Industry recognized certification enables you to add this credential to your resume upon completion of all courses

Need Custom Training for Your Team?
Get Quote
Call Us

Toll Free (844) 397-3739

Inquire About This Course
Dr. Yogesh Kulkarni, Instructor - Hands-on Project - Data Preparation, Modeling & Visualization

Dr. Yogesh Kulkarni

16+ years in CAD/Engineering software development, in various capacities, including R & D group and site manager. Recently finished a PhD in Geometric Modeling. Currently working as a Data Analytics consultant, in areas such as Natural Language Processing, Text Mining, Machine Learning and Deep Learning.

Hands-on Projects, Including Data Preparation, Modeling & Visualization Tasks.

  • Implement real-life machine learning workflows.
  • Hands-on projects, including data preparation, modeling & visualization tasks.
  • Instructor holds a Ph.D. in Geometric Modeling and works in areas such as NLP and Deep Learning.

Course Description

As part of completion of Machine Learning track, students are suggested to do one complete hands-on project. This course provides 4 problems, out of which, one can be selected for implementation, preferably in python, scikit-learn and matplotlib. Given ppt template needs to be filled with answers got from running the python program. Students must submit their projects to the following email address: [email protected] within a month from enrollment date.

What am I going to get from this course?

Implement real-life machine learning workflows including data acquisition, preparation, modeling and visualization.

Prerequisites and Target Audience

What will students need to know or do before starting this course?

Machine Learning algorithms like Regression and Classification. Python and associated libraries such as Numpy, Scikit-Learn and Matplotlib.

Who should take this course? Who should not?

Students, as part of completion of Data Science and Machine Learning tracks. Others, familiar with Machine Learning can also benefit from this course.


Module 1: Capstone Project

Lecture 1 Introduction

Idea and the process of Capstone project

Lecture 2 Problems

Datasets and problem definitions are presented. One of them needs to be chosen for solution.

Lecture 3 Workflow

Slides here are to be filled in with the answers you will get after running your solution. Each slides has questions, which need to be filled in, in the same slide. You will need to send the project to the following email address [email protected]