Hands on Workshop on Machine Learning with Python

Department of Information Technology successfully organized a One Day Hands-on Workshop on “Data Science & Machine Learning with Python” in association with Coding Ninjas, Rohini, New Delhi on Saturday, 12th October 2019. Prof.(Dr.) Neelam Sharma Director and Prof. (Dr.) M.L. Sharma, HOD IT, MAIT addressed the gathering and explained the necessity of such Hands on Workshop by sharing their valuable views.

 

Mr. Parikh Jain, Data Scientist, Software Developer and Software Professional from Coding Ninjas conducted the session. He is  CS graduate from DTU and has 2+ years of teaching experience. He is known as the Ninja of Competitive Coding and Python. Moreover, he holds a superb contribution towards our Online learning portal, CodeZen.

 He explained the basics of Python programming, Machine Learning with Python, the necessary functionality and its implementation. Coding Ninjas Group. distributed certificates at the end of the session.

 

The session proved to be very helpful for faculty and students alike.

 

The event was coordinated by Dr. Anu Rathee, Assistant Professor, Mr. Sachin Garg, Assistant Professor, Department of Information Technology. Over 70 students and more than 30 Assistant Professors, Associate Professors participated with great enthusiasm and were also awarded with the Participation Certificates by Prof. (Dr.) M.L. Sharma, (HOD IT), MAIT. All the students and faculty members are extremely grateful to Management for conduction of this workshop.

 

Schedule Of Workshop

 

 

Highlights of the workshop:

  • Fundamentals of ML
  • Python Programming
  • Data Processing
  • Linear Regression
  • Uni-variate Vs Multivariate
  • Gradient Descent Algorithm
  • Hyper parameters Grid Search
  • Classification Problem

 

 

 

 

 

 

Time

Programme

9.00-10.00

Registration

10.00-10.15

Inaugural Session

10.15-10.30

Introduction to Machine Learning

10.30.11.00

Tea Break

11.00-1.00

Machine Learning & its Applications, Python Programming

1.00- 2.00

Lunch Break

2.00-4.00

Coefficent of Determination, Prediction through Graphs, Approach towards Best Solutions