Algorithmic Introduction To Machine Learning

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


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