The Complete Naive Bayes algorithm course with Python 2023

Name: The Complete Naive Bayes algorithm course with Python 2023
Rating: 4.2 (15 reviews)

GaussianNB, MultinomialNB, BernoulliNB, DictVectorizer, LogisticRegression

Created byHoang Quy La

Last updated 2/2023

English

What you'll learn

Naive Bayes
Numpy
Matplotlib
GaussianNB
LogisticRegression
train_test_split
roc_curve
auc
DictVectorizer
MultinomialNB
BernoulliNB

Course content

8 sections • 21 lectures • 3h 14m total length

Course Structure1:09
Explore the course structure and the introduction to the naive Bayes classifier. Learn to apply theory to practice with sentiment analysis and DIY projects, coding the algorithm for solid understanding.
IMPORTANT NOTES PLEASE DO NOT SKIP1:00
Learn essential notes stored in Google Colab, with all concepts in the Colab notebook; download or read the notebook to understand the course, with the full transcript included.
How to make the most out of this course1:52
What is classification4:15
Distinguish classification from regression, where classification groups data into classes and regression predicts continuous values, using handwritten digits and house prices as examples; classification is a subset of regression.

Basic theory of Naive Bayes algorithm5:35
We introduce the naive Bayes classifier, explain Bayes theorem and the independence assumption, and show how feature vectors predict class labels for animals like a cheetah.
Project 1 implementation Part 112:44
Project 1 implementation Final Part9:48
Split the data into 80% training and 20% testing, train a Naive Bayes classifier, and evaluate accuracy, precision, recall, and F1 with the confusion matrix.
Introduction to confusion matrix7:06
Confusion matrix implementation10:33
Implement and visualize a confusion matrix using numpy, matplotlib, and sklearn, generate true and predicted labels, compute the confusion matrix and classification report, and interpret precision and recall.

Introduction to sentiment analysis and Implementation part 111:11
Learn to build a sentiment analyzer with Python and Naive Bayes, covering sentiment analysis concepts, text preprocessing, tokenization, stopword removal, and count vectorization to convert reviews into a feature matrix.
Implementation final Part21:25
Transform a sparse matrix to a dense one, apply tf-idf with a vectorizer and transformer, then use train_test_split for cross-validation and evaluate multinomial Naive Bayes model with a confusion matrix.

Introduction to Bernoulli Naive Bayes5:15
Bernoulli Naive Bayes Implementation8:51
Learn to implement Bernoulli Naive Bayes with scikit-learn, perform a train/test split, and evaluate using cross-validated accuracy while visualizing the binary feature dataset.
Introduction to Multinomial Naive Bayes12:17
Multinomial Naive Bayes Implementation6:38
Implement multinomial naive bayes using scikit-learn, transforming text features with a vectorizer, training on dummy data, and making predictions on test data.
Introduction to Gaussian Naive Bayes8:31
Gaussian Naive Bayes Implementation16:23

Requirements

Basic knowledge of Python is required.

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.

The Complete Naive Bayes algorithm course with Python 2023

What you'll learn

Explore related topics

Course content

Introduction4 lectures • 8min

Introduction to Naive Bayes classifier5 lectures • 46min

Sentiment analysis using Naive Bayes2 lectures • 33min

Diabetes Project with Naive Bayes1 lecture • 18min

Some other Naive Bayes algorithm6 lectures • 58min

Fertility Project with Naive Bayes1 lecture • 9min

Income prediction with Naive Bayes Algorithm1 lecture • 21min

Thank you1 lecture • 1min

Requirements

Description

Who this course is for: