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Machine Learning Using Python

Course Code: XLT-0006

FREE ACCESS

  •   Access to course content
  •   No access to labs
  •   Certificate not included

PAID ACCESS

  •   Access to course content
  •   Access to all course labs
  •   Blockchain Enabled Certificate and Badges

About This Course

Machine learning is a topic which is leading the trend in the transformation of organisations from digitisation to data driven. This course aims to expose the learner to the code underpinnings of constructing and assessing a subset of the machine learning tasks, namely the classification, time series and clustering/feature extraction tasks. The objective is to target commonly encountered tasks in the space of financial data analysis. Participants will be able to gain familiarity in such tasks and have a deeper understanding of tackling them.

This course should be attended by those are who are keen to explore the challenges in dealing with finance and machine learning. Applicable to students, working professionals and PMETs.

Schedule

Quarter 2: June 27-28
Quarter 3: September 12-13
Quarter 4: December 19-20

Requirements

This course expects that learners come in a basic understanding of python, from there we will build upon their understanding and teach from a data point of view.

What you'll learn

In this course, you will learn how to:
  • Construct analytics models/results as part of solutions to address business problems
  • Evaluate the performance of analytics models
  • Analyse the results or outputs of analytics models
  • Evaluate the importance of features which are used
  • Understand how to reduce features

Course Staff

Course Staff Image #1

Renuga Devi

Renuga is a highly accomplished and dynamic international trainer. She holds a Bachelor of Technology in Information Technology degree from Anna University, Chennai, following which she is awarded a Master of Engineering in Computer Science and Engineering degree from the same university.

She has shouldered responsibilities in making an effective contribution to teaching and learning in various disciplines, modelling teamwork and flexibility to ensure pedagogical and commercial success. She has the ability to apply innovative teaching methods to encourage trainees’ learning objectives and stay abreast of developments within new technologies to improve curriculum, develop new research.

Renuga has published various papers in the international journal. Her efforts and excellence have been acknowledged and awarded at various dignified platforms and forums. She has received the “Smart India Hackathon Award” and Best Result Producer Award”.

She is currently working as a data science trainer with Xaltius and teaches corporate professionals, students and PMETs in the areas of Programming, Data Science and Analytics.


Course Details

  1. Classes Start

  2. Estimated Effort

    Quarter 2: June 27-28, Quarter 3: September 12-13, Quarter 4: December 19-20
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