Data Science & Machine Learning: Naive Bayes in Python

Data Science & Machine Learning: Naive Bayes in Python, available at $69.99, has an average rating of 4.71, with 45 lectures, based on 449 reviews, and has 5514 subscribers.

You will learn about Apply Naive Bayes to image classification (Computer Vision) Apply Naive Bayes to text classification (NLP) Apply Naive Bayes to Disease Prediction, Genomics, and Financial Analysis Understand Naive Bayes concepts and algorithm Implement multiple Naive Bayes models from scratch This course is ideal for individuals who are Beginner Python developers curious about data science and machine learning or Students and professionals interested in machine learning fundamentals It is particularly useful for Beginner Python developers curious about data science and machine learning or Students and professionals interested in machine learning fundamentals.

Enroll now: Data Science & Machine Learning: Naive Bayes in Python

Summary

Title: Data Science & Machine Learning: Naive Bayes in Python

Price: $69.99

Average Rating: 4.71

Number of Lectures: 45

Number of Published Lectures: 45

Number of Curriculum Items: 45

Number of Published Curriculum Objects: 45

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Apply Naive Bayes to image classification (Computer Vision)
Apply Naive Bayes to text classification (NLP)
Apply Naive Bayes to Disease Prediction, Genomics, and Financial Analysis
Understand Naive Bayes concepts and algorithm
Implement multiple Naive Bayes models from scratch

Who Should Attend

Beginner Python developers curious about data science and machine learning
Students and professionals interested in machine learning fundamentals

Target Audiences

Beginner Python developers curious about data science and machine learning
Students and professionals interested in machine learning fundamentals

In this self-paced course, you will learn how to apply Naive Bayes to many real-world datasets in a wide variety of areas, such as:

computer vision
natural language processing
financial analysis
healthcare
genomics

Why should you take this course? Naive Bayes is one of the fundamental algorithms in machine learning, data science, and artificial intelligence. No practitioner is complete without mastering it.

This course is designed to be appropriate for all levels of students, whether you are beginner, intermediate, or advanced. You’ll learn both the intuition for how Naive Bayes works and how to apply it effectively while accounting for the unique characteristics of the Naive Bayes algorithm. You’ll learn about when and why to use the different versions of Naive Bayes included in Scikit-Learn, including GaussianNB, BernoulliNB, and MultinomialNB.

In the advanced section of the course, you will learn about how Naive Bayes really works under the hood. You will also learn how to implement several variants of Naive Bayes from scratch, including Gaussian Naive Bayes, Bernoulli Naive Bayes, and Multinomial Naive Bayes. The advanced section will require knowledge of probability, so be prepared!

Thank you for reading and I hope to see you soon!

Suggested Prerequisites:

Decent Python programming skill
Comfortable with data science libraries like Numpy and Matplotlib
For the advanced section, probability knowledge is required

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?

Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses, including my free course)

UNIQUE FEATURES

Every line of code explained in detail – email me any time if you disagree
Less than 24 hour response time on Q&A on average
Not afraid of university-level math – get important details about algorithms that other courses leave out

Course Curriculum

Chapter 1: Welcome

Lecture 1: Introduction and Outline

Lecture 2: Where to get the Code

Lecture 3: Are You Beginner, Intermediate, or Advanced? All are OK!

Lecture 4: How to Succeed in this Course

Chapter 2: Naive Bayes Concepts (Beginner)

Lecture 1: Concepts Section Introduction

Lecture 2: Classification Review

Lecture 3: Bayes' Rule Review

Lecture 4: Naive Bayes Intuition

Lecture 5: Concepts Section Summary

Lecture 6: Suggestion Box

Chapter 3: Naive Bayes Applications (Beginner-Intermediate)

Lecture 1: Applications Section Introduction

Lecture 2: Strategy and Approach

Lecture 3: Disease Prediction with Naive Bayes

Lecture 4: Disease Prediction with Naive Bayes in Python (pt 1)

Lecture 5: Disease Prediction with Naive Bayes in Python (pt 2)

Lecture 6: Finance with Naive Bayes

Lecture 7: Finance with Naive Bayes in Python (pt 1)

Lecture 8: Finance with Naive Bayes in Python (pt 2)

Lecture 9: Genomics with Naive Bayes

Lecture 10: Genomics with Naive Bayes in Python

Lecture 11: Image Classification with Naive Bayes

Lecture 12: Image Classification with Naive Bayes in Python

Lecture 13: Text Classification with Naive Bayes (pt 1)

Lecture 14: Text Classification with Naive Bayes (pt 2)

Lecture 15: Text Classification with Naive Bayes in Python

Lecture 16: Applications Section Summary

Lecture 17: Application Exercise

Chapter 4: Naive Bayes In-Depth (Advanced)

Lecture 1: Gaussian Naive Bayes Theory

Lecture 2: Gaussian Naive Bayes in Python

Lecture 3: Bernoulli Naive Bayes Theory

Lecture 4: Multinomial Naive Bayes Theory

Lecture 5: Exercises: Test Your Might!

Chapter 5: Setting Up Your Environment (Appendix/FAQ by Student Request)

Lecture 1: Pre-Installation Check

Lecture 2: Anaconda Environment Setup

Lecture 3: How to install Numpy, Scipy, Matplotlib, Pandas, TensorFlow, +More

Chapter 6: Extra Help With Python Coding for Beginners (Appendix/FAQ by Student Request)

Lecture 1: How to Code by Yourself (part 1)

Lecture 2: How to Code by Yourself (part 2)

Lecture 3: Proof that using Jupyter Notebook is the same as not using it

Lecture 4: Temporary 403 Errors

Chapter 7: Effective Learning Strategies for Machine Learning (Appendix/FAQ by Student Requ

Lecture 1: How to Succeed in this Course (Long Version)

Lecture 2: Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Lecture 3: Machine Learning and AI Prerequisite Roadmap (pt 1)

Lecture 4: Machine Learning and AI Prerequisite Roadmap (pt 2)

Chapter 8: Appendix / FAQ Finale

Lecture 1: What is the Appendix?

Lecture 2: BONUS

Instructors

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 4 votes
4 stars: 193 votes
5 stars: 251 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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