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