Algorithmic Introduction To Machine Learning

A Conceptual Framework

What you'll learn

Understand the process of preprocessing data

Understand the working behind various machine learning algorithms

Use different evaluation measures and decode confusion matrix

Use different machine learning techniques to design AI machine and enveloping applications for real world problems.

Requirements

Basic knowledge of data structures is required

Description

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to “self-learn” from training data and improve over time, without being explicitly programmed. Machine learning algorithms are able to detect patterns in data and learn from them, in order to make their own predictions. This course is intended for people who wish to understand the functioning of popular machine learning algorithms. This gives a behind the scene look of who things are working. We will start by looking at some data pre-processing techniques, then we will move on to look at supervised and unsupervised learning algorithms. Finally, we will look at what cross valuation is and how it is done.In this course we will look at: Data Preprocessing [Handling Missing Values, Data Encoding (Conversion of Categorical Data into Nominal Data), Data Normalization] Supervised Learning[Linear Regression, Decision Tree Regression, Decision Tree Classification, Naive Bayes Classification, K Nearest Neignbour Classification] Model Evaluation [Evaluation of Classifiers, Deciding Confusion Matrix] Unsupervised Learning [K Means Clustering, Hierarchical Clustering] Model Improvement [Cross Validation]By the end of this course, you will have a thorough understanding of how these machine learning algorithms function which will in turn enable you to develop better ML models.

Overview

Section 1: Introduction

Lecture 1 Course Introduction

Lecture 2 Intro to Machine Learning

Section 2: Data Preprocessing

Lecture 3 Handling Missing Values

Lecture 4 Data Encoding

Lecture 5 Data Normalization

Section 3: Supervised Learning

Lecture 6 Linear Regression - I

Lecture 7 Linear Regression - II

Lecture 8 Decision Tree Regression

Lecture 9 Decision Tree Classification

Lecture 10 Naive Bayes Classification

Lecture 11 KNN Classification

Lecture 12 Model Evaluation

Section 4: Unsupervised Learning

Lecture 13 Clustering Introduction

Lecture 14 KMeans Clustering

Lecture 15 Hierarchical Clustering

Section 5: Cross Validation

Lecture 16 Cross Validation

Beginners desirous of understanding how machine learning algorithms work