What you'll learn
Naive Bayes
Numpy
Matplotlib
GaussianNB
LogisticRegression
train_test_split
roc_curve
auc
DictVectorizer
MultinomialNB
BernoulliNB
Requirements
Basic knowledge of Python is required.
Description
Unlock the full potential of Naive Bayes with this comprehensive course! Whether you're a beginner or an experienced professional, this course will provide you with a thorough understanding of this powerful machine learning algorithm and its many applications.You'll start by exploring the mathematical foundations of Naive Bayes, including the Bayes theorem and the underlying assumptions that make it such a useful tool for data analysis and prediction. From there, you'll delve into real-world applications, learning how Naive Bayes can be used for text classification, spam filtering, sentiment analysis, and much more.Throughout the course, you'll also have the opportunity to put your knowledge into practice through hands-on exercises and case studies. Whether you're working with large datasets or smaller ones, you'll learn how to use Naive Bayes to make predictions with accuracy and confidence.By the end of the course, you'll have a strong understanding of how Naive Bayes can be applied to a wide range of problems and situations. Whether you're a data scientist, machine learning engineer, or simply interested in exploring the power of this exciting algorithm, this course is the perfect starting point.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 BayesNumpyLogistic Regression.MatplotlibGaussianNBtrain_test_splitroc_curveaucDictVectorizerMultinomialNBBernoulliNBMoreover, 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 AnalysisMNIST Project. So why wait? Enroll now and take your understanding of Naive Bayes to the next level
Overview
Section 1: Introduction
Lecture 1 Course Structure
Lecture 2 IMPORTANT NOTES PLEASE DO NOT SKIP
Lecture 3 How to make the most out of this course
Lecture 4 What is classification
Section 2: Introduction to Naive Bayes classifier
Lecture 5 Basic theory of Naive Bayes algorithm
Lecture 6 Project 1 implementation Part 1
Lecture 7 Project 1 implementation Final Part
Lecture 8 Introduction to confusion matrix
Lecture 9 Confusion matrix implementation
Section 3: Sentiment analysis using Naive Bayes
Lecture 10 Introduction to sentiment analysis and Implementation part 1
Lecture 11 Implementation final Part
Section 4: Diabetes Project with Naive Bayes
Lecture 12 Introduction and Implementation
Section 5: Some other Naive Bayes algorithm
Lecture 13 Introduction to Bernoulli Naive Bayes
Lecture 14 Bernoulli Naive Bayes Implementation
Lecture 15 Introduction to Multinomial Naive Bayes
Lecture 16 Multinomial Naive Bayes Implementation
Lecture 17 Introduction to Gaussian Naive Bayes
Lecture 18 Gaussian Naive Bayes Implementation
Section 6: Thank you
Lecture 19 Thank you
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.


Homepage

https://www.udemy.com/course/the-complete-naive-bayes-algorithm-course-with-python-2023/