AI for Data Science & Analytics

What you'll learn

• Building and evaluating ML models
• Preparing data for training
• Tuning model performance
• Deploying models to production
• Understand transformers and large language models
• 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

Apply AI and ML techniques to data science workflows. Master pandas, scikit-learn, feature engineering, and model deployment with MLflow and FastAPI.

What this course covers: AI for Data Science & Analytics works through transformers and large language models, feature engineering and data preparation, deploying and monitoring models and working with Python libraries such as NumPy, pandas and scikit-learn.

Skills you will build: deploying models to production, tuning model performance 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 transformers and large language models 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

• A
  Anjali W. ★★★★★ 4.5/5 Sample review

  Well organised. Revisiting the working with Python libraries such as NumPy, pandas and scikit-learn part a second time really helped it click. Loved that model evaluation got as much attention as model building.

• I
  Ishaan K. ★★★★★ 5.0/5 Sample review

  Clear structure from feature engineering and data preparation through to deploying and monitoring models. I'd like a few more practice tasks, but overall very useful. The deployment section filled a gap most ML courses skip.

• R
  Rahul K. ★★★★★ 4.5/5 Sample review

  Good balance of theory and practice. The transformers and large language models lessons helped me apply deploying models to production in real situations. 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.