Learn Reinforcement Learning Online: Free Course with AI Tutor & Certificate
You'll build a strong foundation in machine learning algorithms through this reinforcement learning course. Over 8 hours, you will complete 6 modules and 35 video lessons. The curriculum covers supervised and unsupervised learning techniques, parameter estimation, and feature selection to prepare you for advanced AI concepts.
What you'll learn in this Reinforcement Learning course
Five concrete outcomes you'll walk away with, each mapped to modules in the curriculum below.
Understand the fundamentals of machine learning and its applications.
Differentiate between supervised and unsupervised learning techniques.
Apply various supervised learning techniques such as linear regression, logistic regression, decision trees, etc.
Apply various unsupervised learning techniques such as clustering and dimensionality reduction.
Gain knowledge of parameter estimation and feature selection techniques in machine learning.
Reinforcement Learning course syllabus
01 Introduction To Machine Learning
- What is Machine Learning?
- Why You Should Learn Machine Learning?
- Roadmap To Become ML Expert
- Is Machine Learning Career Worth It?
- How To Use ChatGPT for Machine Learning?
02 Machine Learning Core Elementals
- Difference between Supervised Learning and Statsitical Modelling.
- Difference between Supervised Learning and Unsupervised Learning.
- Data Normalization
- Datasets for Machine Learning
- Different Paradigms of Machine Learning
- Hypothesis Evaluation
03 Supervised Learning Techniques
- Linear Regression.
- Multi linear Regression.
- Logistic Regression
- K-Nearest Neighbours
- Decision Tree
- Random Forest
- Boosting
- SVMs (primal and dual forms)
- Naive Bayes
04 Unsupervised Learning Techniques
- K-Means Clustering
- Hierarchical Clustering
- Gaussian Mixture Modeling
- Apriori Algorithm
- Singular Value Decomposition (SVD).
05 Parameter Estimation
- Bayesian Parameter Estimation
- Bias-Variance Tradeoff
- Maximum Likelihood
- VC-Dimensions and Distribution
06 Feature Selection and Dimensionality Reduction
- Independent Component Analysis
- Linear Discriminant Analysis.
- Principal Component Analysis.
- Principal Component Analysis Implementation python
- Step Backward Feature Selection.
- Step Forward Feature Selection.
Skills you'll gain
Add these to your LinkedIn profile and resume the day you finish.
Who should take this Reinforcement Learning course?
And exactly what you need before starting (spoiler: almost nothing).
This course is for
- Aspiring data scientists wanting a structured reinforcement learning tutorial to understand core ML concepts.
- Software developers in India looking to transition into artificial intelligence and machine learning roles.
- Students seeking a free reinforcement learning course to supplement their university computer science curriculum.
- Data analysts who want to move beyond basic statistics and apply predictive modeling techniques.
Prerequisites
- Learners should have a basic understanding of Python programming and high school mathematics.
- Familiarity with basic statistics and probability concepts will help you grasp parameter estimation faster.
- No prior machine learning experience is required to begin the first module.
How learning works on LearnTube
The structure of a bootcamp, the price of free.
AI curates your path
Our AI picks the top 1% of video content for each topic and arranges it into a structured, goal-based curriculum. No more tutorial roulette.
Learn, then get quizzed
After every lesson, your AI tutor asks you a question and gives personalised feedback on your answer, so gaps get fixed immediately.
Finish with proof
Complete the course, build the capstone project, and claim a certificate recognised by 900+ hiring partners.
A complete Reinforcement Learning course: free, structured and job-focused
You'll apply linear regression, logistic regression, and decision trees to real data by the end of this reinforcement learning course. We start by asking, "What is Machine Learning?" before moving directly into the Machine Learning Core Elementals. You will learn to differentiate between supervised and unsupervised learning techniques, giving you the practical tools needed to train predictive models. The curriculum also covers Parameter Estimation and Feature Selection and Dimensionality Reduction, ensuring you understand how to optimize your algorithms.
LearnTube makes mastering these concepts straightforward and interactive. We curate top YouTube video lessons and pair them with an AI tutor so you don't just watch passively. You'll take a quick quiz after every single lesson to reinforce what you just learned. This active recall helps you retain complex topics like clustering and dimensionality reduction much faster than traditional video platforms.
The entire 8-hour learning experience is completely free. You can access all 6 modules and 35 video lessons at no cost, joining over 60,000 learners on our platform. If you want to highlight your new skills to employers, you can choose to purchase an optional paid certificate upon completion. This flexible approach lets you learn at your own pace while building a solid foundation for advanced deep reinforcement learning tutorials.
Jobs you can target after learning Reinforcement Learning
Reinforcement Learning is a gateway skill for some of the fastest-growing roles in tech.
Designs and trains predictive models using supervised and unsupervised learning techniques.
Analyzes complex datasets and builds algorithms to solve business problems.
Integrates machine learning capabilities and decision trees into software applications.
Typical entry-level salary ranges in India as of September 2026. Actual compensation varies by city, company and experience.
Earn a Reinforcement Learning certificate employers recognise
Finish the course and claim a certificate you can share anywhere.
- Learn 100% free: every lesson, quiz and the AI tutor cost nothing
- Claim your certificate when you complete the course (an optional paid add-on)
- Recognised by 900+ hiring partners across India
- One click to share on LinkedIn and add to your resume
Why learners choose LearnTube
Real reviews from Google. You learn free, you get assessed free, and you pay only if you want the certificate.
Though I have 11 years of experience as an HRBP, the course helped me refresh my knowledge and gave me insights into new trends in the market. The modules were clear and easy to understand, and the quizzes were well designed. I have added the certificate to my LinkedIn profile.
I had a lot of doubts while learning, but the AI chatbot answered them in detail with simple summaries. The videos on the platform saved me a lot of time as I didn't have to search YouTube and watch random content.
Just earned my certificate in HR Management from LearnTube.ai! The platform is user-friendly and the assessment was a great way to validate my knowledge. Feeling proud of this accomplishment!
I enrolled in a LearnTube personalized course to get into a Data Analytics role. It helped me learn SQL in depth and gave me hands-on experience with Power BI. I attached the LearnTube certificate to my CV, it added credibility to my profile, and I'm currently in the interview process.
The interface is user-friendly and intuitive, making it easy to navigate the course. I encountered no errors during my time using the platform, which was a refreshing change from other online learning platforms.
Just cleared the Business Analyst assessment and got certified! Loved how the course simplifies big concepts like data-driven decision making into short, practical lessons. The automated quizzes kept me on track the whole time. Perfect boost for my resume!
The certificate gave me a real edge during my B.Tech. By the time I graduated, I had skills that set me apart from my peers, and the certification opened up more job opportunities. It gave me the confidence to stand out during interviews.
I manage many software projects as a General Manager and was looking for a good certification program to stay competitive. After researching many programs, I chose LearnTube and it was much better than I expected. The material is well-organized and practical, and what I liked most is the lifetime access.
Frequently asked questions
Everything learners ask before starting this Reinforcement Learning course.
How does this differ from plain YouTube tutorials?
LearnTube organizes curated YouTube videos into a structured reinforcement learning syllabus. You get an AI tutor that generates a quiz after every video. This ensures you actively practice the concepts instead of just watching passively.
What tools or software do I need installed?
You will need Python installed on your computer along with standard data science libraries like Jupyter Notebook, NumPy, and scikit-learn. A modern web browser is all you need to access the video lessons and AI quizzes.
Is prior machine learning experience needed to enroll?
No prior ML experience is necessary. The course begins with an Introduction To Machine Learning and explains core elementals from scratch. However, basic Python programming and math skills will make the learning process much smoother.
What job roles and salaries does this training lead to?
Completing this foundational training prepares you for entry-level Machine Learning Engineer and Data Scientist roles. In India, these positions typically offer starting salaries between ₹6 LPA and ₹12 LPA depending on your programming background.
Is this reinforcement learning course completely free?
Yes. All 6 modules, 35 video lessons, and AI tutor quizzes are entirely free to access. You only pay if you decide to purchase the optional certificate after finishing the curriculum.
Can I learn at my own pace?
Absolutely. The entire 8-hour curriculum is self-paced. You can watch the lessons, complete the quizzes, and review the supervised learning techniques whenever it fits your personal schedule.
Will I learn deep reinforcement learning in this class?
This specific course focuses heavily on the essential machine learning foundations required before tackling deep RL. You will master parameter estimation, feature selection, and supervised learning to prepare for advanced topics.
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