The Ultimate Beginners Guide to Machine Learning

The Ultimate Beginners Guide to Machine Learning, available at Free, has an average rating of 4.41, with 22 lectures, based on 93 reviews, and has 3794 subscribers.

You will learn about Learn an initial theoretical basis on some machine learning algorithms Implement simple projects using Orange tool for machine learning tasks such as classification, regression, clustering and association Learn machine learning without knowing a single line of computer programming Use Orange visual tool to create, analyze and test algorithms This course is ideal for individuals who are People interested in starting their studies in Machine Learning or People who want to start a career in Machine Learning or Undergraduate students studying subjects related to Artificial Intelligence or Anyone interested in Artificial Intelligence It is particularly useful for People interested in starting their studies in Machine Learning or People who want to start a career in Machine Learning or Undergraduate students studying subjects related to Artificial Intelligence or Anyone interested in Artificial Intelligence.

Enroll now: The Ultimate Beginners Guide to Machine Learning

Summary

Title: The Ultimate Beginners Guide to Machine Learning

Price: Free

Average Rating: 4.41

Number of Lectures: 22

Number of Published Lectures: 22

Number of Curriculum Items: 22

Number of Published Curriculum Objects: 22

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Learn an initial theoretical basis on some machine learning algorithms
Implement simple projects using Orange tool for machine learning tasks such as classification, regression, clustering and association
Learn machine learning without knowing a single line of computer programming
Use Orange visual tool to create, analyze and test algorithms

Who Should Attend

People interested in starting their studies in Machine Learning
People who want to start a career in Machine Learning
Undergraduate students studying subjects related to Artificial Intelligence
Anyone interested in Artificial Intelligence

Target Audiences

People interested in starting their studies in Machine Learning
People who want to start a career in Machine Learning
Undergraduate students studying subjects related to Artificial Intelligence
Anyone interested in Artificial Intelligence

The area of Machine Learning is currently the most relevant field in Artificial Intelligence, being responsible for the use of intelligent algorithms that make computers learn through databases. The Machine Learning job market in various parts of the world is on the rise and the tendency is for this type of professional to be increasingly in demand! Some studies even indicate that knowledge in this area will soon be a prerequisite for Information Technology professionals!

To take you to this area, in this quick, basic and free course you will have a theoretical and practical overview of some machine learning algorithms using the Orange visual tool, which is one of the easiest tools for those starting learning since no computer programming skills are needed! The course is divided into four parts, which present the main areas of machine learning:

Classification: Naïve Bayes, decision trees, rules, and support vector machines (SVM) algorithms
Regression: linear regression algorithm
Clustering: k-means algorithm
Association rules: – apriori algorithm

This course aims to serve as a basic reference on the main machine learning techniques, especially for beginners in the area who do not have much time to take a longer and more complete course! I will see you in class!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course content

Lecture 2: Course materials

Chapter 2: Classification

Lecture 1: What is classification?

Lecture 2: Naïve Bayes

Lecture 3: Naïve Bayes in Orange

Lecture 4: Decision trees

Lecture 5: Decision trees in Orange

Lecture 6: Rule based learning

Lecture 7: Rules in Orange

Lecture 8: SVM (Support Vectors Machines)

Lecture 9: SVM in Orange

Chapter 3: Regression

Lecture 1: What is regression?

Lecture 2: Linear regression

Lecture 3: Linear regression in Orange

Chapter 4: Clustering

Lecture 1: What is clustering?

Lecture 2: k-means algorithm

Lecture 3: k-means in Orange

Chapter 5: Association

Lecture 1: What are association rules?

Lecture 2: Apriori algorithm

Lecture 3: Apriori in Orange

Chapter 6: Final remarks

Lecture 1: Final remarks

Lecture 2: BONUS

Instructors

Jones Granatyr
Professor
AI Expert Academy
Instructor

Rating Distribution

1 stars: 1 votes
2 stars: 3 votes
3 stars: 11 votes
4 stars: 33 votes
5 stars: 45 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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