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Aprendizaje Por Refuerzo

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Overview

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Learning outcomes

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Course content

1

Introducción Al Aprendizaje Por Refuerzo

2

Fundamentos De Aprendizaje Automático

3

Algoritmos De Refuerzo

4

Aprendizaje Por Refuerzo En Entornos Dinámicos

5

Aplicaciones Prácticas Del Aprendizaje Por Refuerzo

Career Path

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Key facts

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Why this course

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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
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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

The Aprendizaje Por Refuerzo course exceeded my expectations. The curriculum was aligned with my goal of mastering reinforcement learning for predictive analytics, and the hands‑on projects—especially the multi‑armed bandit simulation—gave me concrete skills I could apply at work. The lecture videos were clear, the supplemental reading materials were up‑to‑date with the latest research, and the weekly quizzes reinforced the concepts effectively. Overall, the professional tone of the instruction and the relevance of the content helped me deliver a successful pilot project for my company, so I highly recommend this course.

LS
Lucas Silva
BR · Course completed

Fiquei impressionado com o quanto o curso me ajudou a alcançar meus objetivos de aprender a usar aprendizagem por reforço em jogos. Os exemplos práticos, como treinar um agente para jogar "Snake" no ambiente OpenAI Gym, foram super úteis. O material didático era bem organizado e os vídeos tinham legendas em português, o que facilitou a compreensão. No fim, consegui criar um pequeno bot que já está rodando no meu projeto pessoal, então a experiência foi muito positiva.

FW
Felix Wagner
DE · Course completed

Der Kurs war genau das, was ich gesucht habe, um meine Kenntnisse im Bereich Reinforcement Learning zu vertiefen. Besonders gut gefallen hat mir das Kapitel über Q‑Learning, das ich direkt in meinem Forschungsprojekt zu automatisierten Handelssystemen einsetzen konnte. Die Materialien, inklusive der gut kommentierten Jupyter‑Notebooks, waren von hoher Qualität und stets aktuell. Der detaillierte und dennoch verständliche Stil des Dozenten machte das Lernen zu einem echten Vergnügen, und ich fühle mich jetzt sicher genug, um eigene Experimente zu planen.

HT
Hiroshi Tanaka
JP · Course completed

とても楽しかったです!このコースで強化学習の基礎から実践的なスキルまでしっかり学べました。特に、ロボットアームを制御するプロジェクトで、ポリシー勾配法を実装したことが自分のキャリアに直結しています。教材は分かりやすく、日本語字幕もあり、学習体験がスムーズでした。全体的に満足していて、次のステップとしてもっと高度なアルゴリズムに挑戦したいです。





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Recently updated!

May 2026