Top University Professor

**This course includes**

- 54 hours on-demand video
- Full lifetime access
- Access on mobile and TV

**What you'll learn**

- Hypothesis Space and Inductive Bias
- Evaluation and Cross-Validation
- Linear Regression
- Learning Decision Tree
- Python Exercise on Decision Tree and Linear Regression
- and Much MUch More!!

**Description**

In this course we will have a quick introduction to machine learning and this will not be very deep in a mathematical sense but it will have some amount of mathematical trigger and what we will be doing in this course is covering different paradigms of machine learning and with special emphasis on classification and regression tasks and also will introduce you to various other machine learning paradigms. In this introductory lecture set of lectures I will give a very quick overview of the different kinds of machine learning paradigms and therefore I call this lectures machine learning. )

A brief introduction with emphasis on brief right, so the rest of the course would be a more elongated introduction to machine learning right.

**Curriculum**

__Course A__

Week 1:

Introduction

Different types of Learning

Hypothesis Space and Inductive Bias

Evaluation and Cross-Validation

Week 2:

Linear Regression

Introduction to Decision Trees

Learning Decision Tree

Overfitting

Python Exercise on Decision Tree and Linear Regression

Week 3:

k-Nearest Neighbour

Feature Selection

Feature Extraction

Collaborative Filtering

Python Exercise on kNN and PCA

Week 4:

Bayesian Learning

Naive Bayes

Bayesian Network

Python Exercise on Naive Bayes

Week 5:

Logistic Regression

Introduction Support Vector Machine.

SVM _ The Dual Formulation

SVM _ Maximum Margin with Noise

Nonlinear SVM and Kernel Function

SVM _ Solution to the Dual Problem

Python Exercise on SVM.

Week 6:

Introduction.

Multilayer Neural Network

Neural Network and Backpropagation Algorithm

Deep Neural Network

Python Exercise on Neural Network

Week 7:

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Week 8:

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Week 9:

Agglomerative Hierarchical Clusteringd

Python Exercise on kmeans clustering

__Course B__

Week 1:

Machine Learning

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Week 2:

Probability Basics

Probability Basics part 2

Week 3:

Linear Algebra

Linear Algebra part 2

Week 4:

Statistical Decision Theory - Classification

part two

Bias Variance

Week 5:

Linear Regression

Multivarite Regression

Week 6:

Subset Selection

Subset Selection part 2

Shrinkage Methods

Principal Components

Partial least squeres

Week 7:

Sampling

Linear Classification

Student

Logistic Regression

Sample

Linear Discrinimant

Confidence intervals

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