192 rank in JEE Mains, CBSE 12th topper (97.8%). Completed B Tech in Material Science from IIT Roorkee in 3.5 years

Somesh Mohapatra

Machine Learning is the field of study that gives the computer the ability to learn without being explicitly programmed, as defined by Arthur Samuel. This collection focuses on the understanding of Machines in the day to day life and a brief explanation of its different types: Supervised Learning, Unsupervised Learning, Reinforced Learning, and Hebbian Learning. The further lessons provide with the description of Supervised and Unsupervised learning: Introduction, Different Approaches, the conditions that need to be checked, the outputs that are expected, the developments required and its various advantages. Somesh also tests the concepts by providing us with the different type of questions for us to have a better understanding. The collection moves forward with the overview of the cost function and its hypothesis, parameters and goal. A complete lesson is dedicated to the advancement to the Cost Function modelling and uses an appropriate model to describe how the cost function is applied to a real life problem. The last two lessons provide with the introduction and mathematical representation of Gradient Descent and also conceptual knowledge is put to test by solving certain questions. 

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A very informative lesson on the importance of Machine Learning in the day to day's life: where all machines are used and why they play such a vital role in our lives. Thereby it tells us theoretical definitions of a Machine by different scientists with the help of further explanations and examples. 

Introduction To Machine Learning

This lesson deals with the basic idea of Learning, explaining the requirement and the idea of the concept. It throws light on the basic idea of Supervised Learning, Unsupervised Learning, Reinforced Learning, and Hebbian Learning. It is to induce the need for such a system and how it might help.

Learning Methods In Machine Learning

This method of learning is important for the machine to be able to understand human attributes and then relate to algorithms and systems in a pattern as required. This lesson starts with a basic introduction to Supervised Learning and further explanation of its different types: Error Correction Gradient Descent, Stochastic Approach, Least Main Square Approach, and Backpropagation Approach. 

Supervised Learning: Introduction And Different Approaches

This lesson talks of areas where Machine needs to analyse the systems by itself and learn accordingly. This is necessary to let the independence of the Machine and the choices such that human intervention is minimum. Here, Somesh throws light on the basic introduction of Unsupervised learning, the conditions that need to be checked, the outputs that are expected, the developments required and its various advantages. 


Unsupervised Learning: Introduction, Conditions, Developments And Advantages

This lesson begins with the type of questions that can be asked under Supervised Learning. Thereby the lesson discusses some basic questions and supervised learning method problems with the help of illustrations before further getting into the Learning Algorithms.


Questions On Supervised Learning

This lesson begins with the type of questions that can be asked under Unsupervised Learning. Thereby the lesson discusses some basic questions and Unsupervised learning method problems with the help of illustrations before further getting into the Learning Algorithms.


Questions On Unsupervised Learning

This lesson deals with the hypothesis, parameters and goal of the cost function. It is there to understand the need of the Cost Function and relate to the fact that how important is the modelling of the Cost Function according to the System parameters.
 

Cost Function: Introduction

This lesson provides a quick recap to cost function introduction. Thereby it throws light on the advancement to the Cost Function modelling and uses an appropriate model to describe how the cost function is applied to a real life problem.


Cost Function: Advanced

This lesson begins with an explanation of the concept Gradient Descent and its mathematical representation. It also throws light on the graphical representation of linear variables along with the explanation on which gradient descent actually depend on. 

Gradient Descent: Introduction And Mathematical Representation

This lesson is used primarily to close the quarters of Linear Regression, Cost Function and Gradient Descent using a question-answer approach. It will help you in applying the conceptual knowledge and get a strong hold over them by solving these questions. 


Questions On Gradient Descent

Currently pursuing B.Tech in the field of computer science. Exploring the world of Artificial Intelligence

Palash Sharma

In this course, the educator discusses the concepts of cryptography and also the fundamentals required to understand the various security involved with computing and networking devices.

Cryptography and Cyber Security: GATE (CS and IT)

Mentored 81,000+ Students | My Multiple Students secured AIR Below 100 in GATE | Joined top IISc, IITs & NITs | Joined PSUs | 5+ Years Exp.

Sweta Kumari

In this course , I am covering the entire Syllabus of network Security through the expected network security GATE Questions.

(Hindi) Previous Year GATE Questions On Network Security

B.tech graduate in Computer Science & Engineering.
Experienced in Java, Sql, Mannual & Automation Testing. 

Suraj Singh Mehta

In this course we will learn about Network connection, Network Security and Network Protocol.

(Hindi) Computer Network Part-1 for GATE

UG Student at IIT Roorkee |Sports Freak| Data Analyst|  Blogger 

Pravesh Sharma

This course will give everyone an insight into machine learning. Many of the people around usually heard this word but they don't know about it and this course will help them a lot

Introduction to Machine Learning for Data science

In this particular course, our motive is to learn multiple choice questions related to network security important for GATE Exam.

(Hindi) Network Security - 40 Objective Questions

Undergrad @ IIIT Gwalior. Machine Learning enthusiast. Qualified JEE Mains and Advanced. Gate Aspirant.
Excellent orator.

Surya Pratap Singh

In this course, we will study about various Routing Algorithms used in Networking layer in a simple and lucid way.

Lessons

10 lessons • 1h 6m

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Introduction To Machine Learning

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Learning Methods In Machine Learning

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Supervised Learning: Introduction And Different Approaches

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Unsupervised Learning: Introduction, Conditions, Developments And Advantages

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Questions On Supervised Learning

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Questions On Unsupervised Learning

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Cost Function: Introduction

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Cost Function: Advanced

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Gradient Descent: Introduction And Mathematical Representation

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Questions On Gradient Descent