Learning Course at IIT Madras Research Park, Chennai
This 13 weeks classroom training on Machine Learning will help participants to learn from basics of Machine Learning, understand the math behind algorithms and get handson 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.
Highlights
 Designed for Working professionals & Engineering students
 Classroom Program with expert trainers capable of making learningtechnology simple
 Work on over 15+ realtime data sets
 A seamless transition between theoretical concepts and practical handson
 Continuous Assessment and Mentorship
Skills that you will learn
 Python programming
 Clear understanding of ML algorithms & math behind them
 Data cleaning and PreProcessing of numerical and text data
 Predictive Analytics and statistics
What you get to learn & work with?
Week 1
Fundamentals of AI & ML
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
Classification Problem
Logistics Regression
– Classification problem
– Data Preprocessing
– Defining a Model
– Error & Accuracy Calculation
– Gradient Descent Algorithm
– Prediction
Problem: Handwritten Digit Recognition using Gradient Descent Algorithm
Week 3
Multivariate Linear Regression & Evaluation Measures
– Introduction to Scikit Learn
– Label Encoding
– Data Preprocessing
– Gradient Descent Algorithm, TNC
– Regularization Parameter
– Hyperparameter Grid Search
– Bias, Variance, Accuracy, Precision
Problem: Chennai housing price prediction using scikit learn
Week 4
K Nearest Neighbor
– What is KNN?
– Example KNN Problem
– Defining the Objective function
– Optimize Objective function
– Prediction & Accuracy
Regression Problem:
Chennai housing price prediction
Classification Problem: Tshirt size Prediction
Week 5
Naive Bayes
– Bayes Theorem
– Naive Bayes Algorithm?
– Example Naive Bayes problem
– Defining the Objective function
– Optimize Objective function
– Prediction & Accuracy
Problem: Building a spam classifier
Week 6
Decision Tree
Random Forest
– What is Decision Tree?
– Calculating Entropy
– Calculating Information gain
– Random Forest & Bagging
– Random Forest Vs Decision Tree
– Pruning
Decision Tree Problem:
Depending on the weather predicting the possibility of playing cricket
Random Forest Problem:
Behavioral Risk factor Surveillance System
Week 7
Ensembles
– What is Gradient Boosting?
Xgboost:
– Algorithmic Enhancements
– System Optimization
– Defining the Objective function
– Optimize Objective function
– Prediction & Accuracy
Adaboost:
– Define Objective function
– Optimize Objective function
– Prediction & Accuracy
Problem:
Diabetics Prediction
Week 8 & 9
Support Vector Machines
– What is SVM?
– Intuition Behind SVM
– Defining the Objective function
– Optimize Objective function
– Prediction & Accuracy
Problem:
Solving MNIST dataset
Week 10
Project 1
Solving Different Datasets
Week 11
K Means Algorithm
– Clustering Problem
– KMeans Algorithm
– KMeans for nonseparated clusters
– KMeans Optimization objective
– Random Initialization
– Choosing the number of clusters
Problem:
Movie Recommendation
Week 12
Neural Networks
– What is Neural network?
– Defining a Model
– Back Propagation Algorithm
– Classification Problem
Problem:
Solving Fashion MNIST dataset
Week 13
Project 2
Solving Different Datasets
Who is this program for?

Engineers

Software/IT/Data Professionals

Engineering students/Professors

Hobbyists
Minimum Eligibility

Should be proficient in at least one of the programming languages (C/C++/Python/Java/R)

Should be ready to commit to rigorous training and learning
Class Timings

Total duration – 13 Weeks

Class Timings: 9.30 AM to 4.30 PM
 Lunch & Refreshments will be provided
Program Fee
Rs.25,000/
Fee Inclusions
 13 Weeks of Training @ IIT Madras Research Park
 Resource materials – (Book, Pen, Notebook, Datasets, and Software)
 Lunch & Refreshments
 Certification
 GST
Fee Structure
Online Advance Reg. Fees: Rs.2000/
Balance fees to be paid in 2 installments
 1st Installment – on the 1st day of the batch
 2nd Installment – on 5th week of the batch
Upcoming Batches
Mar 01st – May 24th, 2020
(Sundays)
Timings: 9.30 AM to 4.30 PM
Venue
IIT Madras Research Park
Have some queries?
Please call us at +91 9677048653 / +91 8056603335
Or Leave the message below. We will reach out to you 🙂