Study Materials:

## Introduction to Machine Learning

## Top 10 Applications of Machine Learning

## Different Types of Machine Learning

## Supervised Learning

## Hypothesis Space and Inductive Bias

## Evaluation and Cross-Validation

## Linear Regression

## Decision Tree

## Learning Decision Tree

## Overfitting

## K-nearest neighbour

## Feature Selection

## Feature Extraction

## Collaborative Filtering

## Bayesian Learning

## Bayesian Network

## Logistic Regression

## Support Vector Machine (SVM)

## SVM : The Dual Formulation

## SVM: Maximum Margin with Noise

## Non-linear SVM and Kernel Function

## SVM: Solution to the Dual Problem

## Deep Neural Network

## Multilayer Neural Network

## Iris Dataset Prediction

## Convolutional Neural Networks Architecture and Applications

## Artificial Neural Network (ANN)

## Artificial Neural Network Applications in the Real World

## Recurrent Neural Networks – Deep Learning Fundamentals

## XGBoost in Machine Learning – Features & Importance

## GBoost Algorithm – Applied Machine Learning

## AdaBoost Algorithm For Machine Learning

## Agglomerative Hierarchical Clustering

## Linear Algebra: Vector Spaces, Subspaces, Orthogonal Matrices, Quadratic Form

**For Books → ML Books**

**For Interview Question And Answer → ML - Question**

**Programming Code → Programming Codes**

**Project → ML - Project**

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