Students, Working Professionals seeking a career in ML
This course is the perfect place for beginners to understand the core idea of building systems that have the ability to automatically learn from data and improve the experience without being explicitly programmed. In this course, you will learn about concepts of Machine Learning, effective machine learning techniques, and gain practice implementing them and getting them to work for yourself all in a classroom program.
The course will be mentored & guided by Industry experts having hands-on experience in ML-based industry projects. The course includes 1 major & 3 minor projects based on real-world applications with guided lab sessions.
Pre-requisites:
Should know the basics of any one programming language
Hands-on on python.
Basic syntax and data structures in python
Introduction to data frames, numpy, pandas and other libraries in python.
Introduction to ML
Types of problems solved by ML.
Real world scenarios where ML is applicable.
Basic Terminology used in ML via a case study.
Building Regression models like Linear Regression, its variants etc.
Evaluating regression models, evaluation metrics, cost function etc.
Introduction to classification techniques.
Logistic Regression, Decision Trees, Random Forests.
Evaluating Classification Models.
Clustering and Similarity techniques
Other clustering Algorithms :
K-means Algorithm
Apriori Algorithm etc
Introduction to Deep Learning
Best Practices while designing Machine Learning Solutions
Closing Remark
| Batch | Date | Type | Register |
|---|---|---|---|
| ML-Batch-1 | 29 June to 28 July '19 | Weekend Classes | Registration Closed |
| ML batch-2 | 21 December to 19 January '20 | Weekend Classes | Registration Closed |
