Supervised vs Unsupervised vs Reinforcement Learning | Machine Learning Tutorial | Simplilearn
In this video, you will learn about Supervised vs Unsupervised vs Reinforcement Learning. You will understand the definition of each of these learning techniques and look at the various algorithms that are part of these methods. You will see the approach and the learning methods used in the training process. Finally, you will learn the various applications of Supervised, Unsupervised, and Reinforcement Learning.
Below topics are explained in this video:
0:29 Types of Machine Learning
✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH
⏩ Check out the Machine Learning tutorial videos: https://bit.ly/3fFR4f4
#SupervisedvsUnsupervisedvsReinforcementLearning #TypesofMachineLearning #SupervisedLearning #UnsupervisedLearning #ReinforcementLearning #MachineLearningTutorial #MachineLearningTutorialForBeginners #MachineLearning #Simplilearn #MachineLearningCourse
About Simplilearn Machine Learning course:
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 as 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.
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
👉Learn more at: https://bit.ly/3fouyY0
For more updates on courses and tips follow us on:
– Facebook: https://www.facebook.com/Simplilearn
– Twitter: https://twitter.com/simplilearn
– LinkedIn: https://www.linkedin.com/company/simplilearn
– Website: https://www.simplilearn.com