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: 49
Length: 8h 14m
Skill Level: All Levels
Languages: English
Includes: Lifetime access, certificate of completion (shareable on LinkedIn, Facebook, and Twitter), Q&A forum

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

Testimonials and Success Stories


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Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

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I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression. ...

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I have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.

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Hi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.

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Hello, Lazy Programmer!

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...

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By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.

Lectures

Welcome

4 Lectures · 14min
  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 (03:04)

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)

3 Lectures · 42min
  1. Pre-Installation Check (04:13)
  2. Anaconda Environment Setup (20:21)
  3. 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 (05:49)

Extras

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