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The Complete Naive Bayes algorithm course with Python 2023

The Complete Naive Bayes algorithm course with Python 2023

GaussianNB, MultinomialNB, BernoulliNB, DictVectorizer, LogisticRegression

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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.

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.

What you'll learn

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

How do you use the naive Bayes algorithm in Python?

Step 1: Calculate the prior probability for given class labels. Step 2: Find Likelihood probability with each attribute for each class. Step 3: Put these value in Bayes Formula and calculate posterior probability. Step 4: See which class has a higher probability, given the input belongs to the higher probability class.

Gaussian Naive Bayes (GNB) is a classification technique used in Machine Learning (ML) based on the probabilistic approach and Gaussian distribution. Gaussian Naive Bayes assumes that each parameter (also called features or predictors) has an independent capacity of predicting the output variable