Supervised Machine Learning Principles and Practices-Python

Supervised Machine Learning Principles and Practices-Python, available at $19.99, has an average rating of 4.35, with 24 lectures, based on 18 reviews, and has 382 subscribers.

You will learn about Understand the mathematics behine Machine Learning Supervised Machine Learning Models such as Decision Tree, Support Vector Machine, k-Nearest Neighbor, Linear Regression etc. Python Code for Supervised learning models Creating a ML model and solving for a given set of data. This course is ideal for individuals who are Bachelor and Master Degree students or Machine Learning Programmers It is particularly useful for Bachelor and Master Degree students or Machine Learning Programmers.

Enroll now: Supervised Machine Learning Principles and Practices-Python

Summary

Title: Supervised Machine Learning Principles and Practices-Python

Price: $19.99

Average Rating: 4.35

Number of Lectures: 24

Number of Published Lectures: 24

Number of Curriculum Items: 24

Number of Published Curriculum Objects: 24

Original Price: $124.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the mathematics behine Machine Learning
Supervised Machine Learning Models such as Decision Tree, Support Vector Machine, k-Nearest Neighbor, Linear Regression etc.
Python Code for Supervised learning models
Creating a ML model and solving for a given set of data.

Who Should Attend

Bachelor and Master Degree students
Machine Learning Programmers

Target Audiences

Bachelor and Master Degree students
Machine Learning Programmers

In this course, we present the concept of machine learning and the classification of different methods of learning such as Supervised and Unsupervised Learning. We also present reinforcement learning. We offer popular techniques and implement them in Python. We begin with the Decision Tree method. We present this simply with all the required mathematical tools such as entropy. We implement them in Python and explain how the accuracy can be improved. We offer the classification problem with a suitable real-life scenario. Linear Regression is taught using simple real-life examples. We present the L2 Error estimation and explain how we can minimize the error using gradient optimization. This is implemented using the Python library. We also offer the Logistic Regression method with an example and implement in Python. The Nearest Neighbourhood approach is explained with examples and implemented in Python. Support Vector Machines (SVM) are a popular supervised learning model that you can use for classification or regression. This approach works well with high-dimensional spaces (many features in the feature vector) and can be used with small data sets effectively. When trained on a data set, the algorithm can easily classify new observations efficiently. We also present a few more methods. The Bayesian model of classification is used for large finite datasets. It is a method of assigning class labels using a direct acyclic graph. The graph comprises one parent node and multiple children nodes. And each child node is assumed to be independent and separate from the parent. As the model for supervised learning in ML helps construct the classifiers in a simple and straightforward way, it works great with very small data sets. This model draws on common data assumptions, such as each attribute is independent. Yet having such simplification, this algorithm can easily be implemented on complex problems.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Learning by Observation

Lecture 2: Learning Agents

Chapter 2: Forms of Learning

Lecture 1: Forms of Learning – Inductive Learning

Chapter 3: Inductive Learning Methods

Lecture 1: Supervised Learning

Lecture 2: Unsupervised Learning

Lecture 3: Reinforcement Learning

Chapter 4: Decision Tree Model

Lecture 1: Introduction to Decision Trees

Lecture 2: Decision Tree Construction Algorithm

Lecture 3: Mathematical Constructs for Decision Tree – Entropy, Remainder and Info gain

Lecture 4: Decision Tree Code using sklearn – Syntax explained

Lecture 5: Decision Tree – Python Lab

Lecture 6: Decision Tree Testing the Model Python Lab

Chapter 5: Linear Regression

Lecture 1: Linear Regression – Gradient Descent – Concept and Algorithm

Lecture 2: Linear Regression – Gradient Descent – Multivariate

Lecture 3: Writing Python code using Skilearn

Lecture 4: Linear Regression – Python with Skilearn Practical Demonstration

Chapter 6: Data Preprocessing and Normalization

Lecture 1: Data Normalization

Lecture 2: Data Preprocessing and Normalization

Lecture 3: Data Normalization in Python

Lecture 4: Data Preprocessing Python Lab

Chapter 7: Logistic Regression

Lecture 1: Classificiation Problem and Logistic Regression

Lecture 2: Logistic Regression Python Lab

Chapter 8: K – Neareest Neighbour Method of Learning

Lecture 1: K NN – Mathematical Model

Lecture 2: KNN for Handwritten Digit Recognition Lab

Instructors

Xavier Chelladurai
Professor

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 3 votes
4 stars: 9 votes
5 stars: 5 votes

Frequently Asked Questions

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