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Monday, May 27, 2019
Random Forest In R | Random Forest Algorithm | Random Forest Tutorial |Machine Learning |Simplilearn
Random Forest In R | Random Forest Algorithm | Random Forest Tutorial |Machine Learning |Simplilearn
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This Random Forest in R tutorial will help you understand what is Random Forest, how does a Random Forest work, applications of Random Forest, important terms to know and you will also see a use case implementation where we predict the quality of wine using a given dataset. Random Forest is an ensemble Machine Learning algorithm. Ensemble methods use multiple learning models to gain better predictive results. It operates building multiple decision trees. To classify a new object based on its attributes, each tree is classified, and the tree “votes” for that class. The forest chooses the classification having the most votes (over all the trees in the forest). Now let us get started and understand what is Random Forest and how does it work.
Below topics are explained in this Random Forest in R tutorial :
1. What is Random Forest? ( 00:50 )
2. How does a Random Forest work? ( 02:48 )
3. Applications of Random Forest ( 05:17 )
4. Use case: Predicting the quality of the wine ( 07:48 )
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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 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 knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
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
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
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 modelling.
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 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 neighbours, 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
Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Random-Forest-in-R-HeTT73WxKIc&utm_medium=Tutorials&utm_source=youtube
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