Machine Learning in Python


Updated On 02 Feb, 19


Machine Learning Tutorial in Python helps you gain expertise in various types of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. Through this playlist you will be learning the important Machine Learning concepts and its implementation in python programming language.


Lecture 22: Linear Regression vs Logistic Regression | Data Science Training | Edureka

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Lecture Details

** Data Science Certification using R: https://www.edureka.co/data-science-r-programming-certification-course **
This Edureka video on Linear Regression Vs Logistic Regression covers the basic concepts of linear and logistic models. The following topics are covered in this session:
(01:05) Types of Machine Learning
(03:09) Regression Vs Classification
(05:47) What is Linear Regression?
(09:22) What is Logistic Regression?
(13:26) Linear Regression Use Case
(15:02) Logistic Regression Use Case
(16:18) Linear Regression Vs Logistic Regression

Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist

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About the Course

Edurekas Data Science course will cover the whole data lifecycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling 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. Analyze 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. Analyze 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.

For online Data Science training, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.



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Excellent course helped me understand topic that i couldn't while attendinfg my college.

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Great course. Thank you very much.