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Introduction to Machine Learning: Teach Machines to Learn From Examples

Learn artificial intelligence and machine learning basics, and the ways they are revolutionising the world
Course from BitDegree
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The fundamental concepts of AI and applications of machine learning
Different classifications and regression techniques
Clustering, including of k-means and k-nearest neighbours
Bias/variance to improve your machine learning model
Meaning of the decision trees & regression analysis

Instead of following exact instructions and commands, machines are now taught to learn-by-example and make decisions. Machine learning (ML) is a part of a bigger concept called artificial intelligence (AI). The differences between these two terms are subtle, but it is obvious that AI is the more general term. In this introduction to machine learning, you will learn many techniques and terms that are related to the education of machines. 

In ML, developers act as tutors for computers. Imagine that you want to learn to play basketball. You might spend some time reviewing footage from older games. Sooner or later, you will start coming up with various strategies and understanding the rules of the game. 

The same applies to ML. Developers can attempt to use supervised or unsupervised learning. Want to know more? This course explains all machine learning basics in great detail and gives you examples of how machines begin to recognize patterns. 

Introduction to machine learning 

In this machine learning tutorial, you will learn the definition of this broad field of study. On the surface, ML can be known as the process of applying tools and techniques to teach machines to learn from experience. On a deeper level, ML consists of different types of learning, and its application has already revolutionized many industries (for instance, finance, marketing, data analytics). 

After learning the machine learning basics, you might be unaware that you are encountering ML engines daily. For instance, many websites aim to customize content for their users. Therefore, their engines slowly learn from your behavior, visited websites, and ads you interact with. Slowly but surely, such engines customize the offers you see. Therefore, sometimes it is enough to type several letters, and the website seems to have read your mind. 

Current impressive applications of ML 

Machine learning for beginners might be difficult to grasp. How can a car drive itself? How can large volumes of data help us predict the future? How can a person you have never met find out about you simply by taking your picture? 

The last example refers to a small tech company that has created a groundbreaking facial recognition app. With such possibilities up its sleeve, machine learning is definitely the topic you should be studying if you want to become an in-demand employee. 

What will you find in this introduction to machine learning?

In this machine learning tutorial, we will cover the introduction to machine learning first. I will discuss the main concepts that you should recognize as well. Another lesson focuses on making a graphical representation of the way machines make decisions (decision trees). Further on, I will give you tips on choosing the right algorithm for your ML project. 

For instance, if you are using labeled data, it would be best to choose supervised learning algorithms. On the other hand, when you deal with unlabeled data, it would be best to select unsupervised strategies. 

This introduction to machine learning also addresses the most common issues and challenges that people have. For this course, you do not need to have any programming experience since we won’t be working with actual algorithms. After all, this is only an introduction that takes the approach of machine learning for beginners. 

Introduction to Machine Learning: Teach Machines to Learn From Examples
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