The Complete Naive Bayes algorithm course with Python

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The Complete Naive Bayes algorithm course with Python 2023 [TutsNode.net] - The Complete Naive Bayes algorithm course with Python 2023 3 - Sentiment analysis using Naive Bayes
  • 11 - Implementation final Part.mp4 (152.1 MB)
  • 11 - sentiment-analyzer-1.ipynb (20.4 KB)
  • 10 - amazon-cells-labelled.txt (56.9 KB)
  • 10 - sentiment-analyzer.ipynb (12.1 KB)
  • 10 - Introduction to sentiment analysis and Implementation part 1.mp4 (70.1 MB)
5 - Some other Naive Bayes algorithm
  • 18 - Gaussian-Naive-Bayes-1.ipynb (285.7 KB)
  • 17 - Gaussian-Naive-Bayes.ipynb (167.5 KB)
  • 14 - Naive-Bayes-classifiers-1.ipynb (83.8 KB)
  • 16 - Multinomial-Naive-Bayes-1.ipynb (50.9 KB)
  • 15 - Multinomial-Naive-Bayes.ipynb (47.4 KB)
  • 13 - Naive-Bayes-classifiers.ipynb (35.5 KB)
  • 15 - Introduction to Multinomial Naive Bayes.mp4 (113.8 MB)
  • 18 - Gaussian Naive Bayes Implementation.mp4 (111.2 MB)
  • 14 - Bernoulli Naive Bayes Implementation.mp4 (62.2 MB)
  • 17 - Introduction to Gaussian Naive Bayes.mp4 (50.0 MB)
  • 16 - Multinomial Naive Bayes Implementation.mp4 (47.4 MB)
  • 13 - Introduction to Bernoulli Naive Bayes.mp4 (28.4 MB)
2 - Introduction to Naive Bayes classifier
  • 7 - Introduction-to-Naive-Bayes-algorithm-2.ipynb (142.0 KB)
  • 8 - Introduction-to-Confusion-matrix.ipynb (2.2 KB)
  • 5 - Introduction-to-Naive-Bayes-algorithm.ipynb (2.0 KB)
  • 6 - Introduction-to-Naive-Bayes-algorithm-1.ipynb (72.7 KB)
  • 6 - utilities.py (1.4 KB)
  • 9 - Introduction-to-Confusion-matrix-1.ipynb (16.9 KB)
  • 6 - data-multivar-nb.txt (4.7 KB)
  • 6 - Project 1 implementation Part 1.mp4 (87.9 MB)
  • 9 - Confusion matrix implementation.mp4 (69.3 MB)
  • 7 - Project 1 implementation Final Part.mp4 (68.9 MB)
  • 8 - Introduction to confusion matrix.mp4 (61.7 MB)
  • 5 - Basic theory of Naive Bayes algorithm.mp4 (45.0 MB)
4 - Diabetes Project with Naive Bayes
  • 12 - diabetes.csv (25.2 KB)
  • 12 - Introduction and Implementation.mp4 (122.0 MB)
  • 12 - Diabete-project-with-Naive-Bayes.ipynb (15.6 KB)
1 - Introduction
  • 4 - What is classification.mp4 (28.6 MB)
  • 3 - How to make the most out of this course.mp4 (8.8 MB)
  • 1 - Course Structure.mp4 (8.8 MB)
  • 2 - IMPORTANT NOTES PLEASE DO NOT SKIP.mp4 (4.7 MB)
6 - Thank you
  • 19 - Thank you.mp4 (24.0 MB)
  • TutsNode.net.txt (0.1 KB)
  • [TGx]Downloaded from torrentgalaxy.to .txt (0.6 KB)
  • .pad
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    • 4 (87.6 KB)
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    • 6 (167.1 KB)
    • 7 (133.9 KB)
    • 8 (342.8 KB)
    • 9 (264.4 KB)
    • 10 (41.2 KB)
    • 11 (128.8 KB)
    • 12 (29.6 KB)
    • 13 (458.7 KB)
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Description


Description

Are you ready to take your data science skills to the next level? If so, our comprehensive course on the Naive Bayes algorithm is just what you need! This course teaches you the theories, applications, and real-life examples of this powerful data science tool.

I have years of industry experience and will guide you every step of the way, from the basics to advanced concepts. With hands-on learning, you’ll work on real-life projects and use cutting-edge tools and technologies, such as Python and its famous libraries, like sci-kit-learn. my course is perfect for anyone looking to upskill, change careers, or simply expand their knowledge in data science.

In this course, you’ll learn how to implement Naive Bayes for solving various problems, including text classification, sentiment analysis, and spam filtering. You’ll also learn how to build and evaluate models for maximum accuracy. With interactive and self-paced learning, you’ll have the opportunity to put your newfound skills into practice as you work through real-life projects.

My course is designed to be flexible and self-paced so that you can learn at your own pace and on your schedule. And with my support team always available to help, you’ll always be on your own.

In addition to our comprehensive course, you’ll also have access to our extensive library of resources, including tutorials, guides, and notes, to help you further your understanding of the subject. Whether you’re just starting out or already familiar with Naive Bayes, our course is designed to meet your needs and help you reach your goals.

So, why wait? Enroll in our Naive Bayes course today and take the first step towards mastering one of the most powerful algorithms in data science. My course is perfect for anyone looking to enhance their data science skills, regardless of their current level of expertise.

This course is fun and exciting, but at the same time, we dive deep into Naive Bayes. Throughout the brand new version of the course, we cover tons of tools and technologies, including:

Naive Bayes
Numpy
Logistic Regression.
Matplotlib
GaussianNB
train_test_split
roc_curve
auc
DictVectorizer
MultinomialNB
BernoulliNB

Moreover, the course is packed with practical exercises based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your models. There are several big projects in this course. These projects are listed below:

Diabetes project.
Data Project.
Sentiment Analysis
MNIST Project.

So why wait? Enroll now and take your understanding of Naive Bayes to the next level
Who this course is for:

Anyone interested in Machine Learning.
Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence
Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets.
Any students in college who want to start a career in Data Science
Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.

Requirements

Basic knowledge of Python is required.

Last Updated 2/2023



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The Complete Naive Bayes algorithm course with Python


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1.1 GB
seeders:28
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The Complete Naive Bayes algorithm course with Python


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