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Find top Reinforcement Learning tutors - learn Reinforcement Learning today

Master Reinforcement Learning from our Reinforcement Learning tutors, mentors, and teachers who will personalize a study plan to help you refine your Reinforcement Learning skills. Find the perfect Reinforcement Learning tutor now.

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Learn Reinforcement Learning with online tutors

  • Learn Reinforcement Learning with Reinforcement Learning tutors - Tanisha Bhayani

    Tanisha Bhayani

    Reinforcement Learning tutor

    US$32.00 /15 min

    284 reviews

    6 years of experience in building and managing AI Systems. * I develop end-to-end AI systems from requirements analysis, and data gathering to deployment, implementing new methods/research papers, and turning projects into research outcomes. I have achieved substantial performance in DL/ML/RL models for CV and NLP domain problems. * Implementing and deploying projects handling and maintaining scalability, research papers as well as POCs. * Project planning, requirements gathering, and analysing requirements to define the system's architecture, and implementation timeline. * Provide mentoring to junior developers for ML projects. Website: [roshantanisha.github.io/?ref=codementor](http://roshantanisha.github.io/?ref=codementor) Summary: I like to work on technology that is smart, simple and sophisticated. This sums up the vast knowledge required to work on projects to excel in a working product. I like to train Deep Neural Networks and understand them well. I have mentored many students for their AI careers, teaching them Machine Learning and Mathematics. I am a mentor for the RFS (Reach for the Stars) Programme by the Aga Khan Education Board for India. I am an alumnus of this program as well. I have a cumulative experience of 6 years working in the product and service-based industry for creating Machine Learning projects. I have done some innovative work that I am proud of and am continuing to do so. I try my best to contribute my expertise to the project I am working on. Highly Experienced in Machine Learning, Deep Learning, Advanced Deep Learning, Artificial Intelligence, and Algorithms, including models in the production environment, and deploying ML models. Working with top Indian colleges like BITS, niche NLP and CV, real-estate startups, MNCs, and Fortune top 20 companies, working with sensitive anonymized datasets, and creating state-of-the-art models are some of my achievements. I have strong and correct knowledge of Deep Learning concepts from the above experiences.

  • Learn Reinforcement Learning with Reinforcement Learning tutors - Abdelfettah Besbes

    Abdelfettah Besbes

    Reinforcement Learning tutor

    US$30.00 /15 min

    847 reviews

    Embedded Systems Engineer and Certified Tensorflow Developer with a strong technical/coding background, including C, C++, Java, Python and Assembly. Additionally skilled back-end development based on Spring/Django Framework, and Machine Learning with Tensorflow and Pytorch.

  • Learn Reinforcement Learning with Reinforcement Learning tutors - Satwik Kansal

    Satwik Kansal

    Reinforcement Learning tutor

    US$18.00 /15 min

    100 reviews

    https://satwikkansal.xyz I have been freelancing since 2k17. I love the process of turning ideas into code, and you can count on me for writing good quality code. Aside from freelancing, I've authored a popular developer project called "What the f*ck Python?", and I've developed multiple courses & tutorials for many companies including IBM, Packt, Educative and others. Presently, I'm working on a book on Python for O'Reilly. My areas of expertise include Backend System, Automated Trading Systems, and Decentralized Applications (a.k.a Dapps on the blockchain). I've worked with a few startups in the past (including a couple of my own), helping me learn how to build products in a fast yet cost-effective way. I try to actively engage with Open Source organizations and occasionally write looong tutorials on specific technical topics. Luckily, some of my works were trending on HackerNews, Github, Newsletters, and Podcasts like PythonBytes, PythonWeekly, DataElixir, and more. If there's an interesting opportunity to discuss, please drop me a message, I usually get back to messages in a day. Always excited to work on cool projects :)

  • Learn Reinforcement Learning with Reinforcement Learning tutors - Mika

    Mika

    Reinforcement Learning tutor

    US$20.00 /15 min

    78 reviews

    As a senior software engineer and a tech lead with 9+ years of professional experience, I like to help others and solve their problems. I've worked with several silicon valley companies and I'm the owner of top trending open source project on GitHub. You can check my profile at https://github.com/alirezamika Feel free to contact me, I would like to help. I won't charge unless you are satisfied.

  • Learn Reinforcement Learning with Reinforcement Learning tutors - Gopal Chitalia

    Gopal Chitalia

    Reinforcement Learning tutor

    US$20.00 /15 min

    71 reviews

    I am [Gopal Chitalia](https://scholar.google.com/citations?user=QncMQEIAAAAJ&hl=en), a graduate student at Purdue University and working with Prof. [Jan Anders Mansson](https://www.purdue.edu/engineering/mdlab/mansson/) on fault detection in motors using deep learning in a joint project with Wistron. Additionally, I'm working with Prof. [Junjie Qin](https://engineering.purdue.edu/people/junjie.qin.1) on transfer learning application using LLMs for optimizing power networks. I have had the privilege of working at [Center for Building Science Lab](https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcbs.iiit.ac.in%2F&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=UCjKsb98EbNq8jGvbf%2F5TEETyHMI1gnMx20lLBeQmog%3D&reserved=0) under the supervision of Prof. [Vishal Garg](https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscholar.google.co.in%2Fcitations%3Fuser%3DvyH26MIAAAAJ%26hl%3Den&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=pDBa4RZtT0eg4rN%2FdvVtv3q%2F6goK1EEVT6%2F6UeGPcw4%3D&reserved=0). I have previously been a Research Assistant/Visiting ML Scholar at the [Smart Grid Research Unit](https://nam04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.sgru.eng.chula.ac.th%2F&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=UAp49aqIF2oxM3WIO5IS68gTyMLeYsKR5ENaosJMhvk%3D&reserved=0) (SGRU) under [Manisa Pipattanasomporn](https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscholar.google.com.sg%2Fcitations%3Fuser%3D4W2KIQkAAAAJ%26hl%3Den&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=Pey86yYp3KpRPj9d9a97nzxLxQGMkQrN%2BFASqUqyheQ%3D&reserved=0) (Adjunct Faculty, Virginia Tech). In addition to my academic pursuits, I have also contributed to the industry in the domains of Energy Efficiency, IoT, and Machine Learning. I have worked at [ClevAir](https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fclevair.io%2Fen%2F&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=nonCkudNp3cFUL2CNs6rMHm0HyQXSnh9lhoJ76s5xus%3D&reserved=0) (Norway) as a **Data Scientist** and Growthworks.ai (CA, USA) as an **ML Scientist/Energy Demand Expert**. My research work on predicting short/long-term energy prediction in office/residential buildings using machine learning is published in [Applied Energy](https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fabs%2Fpii%2FS0306261920309223%3Fvia%253Dihub&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=V%2Bq5MVSeJ7deNpE%2F%2FEP1PYrsrEVWEH%2FwNK1yAG4CPlM%3D&reserved=0). The results improve the state-of-the-art results by 20-40%. I am also an active reviewer at Applied Energy. Furthermore, I have collaborated on a building-level dataset paper which has been published in [Nature Scientific Data](https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nature.com%2Fsdata%2F&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=rtv6fVxtvuYsk5NAwwR8ujauC1AfX9cs32w4evIj%2BZU%3D&reserved=0). Previously, I have also worked with Prof. [Jyotirmay Mathur](https://scholar.google.co.in/citations?user=AmGPPL4AAAAJ&hl=en) at the [Centre for Energy & Environment](http://www.mnit.ac.in/dept_cree/index.php), MNIT Jaipur on Predicting time ahead heating/cooling energy demand HVAC systems. Moreover, as an independent research student, I also collaborated with Prof. [Praveen Paruchuri](https://scholar.google.com/citations?user=ILUqgKEAAAAJ&hl=en) of [Machine Learning Lab](https://mll.iiit.ac.in/), IIIT-H for Reinforcement Learning (RL) applications for controlling HVAC systems.

  • Learn Reinforcement Learning with Reinforcement Learning tutors - Miklos Kepes

    Miklos Kepes

    Reinforcement Learning tutor

    US$13.00 /15 min

    69 reviews

    My name is Miklos, a friendly machine learning engineer/applied scientist currently based in London. **Background** I studied Computer Science & AI at Imperial College London and have been working as a ML engineer/applied scientist since. I have extensive experience with Python and its machine learning libraries especially Pytorch, Pandas, Numpy. I have also worked with other languages including Java, SQL, C. One of my main interests is Reinforcement Learning, as part of my master's project I used Deep RL for simulated robotics, and I have worked on several projects since. **I offer teaching & mentoring** - Python programming: teaching from beginner to advanced, assistance with projects, code reviews, freelance jobs - Assistance with ML projects, especially Pytorch, Pandas, Numpy - Assistance with RL projects, experienced with algorithms DQN, AC, DDPG, PPO, and MARL to some extent - I can also offer some help with questions concerning evolutionary algorithms, cellular automata, multi-agent systems - Event-based modelling Please try to book in advance. Don't hesitate to get in touch, I am looking forward to working with you!

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    Chris Potempa

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    Emmanuel Gbenga

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    Learn Reinforcement Learning with Reinforcement Learning tutors - David Ingle

    David Ingle

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Frequently asked questions

Learning Reinforcement Learning effectively takes a structured approach, whether you're starting as a beginner or aiming to improve your existing skills. Here are key steps to guide you through the learning process:

  • Understand the basics: Start with the fundamentals of Reinforcement Learning. You can find free courses and tutorials online that cater specifically to beginners. These resources make it easy for you to grasp the core concepts and basic syntax of Reinforcement Learning, laying a solid foundation for further growth.
  • Practice regularly: Hands-on practice is crucial. Work on small projects or coding exercises that challenge you to apply what you've learned. This practical experience strengthens your knowledge and builds your coding skills.
  • Seek expert guidance: Connect with experienced Reinforcement Learning tutors on Codementor for one-on-one mentorship. Our mentors offer personalized support, helping you troubleshoot problems, review your code, and navigate more complex topics as your skills develop.
  • Join online communities: Engage with other learners and professionals in Reinforcement Learning through forums and online communities. This engagement offers support, new learning resources, and insights into industry practices.
  • Build real-world projects: Apply your Reinforcement Learning skills to real-world projects. This could be anything from developing a simple app to contributing to open source projects. Using Reinforcement Learning in practical applications not only boosts your learning but also builds your portfolio, which is crucial for career advancement.
  • Stay updated: Since Reinforcement Learning is continually evolving, staying informed about the latest developments and advanced features is essential. Follow relevant blogs, subscribe to newsletters, and participate in workshops to keep your skills up-to-date and relevant.

The time it takes to learn Reinforcement Learning depends greatly on several factors, including your prior experience, the complexity of the language or tech stack, and how much time you dedicate to learning. Here’s a general framework to help you set realistic expectations:

  • Beginner level: If you are starting from scratch, getting comfortable with the basics of Reinforcement Learning typically takes about 3 to 6 months. During this period, you'll learn the fundamental concepts and begin applying them in simple projects.
  • Intermediate level: Advancing to an intermediate level can take an additional 6 to 12 months. At this stage, you should be working on more complex projects and deepening your understanding of Reinforcement Learning’s more advanced features and best practices.
  • Advanced level: Achieving proficiency or an advanced level of skill in Reinforcement Learning generally requires at least 2 years of consistent practice and learning. This includes mastering sophisticated aspects of Reinforcement Learning, contributing to major projects, and possibly specializing in specific areas within Reinforcement Learning.
  • Continuous learning: Technology evolves rapidly, and ongoing learning is essential to maintain and improve your skills in Reinforcement Learning. Engaging with new developments, tools, and methodologies in Reinforcement Learning is a continuous process throughout your career.

Setting personal learning goals and maintaining a regular learning schedule are crucial. Consider leveraging resources like Codementor to access personalized mentorship and expert guidance, which can accelerate your learning process and help you tackle specific challenges more efficiently.

The cost of finding a Reinforcement Learning tutor on Codementor depends on several factors, including the tutor's experience level, the complexity of the topic, and the length of the mentoring session. Here is a breakdown to help you understand the pricing structure:

  • Tutor experience: Tutors with extensive experience or high demand skills in Reinforcement Learning typically charge higher rates. Conversely, emerging professionals might offer more affordable pricing.
  • Pro plans: Codementor also offers subscription plans that provide full access to all mentors and include features like automated mentor matching, which can be a cost-effective option for regular, ongoing support.
  • Project-based pricing: If you have a specific project, mentors may offer a flat rate for the complete task instead of an hourly charge. This range can vary widely depending on the project's scope and complexity.

To find the best rate, browse through our Reinforcement Learning tutors’ profiles on Codementor, where you can view their rates and read reviews from other learners. This will help you choose a tutor who fits your budget and learning needs.

Learning Reinforcement Learning with a dedicated tutor from Codementor offers several significant benefits that can accelerate your understanding and proficiency:

  • Personalized learning: A dedicated tutor adapts the learning experience to your specific needs, skills, and goals. This personalization ensures that you are not just learning Reinforcement Learning, but exceling in a way that directly aligns with your objectives.
  • Immediate feedback and assistance: Unlike self-paced online courses, a dedicated tutor provides instant feedback on your code, concepts, and practices. This immediate response helps eliminate misunderstandings and sharpens your skills in real-time, making the learning process more efficient.
  • Motivation and accountability: Regular sessions with a tutor keep you motivated and accountable. Learning Reinforcement Learning can be challenging, and having a dedicated mentor ensures you stay on track and continue making progress towards your learning goals.
  • Access to expert insights: Dedicated tutors often bring years of experience and industry knowledge. They can provide insights into best practices, current trends, and professional advice that are invaluable for both learning and career development.
  • Career guidance: Tutors can also offer guidance on how to apply Reinforcement Learning in professional settings, assist in building a relevant portfolio, and advise on career opportunities, which is particularly beneficial if you plan to transition into a new role or industry.

By leveraging these benefits, you can significantly improve your competency in Reinforcement Learning in a structured, supportive, and effective environment.

Personalized Reinforcement Learning mentoring through Codementor offers a unique and effective learning approach compared to traditional classroom learning, particularly in these key aspects:

  • Customized content: Personalized mentoring adapts the learning material and pace specifically to your needs and skill level. This means the sessions can focus on areas where you need the most help or interest, unlike classroom settings which follow a fixed curriculum for all students.
  • One-on-one attention: With personalized mentoring, you receive the undivided attention of the tutor. This allows for immediate feedback and detailed explanations, ensuring that no questions are left unanswered, and concepts are fully understood.
  • Flexible scheduling: Personalized mentoring is arranged around your schedule, providing the flexibility to learn at times that are most convenient for you. This is often not possible in traditional classroom settings, which operate on a fixed schedule.
  • Pace of learning: In personalized mentoring, the pace can be adjusted according to how quickly or slowly you grasp new concepts. This custom pacing can significantly enhance the learning experience, as opposed to a classroom environment where the pace is set and may not align with every student’s learning speed.
  • Practical, hands-on learning: Mentors can provide more practical, hands-on learning experiences tailored to real-world applications. This direct application of skills is often more limited in classroom settings due to the general nature of the curriculum and the number of students involved.

Personalized mentoring thus provides a more tailored, flexible, and intensive learning experience, making it ideal for those who seek a focused and practical approach to mastering Reinforcement Learning.

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