Reinforcement Learning Basics

What you'll learn

• Building and evaluating ML models
• Preparing data for training
• Tuning model performance
• Deploying models to production
• Understand working with Python libraries such as NumPy, pandas and scikit-learn
• Understand neural network architectures

Planned outline shown above (sample metadata). The detailed module and lesson list for this course hasn't been published yet.

Description

Master reinforcement learning basics with hands-on projects, real-world examples, and expert guidance designed for ai courses learners.

What this course covers: Reinforcement Learning Basics works through working with Python libraries such as NumPy, pandas and scikit-learn, neural network architectures, transformers and large language models and deep learning frameworks (TensorFlow / PyTorch).

Skills you will build: building and evaluating ML models, deploying models to production and tuning model performance.

Practical applications: prediction and classification systems, computer vision and NLP applications and AI features inside products.

Who should take this course: developers, data analysts and engineering students moving into machine learning and AI. No prior experience is needed — the course starts from the basics.

Learning outcomes: by the end of the course you should be able to apply working with Python libraries such as NumPy, pandas and scikit-learn and building and evaluating ML models to your own work or projects, and know which areas to practise next.

Requirements

• No prior experience required
• A computer or smartphone with internet access
• Time to practise each week

Your instructor

• D
  Dev P. ★★★★★ 4.5/5 Sample review

  I joined to get better at tuning model performance. The practical exercises on transformers and large language models were exactly what I needed. The hands-on notebooks made backpropagation finally make sense to me.

• N
  Nithya V. ★★★★★ 4.5/5 Sample review

  The section on neural network architectures was explained step by step, and the examples made transformers and large language models much easier to follow. The deployment section filled a gap most ML courses skip.

• B
  Bhavna S. ★★★★★ 4.5/5 Sample review

  Good balance of theory and practice. The working with Python libraries such as NumPy, pandas and scikit-learn lessons helped me apply building and evaluating ML models in real situations. Loved that model evaluation got as much attention as model building.

Sample reviews are illustrative demo content, not verified purchases. Ratings from learners will appear here once submitted.