Machine Learning Value Package + Guaranteed Internship

Learn complete Python and Machine learning, build projects and start your Machine Learning Career

Created By: Niranjan Kumar

Course Duration : 2 Months (Lifetime Content Access)


549 Ratings | 4786 Enrollments


  • Lifetime access to course material

  • Hard & Soft copy of certificate of completion

What will I learn?

  • Complete knowledge of python

  • Build 4 real life projects using Machine Learning

  • Complete understanding of using the latest cutting edge ML & AI algorithms

  • Ability to use the best algorithm on the basis of the problem statement provided



The whole world has been in constant change since the release of computers and the internet! They used to be an extra hand to the capabilities of the human mind since the last couple of decades.

Recently, with the advances in the power of computers and ease of access to the large amount of information, it has been made possible to run special algorithms on these tufts of data!

How Machine learning works – With the availability of a tremendous amount of DATA and the ease in accessing them, it is possible to make the computers learn to take decisions. Once we have enough data, ML algorithms are employed based on this data and we get nearly accurate results through prediction. As more data is accumulated, the accuracy improves. Accurate algorithms are then created and data-driven predictions are obtained. You will see in details how this actually happens during this course.

This course gives you COMPLETE know how of Python & Machine Learning, and hence making you are confident enough to launch your career in the world of ML and AI.

For any doubts or concerns please contact +91-9266677335.

Once you complete the course and all the assignments you will be granted a soft and hard copy of the completion certificate.  

If you are working professional and wants to enhance your skills, we will provide you an experience letter once you complete the course and have worked on the machine learning projects

Details related to Guaranteed Internship:

i) Why? - We believe acquiring a new skill without working on an actual project is wasteful spending of your time and resources. For your holistic learning experience, both course and internship should be combined.

ii) Who? - An Internship is guaranteed by Eckovation to all Eckovation Alumni - those who complete at least a course with us.

iii) Where? - The Internship will be either with Eckovation or one of our partner organizations. You will be able to access once you register for a course with us. Our Internship Portal will feature profiles of our alumni and also list internship opportunities by various organizations where alumni can apply.

iv) How many? - The internship will be related to the skill(s) which you've acquired from Eckovation platform.  In case of multiple skills, multiple internship opportunities may be provided.

v) Nature of internship? - The internships will be virtual in nature, i.e., you will be able to work on the internship project from home.

vi) When? - The Internship will start only after your course completion in either Summer/December Vacation depending on which comes first. In-semester internships can also be considered on case-by-case basis.

vii) Certificate - A separate internship certificate will be provided at the end of the internship period.


Python Programming Language

  • All the concepts required for Machine Learning

Introduction to Machine Learning

  • Introduction

Supervised and Unsupervised Learning

  • What is Supervised Learning?

  • What is Unsupervised Learning?

Linear regression

  • Simple Linear Regression

  • Multiple Linear Regression

  • Assumptions of Linear Regression

  • Python Implementation

  • Applications of Linear Regression

Logistic regression

  • Introduction

  • Difference b/w linear and logistics regression

  • Logistics Equation

  • Assumptions

  • Python Implementation

  • ROC Curve

Polynomial regression

  • Introduction

  • Limitations

  • Python Implementation

Multivariate regression

  • Introduction

  • Difference b/w Multiple Regression and Multivariate Regression

  • Problem Statement

  • Solution

  • Step wise Python Implementation

Decision Tree

  • Introduction

  • Construction

  • Representation

  • Assumptions

  • Python Implementation

Random Forest

  • Introduction

  • Bootstrap Aggregation

  • Bagging

  • Problem Statement

  • Python Implementation

  • Conclusion

Support Vector Machines

  • Introduction

  • Tuning Parameters: Kernels, Regularization, Gamma, Margin

  • Python Implementation

  • Conclusion

Principal Component Analysis

  • Introduction

  • Feature Elimination

  • Feature Extraction

  • When to use PCA

  • Working of PCA

  • Python Implementation

Linear Discriminant Analysis

  • Introduction

  • Need for LDA

  • Representation of Model

  • How to make predictions from a learned LDA

  • Python Implementation

Hierarchical Clustering

  • Single-link and complete-link clustering

  • Time complexity of HAC

  • Group-average Agglomerative clustering

  • Centroid clustering

  • Optimality of HAC

  • Divisive clustering

  • Cluster labeling

  • Python Implementation

k-means clustering

  • Introduction

  • Business Uses

  • Algorithm

  • Python Implementation

k-mode clustering

  • Introduction

  • Notations

  • Algorithm

  • Python Implementation

Naive Bayes Classification

  • What is Naive Bayes algorithm?

  • How Naive Bayes Algorithms works?

  • What are the Pros and Cons of using Naive Bayes?

  • Applications of Naive Bayes Algorithm

  • Steps to build a basic Naive Bayes Model in Python

  • Tips to improve the power of Naive Bayes Model

Deep Learning Concepts

  • Neural network Introduction

  • Tensorflow installation

  • Convolution Network

Capstone Project

  • Build your industry grade, resume ready project

About Instructors

Niranjan Kumar

BITS Pilani, 8y+ experience

A BITS Pilani graudate, with experience of over 8 years.

Over last couple of years, he has been associated with the top companies like Oracle, and eBay.

He is a proficient software architect, with deep experience in building highly scalable systems distributed online systems.

He has in-depth working knowledge of technologies like Machine Learning system, MEAN stack and many more.


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How do I enroll in the course?

Click on the Enroll Now button on the top of the page. Then select the suitable package for yourself. Then you will be asked to complete the necessary payment. Once you complete the process, you automatically get enrolled for the course.

What are the modes of payments available?

You can make the due payment via netbanking, debit cards, credit cards or online wallet.

Can cash payment be done for courses?

Cash payment facility is not available. Only online transactions are accepted.

What happens after I complete the payment for the course?

You will receive an email confirming the success of subscription and welcoming you to the course. You will be asked to join a learning group on Eckovation corresponding to the course that you have opted.

Can I get a free trial for the course?

It varies from course to course. Some courses have a free trial available, some courses do not have that feature.

Can I pursue the course in laptop as well as mobile?

Yes. You can pursue the course over your laptop by going to . Smartphone users can download the eckovation app from appstore or playstore and login to access your course.

What is the Refund Policy in case I'm not satisfied with the Course?

100% Refund Policy is applicable till 7 days after subscription in case we are not providing what we have promised you earlier. However, after 7 days, no request for refund will be entertained.

I am unable to access the online course. Who should I contact?

You can write an e-mail to You can also contact your course educator or you can call at +91-9266677335.

Is it required for you to complete the course strictly within the course duration mentioned at the top of the page?

No. You can complete the course before or after the stipulated course duration. It is mentioned just to provide a tentative timeline in case you devote 1-2 hours/day to the course regularly. Infact, you'll also have lifetime access to course material.

Is there any Pre-requisite for this Course?

No, there's no pre-requisite for this course. Everything will be covered in the course, right from the scratch.