Machine Learning A-Z

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 deploying and monitoring models

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 machine learning a-z, covering core concepts, practical applications, and industry best practices.

What this course covers: Machine Learning A-Z works through working with Python libraries such as NumPy, pandas and scikit-learn, deploying and monitoring models, transformers and large language models and deep learning frameworks (TensorFlow / PyTorch).

Skills you will build: preparing data for training, 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. It suits learners who already know the basics of machine learning and AI and want to go deeper.

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 preparing data for training to your own work or projects, and know which areas to practise next.

Requirements

• Basic knowledge of machine learning and AI
• A computer with internet access
• Willingness to complete practice tasks

Your instructor

• A
  Aditya G. ★★★★★ 5.0/5 Sample review

  Good balance of theory and practice. The transformers and large language models lessons helped me apply tuning model performance in real situations. The deployment section filled a gap most ML courses skip.

• N
  Neha A. ★★★★★ 5.0/5 Sample review

  Well organised. Revisiting the transformers and large language models part a second time really helped it click. The hands-on notebooks made backpropagation finally make sense to me.

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

  Clear structure from working with Python libraries such as NumPy, pandas and scikit-learn through to deploying and monitoring models. I'd like a few more practice tasks, but overall very useful. 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.