Network Security

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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Gain An Insight Into Machine Learning

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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

Introduction To Machine Learning

Learning Methods In Machine Learning

Supervised Learning: Introduction And Different Approaches

Unsupervised Learning: Introduction, Conditions, Developments And Advantages

Questions On Supervised Learning

Questions On Unsupervised Learning

Cost Function: Introduction

Cost Function: Advanced

Gradient Descent: Introduction And Mathematical Representation

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Questions On Gradient Descent

Somesh Mohapatra

Gain An Insight Into Machine Learning

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