Naive Bayes Classifier in Python | Naive Bayes Algorithm | Machine Learning Algorithm | Edureka

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This Edureka video will provide you with a detailed and comprehensive knowledge of Naive Bayes Classifier Algorithm in python. At the end of the video, you will learn from a demo example on Naive Bayes. Below are the topics covered in this tutorial:

1. What is Naive Bayes?
2. Bayes Theorem and its use
3. Mathematical Working of Naive Bayes
4. Step by step Programming in Naive Bayes
5. Prediction Using Naive Bayes

(450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies)

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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 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. 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 enable 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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Sir great tutorial mere sir to padha nai paare yaha se dekh ke seekh gya🏃

aadilansari
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Thanks for scratch tutorial, I suggest you to always make a scratch code in every video like this one, cause libraries like sklearn never explains the real math and computation behind any ml and dl model

wolfisraging
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was good to see things being done from scratch
generally I use np for mean, stdetc and sklearn and other library

mirelahmed
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Great tutorial!! I love it. Could you share the code and data set?

xiaoqianyang
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It is a good video. The last example is related to confusion matrix only. Good one

bhaskarchoumal
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Is this line correct at 4:52? it should have been P(A|B)P(B) = P(B|A)P(A)

chittapriyamondal
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This is very helpful!
Can I have the data file please?
Also, I tried do the same on google colab, but it shows the error of "zip argument #1 must support iteration" when doing main
How can I resolve this error?

ddgvhp
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Nice Videos and great explanation :) Recommended to everyone . Request you to please share the code and data-set

ChitranshSagarMTAIE
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Great Video. I've been following some of your tutorials and they are very good. Please share the code of the demo

tashus
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the explanation was so good, can you share the code and data set

snehavk-tjjy
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I think in 7:35 slide, There is a wrong calculations with likelihood of 'No' Please verify it.. I got the value of 0.238...

mohammedadil
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Great tutorial. Please share with me the dataset

hymns_instrumental
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Thank you for the explanation! Can you share the dataset and the code used in the video?

ridaayub
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Very helpful.Request you to please share the code and data-set

olivedey
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great video! please share the code & data set ..

charanrathi
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Great sharing. May you also share me the dataset and code? Thanks!

kingchow
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Nice tutorial and really very helpful. Can I please have the source code?

priktaghatak
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Excellent Video!!! Clearly explained. Can I please get the source code and dataset?

fahimirfanalam
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can you share with us this presentation please

manarelkihel
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thanks for the tutorial. can i Get the source code file as well as the dataset?

divyamohan