Random Forest Tutorial | Random Forest in R | Machine Learning | Data Science Training | Edureka

(Data Science Training -)
This tutorial from Edureka Random Forest will help you understand all the basics of Random Forest's machine learning algorithm. This tutorial is ideal for both beginners and professionals who want to learn or improve their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial:

1) Introduction to Classification.
2) Why Random Forest?
3) What is random forest?
4) Random forest use cases
5) How does the random forest work?
6) Demo in R: Diabetes prevention use case

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How does it work?

1. There will be 30 hours of interactive classes led by an instructor, 40 hours of homework and 20 hours of project
2. We have a LIVE one to one technical support 24 hours a day, 7 days a week, to help you with any problem you may face or any clarification you may need during the course.
3. You will gain lifelong access to the recordings in the LMS.
4. At the end of the training, you will have to complete the project based on which we will provide you with a verifiable certificate.

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About the course

Edureka's Data Science course will cover the entire life cycle of the data, from data acquisition and data storage using R-Hadoop concepts, applying modeling through R programming using machine learning algorithms and illustrating impeccable data visualization by taking advantage of "R" capabilities.

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Why learn data science?

Data Science training certifies it with Big Data technologies "on demand" to help you obtain the Data Science job title with better results and Big Data skills and experience in R programming, machine learning and Hadoop framework.

After completing the Data Science course, you should be able to:
1. Get information about the "roles" performed by a data scientist
2. Analyze Big Data using R, Hadoop and Machine Learning.
3. Understanding the life cycle of data analysis
4. Work with different data formats such as XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation.
6. Understand Data Mining techniques and their implementation.
7. Analyze data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement several machine learning algorithms in Apache Mahout
10. Get information about data visualization and optimization techniques.
11. Explore the parallel processing function in R

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Who should go to this course?

The course is designed for all those who want to learn machine learning techniques with implementation in R language and want to apply these techniques in Big Data. The following professionals can attend this course:

1. Developers who aspire to be a "data scientist"
2. Analysis managers who lead a team of analysts.
3. SAS / SPSS professionals seeking to gain understanding in Big Data Analytics
4. Business analysts who want to understand machine learning techniques (ML).
5. Information architects who want to gain experience in predictive analysis.
6. Professionals & # 39; R & # 39; who want to captivate and analyze Big Data.
7. Hadoop professionals who want to learn R and ML techniques.
8. Analysts who want to understand Data Science methodologies.

For more information, write to us at sales@edureka.co or call us at IND: 9606058406 / EE. UU .: 18338555775 (without charge).


Customer rating:

Gnana Sekhar Vangara, Technology Leader at WellsFargo.com, says: "The Edureka Data science course provided me with a very good combination of theoretical and practical training, and the training course helped me in all areas that were not clear before. , especially concepts such as machine learning and Mahout The training was very informative and practical The LMS pre-recorded sessions and assignments were very good, as they contain a lot of information that will help me in my work. difficult subjects to understand in simple terms, Edureka is my GURU teacher now … Thanks EDUREKA and all the best. "

Video credits to edureka! YouTube channel

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    Random Forest Tutorial | Random Forest in R | Machine Learning | Data Science Training | Edureka

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