Data Science and Machine Learning: Naive Bayes in Python

Master a crucial artificial intelligence algorithm and skyrocket your Python programming skills

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Course Data

Lectures: 48
Length: 8h 15m
Skill Level: All Levels
Languages: English
Includes: Lifetime access

Course Description

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

Lectures

Welcome

4 Lectures · 20min
  1. Introduction and Outline (04:37) (FREE preview available)
  2. Where to get the code and data - instant access (01:42)
  3. Are You Beginner, Intermediate, or Advanced? All are OK! (05:01)
  4. How to Succeed in this Course (08:42)

Naive Bayes Concepts (Beginner)

6 Lectures · 49min
  1. Concepts Section Introduction (01:48)
  2. Classification Review (14:59)
  3. Bayes' Rule Review (09:13)
  4. Naive Bayes Intuition (17:29)
  5. Concepts Section Summary (03:01)
  6. Suggestion Box (03:10)

Naive Bayes Applications (Beginner-Intermediate)

17 Lectures · 02hr 23min
  1. Applications Section Introduction (05:22)
  2. Strategy and Approach (01:49)
  3. Disease Prediction with Naive Bayes (07:08)
  4. Disease Prediction with Naive Bayes in Python (pt 1) (12:41)
  5. Disease Prediction with Naive Bayes in Python (pt 2) (10:48)
  6. Finance with Naive Bayes (05:24)
  7. Finance with Naive Bayes in Python (pt 1) (15:28)
  8. Finance with Naive Bayes in Python (pt 2) (08:04)
  9. Genomics with Naive Bayes (07:33)
  10. Genomics with Naive Bayes in Python (07:05)
  11. Image Classification with Naive Bayes (11:04)
  12. Image Classification with Naive Bayes in Python (11:25)
  13. Text Classification with Naive Bayes (pt 1) (16:35)
  14. Text Classification with Naive Bayes (pt 2) (03:01)
  15. Text Classification with Naive Bayes in Python (16:40)
  16. Applications Section Summary (01:39)
  17. Application Exercise (01:23)

Naive Bayes In-Depth (Advanced)

7 Lectures · 01hr 47min
  1. Gaussian Naive Bayes Theory (32:32)
  2. Gaussian Naive Bayes in Python (22:57)
  3. Bernoulli Naive Bayes Theory (13:38)
  4. Multinomial Naive Bayes Theory (15:17)
  5. Exercises: Test Your Might! (03:01)
  6. Bernoulli Naive Bayes in Python (08:45)
  7. Multinomial Naive Bayes in Python (11:46)

More Applications (VIP-only)

2 Lectures · 19min
  1. Categorical Naive Bayes (CategoricalNB) (07:29)
  2. Complement Naive Bayes (ComplementNB) (11:51)

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

2 Lectures · 37min
  1. Windows-Focused Environment Setup (20:21)
  2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)

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

4 Lectures · 49min
  1. How to use Github & Extra Coding Tips (Optional) (11:12)
  2. How to Code Yourself (part 1) (15:55)
  3. How to Code Yourself (part 2) (09:24)
  4. Proof that using Jupyter Notebook is the same as not using it (12:29)

Effective Learning Strategies for Machine Learning (Appendix/FAQ by Student Request)

4 Lectures · 59min
  1. How to Succeed in this Course (Long Version) (10:25)
  2. Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:05)
  3. What order should I take your courses in? (part 1) (11:19)
  4. What order should I take your courses in? (part 2) (16:07)

Appendix / FAQ Finale

2 Lectures · 08min
  1. What is the Appendix? (02:48)
  2. Where to get discount coupons and FREE deep learning material (05:31)

Extras

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