Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving 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 human-level 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 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.

Highlights

  • Designed for Working professionals & Engineering students
  • Classroom Program 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
Z

Skills that you will learn

  • Python programming
  • Clear understanding of ML algorithms & math behind them
  • Data cleaning and Pre-Processing 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: T-shirt 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
– K-Means Algorithm
– K-Means for non-separated clusters
– K-Means 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 🙂

 

12 + 5 =

Share This
Open chat
1
Hello,
How we can help you today?
Powered by