Supervised and Unsupervised Learning In Machine Learning | Machine Learning Tutorial | Simplilearn


This video on “Supervised and Unsupervised Learning” will help you understand what is machine learning, what are the types of Machine learning, what is supervised machine learning, types of supervised machine learning, what is unsupervised learning, types of unsupervised learning and what are the differences between supervised and unsupervised machine learning. In supervised learning, the model learns from a labeled data whereas in unsupervised learning, model trains itself on unlabelled data. Now, let us get started and understand supervised and unsupervised learning and how they are different from each other.

Below are the topics explained in this supervised and unsupervised learning in Machine Learning Tutorial-
1. What is Machine Learning
– Types of Machine Learning
– Supervised Learning
– Unsupervised Learning
2. Supervised Learning
– Types of Supervised Learning
3. Unsupervised Learning
– Types of Unsupervised Learning

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A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in Machine Learning.

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Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
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What skills will you learn from this Machine Learning course?

By the end of this Machine Learning course, you will be able to:

1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire a thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems

We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning

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  1. Do you have any questions on this topic? Please share your feedback in the comment section below and we'll have our experts answer it for you. Thanks for watching the video. Cheers!

  2. You mentioned fraud detection as a case of unsupervised learning in first video of this series and now in this video it is mentioned as a case of supervised learning

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