Probabilistic Machine Learning

The "Probabilistic Machine Learning" course offers an in-depth exploration of probabilistic methods in machine learning, focusing on Bayesian inference, graphical models, and probabilistic reasoning. Participants will learn to apply these techniques to real-world problems, enhancing their ability to handle uncertainty and make data-driven decisions. Through a blend of theoretical understanding and practical applications, this course equips learners with the skills needed to develop and implement robust probabilistic models, preparing them for advanced roles in data science and machine learning.

Coding University

35:11:28 Hours

158 Enrolled

    • (4.77 | 31 Reviews)

English

Last updated

Tue, 30-Jul-2024

Course description

Course Overview

Probabilistic Machine Learning is a cutting-edge course designed to explore the theoretical and practical aspects of probabilistic methods in machine learning. This course provides an in-depth understanding of how probabilistic models can be applied to various machine learning problems, enhancing your ability to handle uncertainty and make informed predictions. Students will delve into Bayesian inference, graphical models, and probabilistic reasoning, gaining hands-on experience with real-world datasets and tools. By blending theoretical knowledge with practical applications, this course aims to equip learners with the skills to develop robust, probabilistic models for complex problems in data science.

Key Learning Objectives

  • Understand the foundational principles of probabilistic reasoning and Bayesian inference.
  • Develop proficiency in constructing and interpreting graphical models such as Bayesian networks and Markov networks.
  • Apply probabilistic methods to real-world machine learning problems, including classification, regression, and clustering.
  • Gain experience with popular tools and libraries for probabilistic modeling and inference.
  • Evaluate the performance and reliability of probabilistic models using various metrics and techniques.

Requirements

  • Basic knowledge of machine learning concepts and techniques.
  • Proficiency in programming with Python or a similar language.
  • Familiarity with probability theory and statistics.
  • Experience with data manipulation and visualization libraries (e.g., NumPy, Pandas, Matplotlib).
  • Prior coursework or experience in advanced machine learning or data science is beneficial but not required.

Outcomes

By the end of this course, participants will be able to:

  • Develop and implement probabilistic models for diverse machine learning tasks.
  • Utilize Bayesian methods to update beliefs and make predictions under uncertainty.
  • Construct and analyze graphical models to understand complex relationships in data.
  • Apply probabilistic programming techniques to real-world datasets and problems.
  • Assess and refine probabilistic models to enhance their accuracy and robustness.

Certification

Upon successful completion of the course, participants will receive a certification in Probabilistic Machine Learning. This certification demonstrates your ability to apply advanced probabilistic methods to solve complex machine learning challenges. It highlights your proficiency in Bayesian inference, graphical models, and probabilistic reasoning, validating your skills and knowledge in this specialized area of machine learning.

What will i learn?

  • Develop and implement probabilistic models for diverse machine learning tasks.
  • Utilize Bayesian methods to update beliefs and make predictions under uncertainty.
  • Construct and analyze graphical models to understand complex relationships in data.
  • Apply probabilistic programming techniques to real-world datasets and problems.
  • Assess and refine probabilistic models to enhance their accuracy and robustness.

Requirements

Coding University

View profile

John Sanchez

09-Aug-2024

5

This course brilliantly dives into probabilistic methods, perfectly balancing theory and practice. It’s an empowering experience that sharpened my skills for tackling real-world challenges in data science! Highly recommended!

Amy Evans

09-Aug-2024

4

This course provides an excellent foundation in probabilistic methods, with a perfect balance of theory and practical application. The content is insightful and engaging, though a faster pace on some topics would be beneficial.

Mary Walker

09-Aug-2024

5

This course excels in blending theoretical insights with practical applications, empowering participants to master Bayesian inference and graphical models. It effectively prepares learners for advanced roles in data science by enhancing their ability to navigate uncertainty.

Lily Edwards

09-Aug-2024

5

This course brilliantly combines theory with practical applications, providing a comprehensive understanding of Bayesian inference and graphical models. The engaging content and real-world problem-solving enhance skills in handling uncertainty, making it an invaluable resource for aspiring data scientists and machine learning professionals.

John Lopez

09-Aug-2024

5

This course excels in blending theory with practical applications, empowering learners to tackle uncertainty using Bayesian inference and graphical models, ultimately enhancing their skills for advanced roles in data science and machine learning. Highly recommended!

Deborah Patel

09-Aug-2024

5

An exceptional course that masterfully blends theory and practical application, empowering learners to confidently navigate uncertainty and create effective probabilistic models for real-world challenges.

Ashley Baker

08-Aug-2024

5

This course provides a comprehensive dive into Bayesian inference and graphical models, merging theory with hands-on applications. Participants gain invaluable skills for tackling uncertainty in real-world scenarios, making it ideal for those aiming for advanced roles in data science and machine learning. Highly recommended!

Jack Collins

08-Aug-2024

4

This course provides an exceptional deep dive into probabilistic methods in machine learning, with a strong emphasis on Bayesian inference and graphical models. The blend of theory and practical applications empowers participants to tackle real-world problems effectively. The instructor's expertise and engaging teaching style make complex concepts accessible. However, some may find the pace brisk, requiring extra study time to fully grasp the material.

Richard Torres

08-Aug-2024

5

Exceptional course for mastering probabilistic methods; perfect for advanced data science and machine learning roles.

Paul Ruiz

08-Aug-2024

5

This course brilliantly combines theory and practical application, empowering participants with essential skills in Bayesian inference and probabilistic reasoning. The focus on real-world problems and uncertainty management makes it invaluable for anyone looking to excel in data-driven decision-making. Highly recommended!

Justin Howard

08-Aug-2024

5

Transformative experience, expertly taught; immensely valuable for aspiring data scientists!

Stephanie Howard

08-Aug-2024

5

This course is a fantastic journey into the world of probabilistic methods in data science! The emphasis on Bayesian inference and graphical models not only deepens theoretical understanding but also equips participants with practical tools for real-world applications. The blend of theory and hands-on experience truly enhances problem-solving skills. It's an invaluable resource for anyone looking to excel in machine learning and make informed, data-driven decisions. Highly recommended!

Deborah Powell

07-Aug-2024

5

This course is a game-changer for anyone looking to deepen their understanding of probabilistic methods in machine learning. The focus on Bayesian inference and graphical models provides invaluable insights into managing uncertainty in real-world scenarios. The blend of theory and practical applications ensures learners are well-prepared to tackle complex data-driven challenges. Highly recommend for those aiming for advanced roles in data science and machine learning!

Emily Roberts

07-Aug-2024

3

This course provides a comprehensive dive into probabilistic methods, particularly excelling in Bayesian inference and graphical models. Its blend of theory and practical application enhances confidence in handling uncertainty. However, some sections may be overly dense for beginners, potentially requiring a stronger statistical background. Overall, a solid foundation for advanced data science roles.

Timothy Johnson

06-Aug-2024

5

This course expertly combines theory and practice, empowering learners with essential skills in Bayesian inference and probabilistic reasoning, ideal for advanced data science careers. Highly recommended!

John Perry

06-Aug-2024

5

This course provides an exceptional blend of theory and practical application, empowering learners to tackle real-world challenges using Bayesian methods and graphical models. It equips participants with essential skills for making informed, data-driven decisions in uncertain environments.

Larry Brown

06-Aug-2024

5

This course brilliantly combines theoretical insights with practical applications, empowering learners to master Bayesian inference and graphical models. It skillfully enhances your ability to navigate uncertainty, making it an invaluable resource for aspiring data scientists and machine learning professionals. Highly recommended!

Andrew Mendoza

05-Aug-2024

5

This course was exceptional! The in-depth exploration of Bayesian inference and graphical models provided invaluable insights. The blend of theory and practical applications empowered me to tackle real-world problems confidently. The engaging instruction and clear focus on uncertainty have truly elevated my skills for advanced roles in data science. Highly recommend!

Jessica Howard

05-Aug-2024

5

This course was truly exceptional! The curriculum seamlessly blended theory and practical applications, making complex concepts accessible. The focus on Bayesian inference and graphical models enriched my understanding and enabled me to effectively tackle real-world problems. It’s an invaluable stepping stone for anyone serious about excelling in data science!

Justin Rodriguez

05-Aug-2024

3

This course provides a comprehensive introduction to probabilistic methods in machine learning, with strong coverage of Bayesian inference and graphical models. The blend of theory and practical application enhances understanding and equips learners to tackle real-world challenges. However, some sections may be dense for beginners, potentially overwhelming those new to the subject. Overall, it successfully prepares participants for advanced roles in data science and machine learning.

Jessica White

04-Aug-2024

5

This course excels in its comprehensive approach to probabilistic methods, particularly Bayesian inference and graphical models. The blend of theory and practical applications empowers learners to tackle real-world challenges confidently. It effectively prepares participants for advanced roles in data science and machine learning by enhancing their decision-making skills.

Sean Morales

04-Aug-2024

5

An exceptional course! It masterfully combines theory and practical applications, empowering participants with essential skills in Bayesian inference and graphical models for real-world data challenges.

Madison Mitchell

04-Aug-2024

5

This course is a fantastic deep dive into the world of probabilistic methods in machine learning. The blend of theoretical insights and practical applications truly enhances understanding. The focus on Bayesian inference and graphical models equips participants with essential skills for tackling real-world challenges. I feel more confident in handling uncertainty and making data-driven decisions. Highly recommend it for anyone looking to advance in data science!

Arthur Roberts

04-Aug-2024

5

This course excels in blending theory with practical application, empowering learners with essential skills in Bayesian inference and graphical models. It's an invaluable resource for mastering uncertainty and making informed data-driven decisions in machine learning.

Cynthia Edwards

04-Aug-2024

4

This course provides a rich understanding of probabilistic methods, emphasizing Bayesian inference and graphical models. The balance of theory and practical application is commendable, making it suitable for real-world problem-solving. While the material is comprehensive, additional hands-on projects would enhance the learning experience even further.

Edward Mendoza

03-Aug-2024

5

An exceptional course that expertly combines theory and practice, empowering students to confidently tackle real-world challenges in data science!

Isabella Allen

03-Aug-2024

5

This course brilliantly merges theory and practice, empowering learners with essential skills in Bayesian inference and graphical models. It's an invaluable resource for anyone looking to excel in data science!

Eric Howard

01-Aug-2024

5

Transformative experience; empowers skills in uncertainty and decision-making.

Scarlett Nelson

01-Aug-2024

5

Excellent course for mastering probabilistic methods and applying them to real-world data challenges!

Jeremy Reyes

31-Jul-2024

5

This course is a fantastic journey into the world of probabilistic methods! Engaging content, real-world applications, and practical skills make it a must for anyone serious about data science. Highly recommended!

John Harris

30-Jul-2024

5

An exceptional course that masterfully combines theory and practice, empowering learners with essential skills for advanced data-driven decision-making. Highly recommended!

$9.99

$109.99

Lectures

24

Skill level

Beginner

Expiry period

Lifetime

Certificate

Yes
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