Generative AI Mastery: From Zero to Expert

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 feature engineering and data preparation

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

Description

A comprehensive course covering all aspects of Generative AI including LLMs, diffusion models, and prompt engineering. Gain hands-on experience building AI-powered applications from scratch.

What this course covers: Generative AI Mastery: From Zero to Expert works through working with Python libraries such as NumPy, pandas and scikit-learn, feature engineering and data preparation, supervised and unsupervised learning and neural network architectures.

Skills you will build: deploying models to production, preparing data for training and building and evaluating ML models.

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 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

• M
  Madhuri I. ★★★★★ 4.0/5 Sample review

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

• K
  Kavya S. ★★★★★ 4.5/5 Sample review

  I joined to get better at preparing data for training. The practical exercises on feature engineering and data preparation were exactly what I needed. Loved that model evaluation got as much attention as model building.

• K
  Kabir J. ★★★★★ 5.0/5 Sample review

  Clear structure from working with Python libraries such as NumPy, pandas and scikit-learn through to feature engineering and data preparation. I'd like a few more practice tasks, but overall very useful. The hands-on notebooks made backpropagation finally make sense to me.

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