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:

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:

- computer vision
- natural language processing
- financial analysis
- healthcare
- genomics

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

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

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I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)

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

- Concepts Section Introduction (01:48)
- Classification Review (14:59)
- Bayes' Rule Review (09:13)
- Naive Bayes Intuition (17:29)
- Concepts Section Summary (03:01)
- Suggestion Box (03:10)

- Applications Section Introduction (05:22)
- Strategy and Approach (01:49)
- Disease Prediction with Naive Bayes (07:08)
- Disease Prediction with Naive Bayes in Python (pt 1) (12:41)
- Disease Prediction with Naive Bayes in Python (pt 2) (10:48)
- Finance with Naive Bayes (05:24)
- Finance with Naive Bayes in Python (pt 1) (15:28)
- Finance with Naive Bayes in Python (pt 2) (08:04)
- Genomics with Naive Bayes (07:33)
- Genomics with Naive Bayes in Python (07:05)
- Image Classification with Naive Bayes (11:04)
- Image Classification with Naive Bayes in Python (11:25)
- Text Classification with Naive Bayes (pt 1) (16:35)
- Text Classification with Naive Bayes (pt 2) (03:01)
- Text Classification with Naive Bayes in Python (16:40)
- Applications Section Summary (01:39)
- Application Exercise (01:23)

- Gaussian Naive Bayes Theory (32:32)
- Gaussian Naive Bayes in Python (22:57)
- Bernoulli Naive Bayes Theory (13:38)
- Multinomial Naive Bayes Theory (15:17)
- Exercises: Test Your Might! (03:01)
- Bernoulli Naive Bayes in Python (08:45)
- Multinomial Naive Bayes in Python (11:46)

- Categorical Naive Bayes (CategoricalNB) (07:29)
- Complement Naive Bayes (ComplementNB) (11:51)

- Anaconda Environment Setup (20:21)
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)

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

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

- What is the Appendix? (02:48)
- Where to get discount coupons and FREE deep learning material (05:31)