Prompt Engineering & LLM Fine-tuning

What you'll learn

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

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

Description

Master the art of prompt engineering and fine-tuning LLMs. Learn RLHF, LoRA, and QLoRA techniques to customize language models for specific business use cases.

What this course covers: Prompt Engineering & LLM Fine-tuning works through neural network architectures, working with Python libraries such as NumPy, pandas and scikit-learn, model training and evaluation and feature engineering and data preparation.

Skills you will build: deploying models to production, tuning model performance and preparing data for training.

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 neural network architectures and deploying models to production 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

• S
  Shreya C. ★★★★★ 4.5/5 Sample review

  The section on model training and evaluation was explained step by step, and the examples made feature engineering and data preparation much easier to follow. Loved that model evaluation got as much attention as model building.

• P
  Priyanka S. ★★★★★ 5.0/5 Sample review

  I joined to get better at tuning model performance. The practical exercises on working with Python libraries such as NumPy, pandas and scikit-learn were exactly what I needed. The hands-on notebooks made backpropagation finally make sense to me.

• S
  Swathi G. ★★★★★ 5.0/5 Sample review

  Prompt Engineering & LLM Fine-tuning covered neural network architectures in more depth than I expected. The pace was right for my level. The deployment section filled a gap most ML courses skip.

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