Roboticist. I am currently working at Fraunhofer institute. A lot of people helped me during my journey. I want to do the same and help othe

Poulastya Mukherjee

This course covers basics of Machine learning and AI.

English

Machine Learning - Part 1

Introduction - 1

Introduction - 2

Concept Learning - 1

Concept Learning - 2

Decision Trees

Decision Trees Algorithm

Avoiding Overfitting

How to estimate a hypothesis using binomial and normal distributions

Hypothesis Estimation - 1

Hypothesis Estimation - 2

Introduction to Linear Regression

Linear Regression Analysis

Logistics Regression Basics

Bayesian Learning Introduction to concepts and formulas

Bayesian Learning Introduction

Bayesian Learning Analysis

Bayesian Learning Analysis Continued

Bayesian Learning Application

Basic concepts of Artificial Neural Networks

Artificial Neural Network Introduction

Weight tuning in Artificial Neural Networks

Artificial Neural Network Formula and Concepts

Artificial Neural Network Hidden Layer Calculation

Artificial Neural Networks Advanced Concepts

Applications of Artificial Neural Networks

Gaussian Estimation and Learning Model

Gaussian Estimation Continued

Instance Learning Basics

Instance Based Learning part II

Basic Concepts of Rule Based Learning

Rule Based Learning Formulas

Applications of Inductive Learning

Genetic Algorithm Basic

Genetic Algorithm Formulas

Genetic Algorithm Applications and Use Cases

Genetic Algorithm Applications

Lessons

31 lessons • 3h 47m

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Introduction - 1

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Introduction - 2

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Concept Learning - 1

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Concept Learning - 2

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Decision Trees

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Decision Trees Algorithm

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Avoiding Overfitting

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Hypothesis Estimation - 1

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Hypothesis Estimation - 2

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Introduction to Linear Regression

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