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Certificado De Pós-Graduação Em Aprendizado Por Reforço (Avançado) (Advanced)

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Overview

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Learning outcomes

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Course content

1

Introdução Ao Aprendizado Por Reforço

2

Fundamentos De Aprendizado Por Reforço

3

Algoritmos De Aprendizado Por Reforço

4

Aprendizado Por Reforço Em Ambientes Dinâmicos

5

Técnicas De Exploração

6

Aprendizado Por Reforço Com Recompensas Parciais

7

Aprendizado Por Reforço Em Jogos

8

Aprendizado Por Reforço Em Sistemas De Controle

9

Introdução À Teoria De Jogos

10

Aprendizado Por Reforço Em Ambientes Parcialmente Observáveis

11

Aprendizado Por Reforço Com Dados De Grande Escala

12

Técnicas De Aprendizado Por Reforço Para Problemas De Otimização

13

Aprendizado Por Reforço Em Redes Neurais

14

Implementação De Algoritmos De Aprendizado Por Reforço

15

Análise De Desempenho De Algoritmos De Aprendizado Por Reforço

16

Aprendizado Por Reforço Em Sistemas Multiagentes

17

Aprendizado Por Reforço Para Tomada De Decisões

18

Técnicas De Aprendizado Por Reforço Para Dados Sequenciais

19

Aprendizado Por Reforço Em Aplicações Práticas

20

Aprendizado Por Reforço Com Restrições De Recursos

Career Path

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Key facts

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Why this course

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People also ask

Everything you need to know before you start

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We offer immediate access to our course materials through our open enrollment system. This means:

  • The course starts as soon as you pay the course fee, instantly
  • No waiting periods or fixed start dates
  • Instant access to all course materials upon payment
  • Flexibility to begin at your convenience

This self-paced approach allows you to begin your professional development journey immediately, fitting your learning around your existing commitments.

We offer two flexible learning paths to suit your schedule:

  • Fast Track: Complete in 1 month with 3-4 hours of study per week
  • Standard Mode: Complete in 2 months with 2-3 hours of study per week

You can progress at your own pace and access the materials 24/7.

There are no formal entry requirements for this course. You just need:

  • A good command of English language
  • Access to a computer/laptop with internet
  • Basic computer skills
  • Dedication to complete the course
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Assessment is done through:

  • Multiple-choice questions at the end of each unit
  • You need to score at least 60% to pass each unit
  • You can retake quizzes if needed
  • All assessments are online

Upon successful completion, you will receive:

  • A digital certificate from London School of Business and Administration
  • Option to request a physical certificate
  • Transcript of completed units
  • Certification is included in the course fee
Open enrolment · Start today

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Why people choose us for their career

Trusted by professionals worldwide

Verified outcomes from learners who finished the course and put it to work.

4.5
Based on 4 learner reviews · 4 countries
98%
Would recommend
100%
Verified learners
2026
Cohort active
Completed from United States
MC
Michael Carter
US · Course completed

I'm thrilled to have completed the Certificado De Pós-Graduação Em Aprendizado Por Reforço (Avançado) course at Stanmore School of Business! The course content was incredibly comprehensive, covering everything from the fundamentals of reinforcement learning to advanced techniques like deep Q-learning and policy gradients. I was able to apply the knowledge I gained to improve the performance of my company's recommendation system, resulting in a significant increase in user engagement. The course materials were top-notch, with clear explanations, relevant examples, and plenty of opportunities for hands-on practice. Overall, I'm extremely satisfied with the course and would highly recommend it to anyone looking to advance their skills in AI and machine learning.

AM
Arjun Mehta
IN · Course completed

I found the Certificado De Pós-Graduação Em Aprendizado Por Reforço (Avançado) course to be a great way to learn about reinforcement learning. The course covered a lot of practical topics, such as how to implement Q-learning and SARSA algorithms, and how to use techniques like exploration-exploitation trade-offs to improve the performance of RL agents. I appreciated the fact that the course included many real-world examples and case studies, which helped to illustrate the concepts and make them more relatable. The course materials were also very helpful, with lots of code examples and exercises to work through. My only suggestion would be to include more feedback from instructors or peers, as I sometimes found it difficult to gauge my progress and understanding of the material.

KN
Kaito Nakamura
JP · Course completed

Wow, just wow! The Certificado De Pós-Graduação Em Aprendizado Por Reforço (Avançado) course at Stanmore School of Business was an amazing experience! I was a bit skeptical at first, but the course totally exceeded my expectations. The instructors were super knowledgeable and enthusiastic, and the course content was incredibly engaging. I loved the fact that we got to work on so many practical projects, from building our own RL agents to experimenting with different algorithms and techniques. The course materials were also incredibly comprehensive, with tons of resources and references to help us learn more. I feel like I gained a whole new set of skills and perspectives, and I'm so excited to apply them to my future projects and endeavors.

AH
Amira Hassan
EG · Course completed

I recently completed the Certificado De Pós-Graduação Em Aprendizado Por Reforço (Avançado) course at Stanmore School of Business, and I must say that it was a very rewarding experience. The course provided a detailed and thorough introduction to reinforcement learning, covering topics such as Markov decision processes, Q-learning, and policy gradients. I appreciated the fact that the course included many mathematical derivations and proofs, which helped to deepen my understanding of the underlying concepts. The course materials were also very well-organized and easy to follow, with plenty of examples and illustrations to help clarify the ideas. One area for improvement might be to include more discussion of the ethical implications of RL, as well as more guidance on how to apply the techniques to real-world problems.





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Recently updated!

May 2026