Scikit Learn Tutorial | Machine Learning with Python | Python for Data Science Training | Edureka




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Python Certification Training for Data Science : https://www.edureka.co/python-programming-certification-training
This Edureka video on “Scikit-learn Tutorial” introduces you to machine learning in Python. It will also takes you through regression and clustering techniques along with a demo on SVM classification on the famous iris dataset. This video helps you to learn the below topics:

1. Machine learning Overview
2. Introduction to Scikit-learn
3. Installation of Scikit-learn
4. Regression and Classification
5. Demo

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Check out our Python Training Playlist: https://goo.gl/Na1p9G

PG in Artificial Intelligence and Machine Learning with NIT Warangal : https://www.edureka.co/post-graduate/machine-learning-and-ai

Post Graduate Certification in Data Science with IIT Guwahati – https://www.edureka.co/post-graduate/data-science-program
(450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies)

#Python #PythonForDataScience #PythonTutorial #PythonForBeginners #PythonOnlineTraining

How it Works?
1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24×7 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. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!
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About the Course

Edureka’s Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR.
During our Python Certification Training, our instructors will help you to:

1. Master the basic and advanced concepts of Python
2. Gain insight into the ‘Roles’ played by a Machine Learning Engineer
3. Automate data analysis using python
4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
6. Explain Time Series and it’s related concepts
7. Perform Text Mining and Sentimental analysis
8. Gain expertise to handle business in future, living the present
9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience

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Why learn Python?

Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next “Big Thing” and a must for Professionals in the Data Analytics domain.

For more information, Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free).

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Comment List

  • edureka!
    November 13, 2020

    Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Python Machine Learning Course curriculum, Visit our Website: http://bit.ly/2OpzQWw

  • edureka!
    November 13, 2020

    Very nice session. Thank you!

  • edureka!
    November 13, 2020

    Excellent lecture crisp and clear

  • edureka!
    November 13, 2020

    Can we do the same in pycharm IDE

  • edureka!
    November 13, 2020

    thank you for this awesome stuff.

  • edureka!
    November 13, 2020

    For the prediction accuracy score, I got 0.6666666666666666 …. but the instructor got 0.9666666666666667 … did anybody else have this issue? I'm trying to figure out if I've made an error.

  • edureka!
    November 13, 2020

    train_test_split is now model_selction , not cross_validation

  • edureka!
    November 13, 2020

    Where I can find the video for Decision Tree, Random Forest and Naive Bayes Classifier?? Please share the link.

  • edureka!
    November 13, 2020

    Most helpful content which I found on YouTube…Thanks!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

  • edureka!
    November 13, 2020

    thanks for this!!

  • edureka!
    November 13, 2020

    In SVM hyperplane separates data points into two parts buts there are three classes of iris , how can we separate three types of data points using single hyperplane ?

  • edureka!
    November 13, 2020

    at last if unable to see the accuracy try out this document, it may help https://scikit-learn.org/stable/modules/cross_validation.html.

  • edureka!
    November 13, 2020

    Thank u mam..it is nice.

  • edureka!
    November 13, 2020

    Thank You.
    This tutorial was very informative as well as being easy to understand.
    Please Keep It up !

  • edureka!
    November 13, 2020

    Thanks a lot, Edureka .. such a nice video explaining tough concepts in a simple manner..Helping in my research work..

  • edureka!
    November 13, 2020

    I am Truely a big fan of EDUREKA now ………

  • edureka!
    November 13, 2020

    This was very concise and informative. Thanks a lot my sister

  • edureka!
    November 13, 2020

    I want to join

  • edureka!
    November 13, 2020

    Edureka rocks!

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