Decision Tree Algorithm & Analysis | Machine Learning Algorithm | Data Science Training | Edureka

This Edureka Decision Tree tutorial will help you understand all the basics of Decision tree. This decision tree tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn decision tree analysis along with examples.
Below are the topics covered in this tutorial:
1) Machine Learning Introduction
2) Classification
3) Types of classifiers
4) Decision tree
5) How does Decision tree work?
6) Demo in R

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#decisiontree #Datasciencetutorial #Datasciencecourse #datascience

How it Works?

1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get Lifetime 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 a Verifiable Certificate!

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About the Course

Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.

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Why Learn Data Science?

Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.

After the completion of the Data Science course, you should be able to:
1. Gain insight into the 'Roles' played by a Data Scientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R

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Who should go for this course?

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

1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies

Customer Reviews:

[durgapradhan]

Your video is very informative. I am at present a retired professor and trying to popularise R in UNIVERSITIES in Odisha, India. Please provide diabetics data for demo as well as practice purpose.

[anuragdwivedi]

thank u very much....best explaination ever

[frederickbastian]

Thanks for the Tutorial. I am starting to learn data science and this way of teaching is really effective for people like me.

[chadarammadhuri]

Very well explained.. Thank you for this great session

[artivarshney]

Thank you so much for the tutorial, very well explained!

[ghoshar]

Very well explained with adequate real-life examples. Thanks.

[rajanadhikari]

I really enjoyed jam-packed information. Well taught!!!!

[ravishankarverma]

Thank you so much for this informative tutorial. Sir your way of teaching is very impressive.

[SayanNand]

Helped me to learn decision tree by watching this video only.

[nikhildeshpande]

The videos are fascinating and in detail.

[narasimhamurthy]

Hi Good Video to go through on DT, Machine Learning Intro

[prosenjitbiswas]

Thanks a ton. Very clear and crisp! I request edureka to also demonstrate examples with python!

[NitinGuptalko]

fantastic content as well as way of explanation. Thanks a lot for such good session.

[hamzasajid]

i am a business student, currently doing MBA from a University .

[PawanSingh-iumi]

well explained. thanks for the great video.

[qureshiboy]

Please forward the Datasets.
Thanks in advance :)

[rahulmjagan]

Great course. Can you please help me with the 'Data set'?
thank you

[hamzasajid]

My question is from where I start to learn data science? Should I learn Python first or learn Machine Language? I m really confused. Plz guide me in this matter. Thank you.

[jayjayf]

Since random forest is multiple decision tree does that also need to have calculated entropy ?

[muhammadhamzahm]

Awesome!
I need this data set for practice. Kindly mail me