The ‘Bayesian Machine Learning in Python: A/B Testing’ course will help you to learn the fundamental tools of the Bayesian method through the example of A/B testing and then you’ll be able to carry those Bayesian techniques to more advanced machine learning models in the future.

The things you’ll learn in this course are not only applicable to A/B testing but rather, they are using A/B testing as a concrete example of how Bayesian techniques can be applied. The course is usually available for **INR 2,799** on Udemy but you can click on the link and get the ‘**Bayesian Machine Learning in Python: A/B Testing**’ for **INR 449**.

## Who all can opt for this course?

- Technically-inclined students and professionals those want to understand Bayesian machine learning methods for use in their data science job

## Course Highlights

Key Highlights | Details |
---|---|

Registration Link | Apply Now! |

Price | INR 449 (INR 2,799) 85% off |

Duration | 10 Hours |

Rating | 4.7/5 |

Student Enrollment | 34,181 students |

Instructor | Lazy Programmer Inc. https://www.linkedin.com/in/lazyprogrammerinc. |

Topics Covered | Bayesian A/B testing, machine learning, Data Science, Data Analytics techniques |

Course Level | Intermediate |

Total Student Reviews | 5,935 |

## Learning Outcomes

- To enhance the effectiveness of A/B testing, use adaptive algorithms
- Recognize the differences between frequentist and Bayesian statistics
- Analyze A/B testing using Bayesian techniques

## Course Content

S.No. | Module (Duration) | Topics |
---|---|---|

1. | Introduction and Outline (24 minutes) | What’s this course all about? |

Where to get the code for this course | ||

How to succeed in this course | ||

2. | The High-Level Picture (18 minutes) | Real-World Examples of A/B Testing |

What is Bayesian Machine Learning? | ||

3. | Bayes Rule and Probability Review (01 hour 37 minutes) | Review Section Introduction |

Probability and Bayes’ Rule Review | ||

Calculating Probabilities – Practice | ||

The Gambler | ||

The Monty Hall Problem | ||

Maximum Likelihood Estimation – Bernoulli | ||

Click-Through Rates (CTR) | ||

Maximum Likelihood Estimation – Gaussian (pt 1) | ||

Maximum Likelihood Estimation – Gaussian (pt 2) | ||

CDFs and Percentiles | ||

Probability Review in Code | ||

Probability Review Section Summary | ||

Beginners: Fix Your Understanding of Statistics vs Machine Learning | ||

Suggestion Box | ||

4. | Traditional A/B Testing (02 hours 08 minutes) | Confidence Intervals (pt 1) – Intuition |

Confidence Intervals (pt 2) – Beginner Level | ||

Confidence Intervals (pt 3) – Intermediate Level | ||

Confidence Intervals (pt 4) – Intermediate Level | ||

Confidence Intervals (pt 5) – Intermediate Level | ||

Confidence Intervals Code | ||

Hypothesis Testing – Examples | ||

Statistical Significance | ||

Hypothesis Testing – The API Approach | ||

Hypothesis Testing – Accept Or Reject? | ||

Hypothesis Testing – Further Examples | ||

Z-Test Theory (pt 1) | ||

Z-Test Theory (pt 2) | ||

Z-Test Code (pt 1) | ||

Z-Test Code (pt 2) | ||

A/B Test Exercise | ||

Classical A/B Testing Section Summary | ||

5. | Bayesian A/B Testing (02 hours 52 minutes) | Section Introduction: The Explore-Exploit Dilemma |

Applications of the Explore-Exploit Dilemma | ||

Epsilon-Greedy Theory | ||

Calculating a Sample Mean (pt 1) | ||

Epsilon-Greedy Beginner’s Exercise Prompt | ||

Designing Your Bandit Program | ||

Epsilon-Greedy in Code | ||

Comparing Different Epsilons | ||

Optimistic Initial Values Theory | ||

Optimistic Initial Values Beginner’s Exercise Prompt | ||

Optimistic Initial Values Code | ||

UCB1 Theory | ||

UCB1 Beginner’s Exercise Prompt | ||

UCB1 Code | ||

Bayesian Bandits / Thompson Sampling Theory (pt 1) | ||

Bayesian Bandits / Thompson Sampling Theory (pt 2) | ||

Thompson Sampling Beginner’s Exercise Prompt | ||

Thompson Sampling Code | ||

Thompson Sampling With Gaussian Reward Theory | ||

Thompson Sampling With Gaussian Reward Code | ||

Exercise on Gaussian Rewards | ||

Why don’t we just use a library? | ||

Nonstationary Bandits | ||

Bandit Summary, Real Data, and Online Learning | ||

(Optional) Alternative Bandit Designs | ||

6. | Bayesian A/B Testing Extension (18 minutes) | More about the Explore-Exploit Dilemma |

Confidence Interval Approximation vs. Beta Posterior | ||

Adaptive Ad Server Exercise | ||

7. | Practice Makes Perfect (18 minutes) | Intro to Exercises on Conjugate Priors |

Exercise: Die Roll | ||

The most important quiz of all – Obtaining an infinite amount of practice | ||

8. | Setting Up Your Environment (FAQ by Student Request) (37 minutes) | Anaconda Environment Setup |

How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow | ||

9. | Extra Help With Python Coding for Beginners (FAQ by Student Request) (42 minutes) | How to Code by Yourself (part 1) |

How to Code by Yourself (part 2) | ||

Proof that using Jupyter Notebook is the same as not using it | ||

Python 2 vs Python 3 | ||

10. | Effective Learning Strategies for Machine Learning (FAQ by Student Request) (59 minutes) | How to Succeed in this Course (Long Version) |

Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? | ||

Machine Learning and AI Prerequisite Roadmap (pt 1) | ||

Machine Learning and AI Prerequisite Roadmap (pt 2) | ||

11. | Appendix / FAQ Finale (08 minutes) | What is the Appendix? |

BONUS |

## Resources Required

- Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF)
- Numpy stack programming in Python

## Featured Review

**Julia Perry (5/5) ** : I’ve found that this instructor’s style of teaching is the way I learn best. Everything is intuitive and built on foundational principles without sacrificing quality. There are plenty of examples and challenges to get you practising. The exercises were really useful and fun to code along with. Well done.

## Pros

**Ethan Xu (5/5)**: This course finds the perfect balance of theory and coding exercise.**Pablo Avendano (5/5)**: This was an excellent introduction to A-B testing using Bayesian machine learning.**Abhyuday Desai (5/5)**: Best instructor if you want to learn the theory and not just the SKLearn APIs.**Tariq Azad (5/5)**: It’s a very good course to get started with Bayesian machine learning.

## Cons

**Adiwid D. (1/5)**: A lot of theory is given without links to the proofs so it’s very difficult to really understand the material.**Anonymized User (1/5)**: If you do not know Python, you will not learn it, if you know Python, you will waste your time.**Subhojit Sengupta (1/5)**: The coding part is too less and complex, unlike other videos of Udemy.**Anonymized User (1/5)**: Python videos just show author writing code, with little to no explanation.

## About the Author

The instructor of this course is Lazy Programmer Inc. who is a Artificial intelligence and machine learning engineer. With 4.6 Instructor Rating and 148,353 Reviews on Udemy, he/she offers 33 Courses and has taught 526,884 Students so far.

- Although Instructor have also been recognised as a data scientist, big data engineer, and full stack software engineer, Instructor currently spend the majority of his time as an artificial intelligence and machine learning engineer with an emphasis on deep learning
- Instructor earned his first master’s degree in computer engineering with a focus on machine learning and pattern recognition more than ten years ago
- Instructor’s second master’s degree in statistics with a focus on financial engineering was awarded to him
- Data scientist and big data engineer with experience in online advertising and digital media (optimising click and conversion rates) (building data processing pipelines)
- Instructor routinely use big data technologies like Hadoop, Pig, Hive, MapReduce, and Spark
- Instructor has developed deep learning models for text modelling, image and signal processing, user behaviour prediction, and click-through rate estimation
- In his/her work with recommendation systems, Instructor used collaborative filtering and reinforcement learning, and they validated the findings using A/B testing
- Instructor have instructed students at universities like Columbia University, NYU, Hunter College, and The New School in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics
- Instructor’s web programming skills have helped numerous businesses
- Instructor handle all of the server-side backend work, frontend HTML/JS/CSS work, and operations/deployment work

## Comparison Table

Parameters | Bayesian Machine Learning in Python: A/B Testing | Data Science: Modern Deep Learning in Python | Data Science: Deep Learning and Neural Networks in Python |
---|---|---|---|

Offers | INR 455 (85% off | INR 455 (87% off | INR 455 (87% off |

Duration | 10.5 hours | 11.5 hours | 12 hours |

Rating | 4.7 /5 | 4.7 /5 | 4.6 /5 |

Student Enrollments | 34,181 | 33,626 | 52,709 |

Instructors | Lazy Programmer Inc. | Lazy Programmer Inc. | Lazy Programmer Inc. |

Register Here | Apply Now! | Apply Now! | Apply Now! |

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