Generative Adversarial Networks

What you'll learn

• Building and evaluating ML models
• Preparing data for training
• Tuning model performance
• Deploying models to production
• Understand model training and evaluation
• Understand supervised and unsupervised learning

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

Description

A comprehensive course on generative adversarial networks, covering core concepts, practical applications, and industry best practices.

What this course covers: Generative Adversarial Networks works through model training and evaluation, supervised and unsupervised learning, working with Python libraries such as NumPy, pandas and scikit-learn and feature engineering and data preparation.

Skills you will build: deploying models to production, preparing data for training 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 model training and evaluation 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
  Manish T. ★★★★★ 3.5/5 Sample review

  The section on working with Python libraries such as NumPy, pandas and scikit-learn 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.

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

  Generative Adversarial Networks covered supervised and unsupervised learning in more depth than I expected. The pace was right for my level. The hands-on notebooks made backpropagation finally make sense to me.

• F
  Farhan Q. ★★★★★ 3.5/5 Sample review

  Well organised. Revisiting the supervised and unsupervised learning part a second time really helped it click. 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.