KNN Algorithm using Python | How KNN Algorithm works | Python Data Science Training | Edureka




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** Python for Data Science: https://www.edureka.co/data-science-python-certification-course **
This Edureka video on KNN Algorithm will help you to build your base by covering the theoretical, mathematical and implementation part of the KNN algorithm in Python. Topics covered under this video includes:

1. What is KNN Algorithm?
2. Industrial Use case of KNN Algorithm
3. How things are predicted using KNN Algorithm
4. How to choose the value of K?
5. KNN Algorithm Using Python
6. Implementation of KNN Algorithm from scratch

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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 Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.

After completing this Machine Learning Certification Training using Python, you should be able to:
Gain insight into the ‘Roles’ played by a Machine Learning Engineer
Automate data analysis using python
Describe Machine Learning
Work with real-time data
Learn tools and techniques for predictive modeling
Discuss Machine Learning algorithms and their implementation
Validate Machine Learning algorithms
Explain Time Series and it’s related concepts
Gain expertise to handle business in future, living the present

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Why learn Machine Learning with Python?

Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.

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Comment List

  • edureka!
    November 28, 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 28, 2020

    Thanks ….got the concept

  • edureka!
    November 28, 2020

    dear edurka team i need this python code plz help me to provide

  • edureka!
    November 28, 2020

    Sir,
    Your video is really informative….

  • edureka!
    November 28, 2020

    how to draw the decision boundaries?

  • edureka!
    November 28, 2020

    Well explained!

  • edureka!
    November 28, 2020

    best explanation i found on the internet till date

  • edureka!
    November 28, 2020

    How do we choose the optimum value of K

  • edureka!
    November 28, 2020

    Explanation of theory was superb..
    Sir, plz try to explain coding as well.
    Overall good session.

  • edureka!
    November 28, 2020

    realy nice. nice job

  • edureka!
    November 28, 2020

    It would be better to include few important steps like cross-validation, standardization of data and in the end touch some base on optimum K (error rate vs k -value). Just 2 cents. Other wise good job.

  • edureka!
    November 28, 2020

    now how would I plot the data?

  • edureka!
    November 28, 2020

    hello and thanks for ur hard work…wish you'd explained the codes alittle bit.

  • edureka!
    November 28, 2020

    very good explaination

  • edureka!
    November 28, 2020

    Excellent video and superb explanation. Could you please share the source code?

  • edureka!
    November 28, 2020

    it was supeb explanation

  • edureka!
    November 28, 2020

    At 12:43 in loadDataset method, in the line (for y in range(4)). Why have you used the value 4 ? What is its significance ?

  • edureka!
    November 28, 2020

    can we built gu interface in python for prediction

  • edureka!
    November 28, 2020

    Hi sir.. Nice explanation.. But i wonder with libraries available in scikitlearn and pandas y dont u code with them and show.. As anyways we will not be using these lengthy codes in real time..

  • edureka!
    November 28, 2020

    Great, it was magical! thanks

  • edureka!
    November 28, 2020

    Really helpful video.Thanks!!
    Do u know how to do predictions on yearly data in python. Which method will be appropriate for this?

  • edureka!
    November 28, 2020

    Ho..is it possible to get this code

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