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)

529 Ratings | 4786 Enrollments

Includes

  • 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

Includes

  • 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

Testimonials

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Description

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

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

These algorithms are practically, parallel human minds, which are able to "think" and compute stuff! This is artificial intelligence!

How Machine learning works – With the availability of 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, AI 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 in this course.

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

For any doubts or concerns please contact Mr. Ritesh (9711608586)

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

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? - Internship is guaranteed by Eckovation to all Eckovation Alumni - those who complete atleast a course with us.

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

Curriculum

Download Curriculum

  • All the concepts required for Machine Learning

  • What is Supervised Learning?

  • What is Unsupervised Learning?

  • Simple Linear Regression

  • Multiple Linear Regression

  • Assumptions of Linear Regression

  • Python Implementation

  • Applications of Linear Regression

  • Introduction

  • Difference b/w linear and logistics regression

  • Logistics Equation

  • Assumptions

  • Python Implementation

  • ROC Curve

  • Introduction

  • Limitations

  • Python Implementation

  • Introduction

  • Difference b/w Multiple Regression and Multivariate Regression

  • Problem Statement

  • Solution

  • Step wise Python Implementation

  • Introduction

  • Construction

  • Representation

  • Assumptions

  • Python Implementation

  • Introduction

  • Bootstrap Aggregation

  • Bagging

  • Problem Statement

  • Python Implementation

  • Conclusion

  • Introduction

  • Tuning Parameters: Kernels, Regularization, Gamma, Margin

  • Python Implementation

  • Conclusion

  • Introduction

  • Feature Elimination

  • Feature Extraction

  • When to use PCA

  • Working of PCA

  • Python Implementation

  • Introduction

  • Need for LDA

  • Representation of Model

  • How to make predictions from a learned LDA

  • Python Implementation

  • 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

  • Introduction

  • Business Uses

  • Algorithm

  • Python Implementation

  • Introduction

  • Notations

  • Algorithm

  • Python Implementation

  • 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

  • Neural network Introduction

  • Tensorflow installation

  • Convolution Network

  • 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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