# Data Science: Bayesian Classification in Python

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### Course Data

Lectures: 18
Length: 3h 38m
Skill Level: All Levels
Languages: English

### Course Description

Welcome to Bayesian Classification!

This course is the sequel to Bayesian Linear Regression, and it's a part of my series on Bayesian Machine Learning. While the previous course looked at regression (predicting a numerical output), this course looks at classification (predicting a categorical output).

This course takes the Bayes classifier (which, despite its name, is not Bayesian), and makes it Bayesian by placing priors on its parameters. In this course we will study the Bayesian Bayes classifier through the lens of Naive Bayes, so it would be a good idea to have a good handle on Naive Bayes before starting this course.

How does this course compare to Bayesian Linear Regression? Bayesian Linear Regression introduced a lot of the necessary math needed for Bayesian Machine Learning, and it built upon the A/B Testing course (mainly the concept of conjugate priors and how to compute the posterior distribution). In this course, we will go faster through the math we've already seen, so that we can focus on the new and interesting parts. Unlike the Bayesian Linear Regression course, the real learning opportunity in this course is in implementing each algorithm you learn about.

Why Bayesian Machine Learning? The main advantage of using Bayesian Machine Learning is that it doesn't require you to find a best guess for the optimal model parameters (a point estimate). Instead, Bayesian ML allows us to integrate over all possible values of the parameters (of which there are usually an infinite number).

Suggested Prerequisites:

• Python coding: if/else, loops, lists, dicts, sets
• Numpy and Pandas coding: matrix and vector operations, loading a CSV file
• Basic math: calculus, linear algebra, probability
• Naive Bayes classifiers
• Bayesian Machine Learning: A/B Testing in Python (know about conjugate priors)
• Bayesian Linear Regression: know about the posterior predictive distribution

## Testimonials and Success Stories

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Looking forward to your new stuff.

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!!

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.

Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.

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. ...

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

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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. ...

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

#### Introduction and Outline

2 Lectures · 04min

#### Bayesian Bayes Classifier

8 Lectures · 01hr 23min
1. Bayes Classifier Review (06:09)
2. Making the Bayes Classifier More Bayesian (09:47)
3. Fit (20:45)
4. Predict (19:16)
5. Limitations, Extensions, Naive Assumption (04:50)
6. Softmax (09:37)
7. Theory Summary (10:21)
8. Suggestion Box (03:10)

#### Gaussian

4 Lectures · 01hr 24min
1. Gaussian - Fitting (18:15)
2. Gaussian - Predicting (19:53)
3. Gaussian Code (26:26)
4. Unknown Mean and Variance (Optional) (19:40)

#### Poisson

4 Lectures · 45min
1. Poisson Likelihood (11:03)
2. Poisson Code pt 1 (20:15)
3. Poisson Code pt 2 (09:21)
4. Poisson Code pt 3 (04:50)

#### Extras

• PDF Notes
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