Machine Learning Algorithms and Applications

Machine Learning Algorithms and Applications, available at Free, has an average rating of 4.75, with 9 lectures, based on 4 reviews, and has 850 subscribers.

You will learn about understand the basics of machine learning using probability theory implement machine learning models using supervised learning algorithms implement machine learning models using unsupervised learning algorithms implement machine learning models for sequential data analysis and prediction This course is ideal for individuals who are This course is ideal for students, professionals, and anyone interested in entering the field of machine learning or No prior experience in machine learning is required, but familiarity with basic math concepts will be beneficial. It is particularly useful for This course is ideal for students, professionals, and anyone interested in entering the field of machine learning or No prior experience in machine learning is required, but familiarity with basic math concepts will be beneficial.

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Summary

Title: Machine Learning Algorithms and Applications

Price: Free

Average Rating: 4.75

Number of Lectures: 9

Number of Published Lectures: 9

Number of Curriculum Items: 9

Number of Published Curriculum Objects: 9

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

understand the basics of machine learning using probability theory
implement machine learning models using supervised learning algorithms
implement machine learning models using unsupervised learning algorithms
implement machine learning models for sequential data analysis and prediction

Who Should Attend

This course is ideal for students, professionals, and anyone interested in entering the field of machine learning
No prior experience in machine learning is required, but familiarity with basic math concepts will be beneficial.

Target Audiences

This course is ideal for students, professionals, and anyone interested in entering the field of machine learning
No prior experience in machine learning is required, but familiarity with basic math concepts will be beneficial.

This course provides a comprehensive learning in the field of machine learning, covering fundamental, advanced concepts, techniques, and applications. The course will guide students through the basics of machine learning algorithms, data preprocessing, model evaluation, and deployment. Students can learn the differences between supervised, unsupervised, and reinforcement learning, and how they are applied in real-world scenarios. Awareness of key machine learning algorithms, including linear regression, clustering, support vector machines, and mixture models, is provided. In depth knowledge on the role of probability in classification, regression, and clustering and the various mathematical functions behind them is discussed in detail. The various aspects of improving model performance and how to evaluate models using various metrics and optimize their performance are explained. Students can discover a wide range of machine learning applications using the knowledge gained over the course. This course is ideal for students, professionals, and anyone interested in entering the field of machine learning. No prior experience in machine learning is required, but familiarity with programming and basic math concepts will be beneficial. All concepts are explained with real time examples, and problems are solved to understand the applications in the real world. More content will be added in the future to go with a deep dive into machine learning.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to Machine Learning

Lecture 2: Supervised Learning

Lecture 3: Unsupervised Learning

Lecture 4: Polynomial Curve Fitting

Lecture 5: Probability Theory – Basic Rules

Lecture 6: Probability theory – Conditional Probability and Bayes Theorem

Lecture 7: Probability Theory – Continuous Random Variables

Chapter 2: Linear Models for Regression

Lecture 1: Maximum Likelihood Estimation – Least Square Method

Lecture 2: Robust and Ridge Regression

Instructors

Dr. Poornima Selvaraj
Instructor at Udemy

Rating Distribution

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4 stars: 2 votes
5 stars: 2 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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