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πŸ“Š Data Science & Machine Learning

Move from data analysis to predictive modelling, ML pipelines and model deployment.

⏱ 14 weeks Live Online β€’ Small batch

Overview

Working professionals with data or analytics backgrounds use this course to move into data-science and ML-engineering roles. The focus is on solving business problems with models that can be deployed and maintained.

What you will learn

  • Exploratory analysis and feature engineering
  • Supervised and unsupervised learning
  • Model selection, tuning and validation
  • ML pipelines and experiment tracking
  • Deploying models as APIs
  • Monitoring and retraining in production

βœ“ Job-Ready Track

This course is part of our job-ready track. It opens roles such as Data Scientist, ML Engineer, Applied Scientist. Placement assistance and weekly interview preparation are included.

Outcomes depend on your effort, background and market conditions. We do not guarantee a job.

Tools & technologies

🐍PythonπŸ“ŠPandasπŸ“ˆScikit-learnπŸ”₯TensorFlow🧰MLflow⚑FastAPI🐳Docker☁️AWS

Syllabus

  • Business problem translation
  • Data cleaning and validation
  • EDA and visualisation
  • Feature engineering basics
  • Regression and classification
  • Model evaluation metrics
  • Cross-validation
  • Hyperparameter tuning
  • Clustering and dimensionality reduction
  • Tree-based models
  • Ensemble methods
  • Model interpretability
  • Pipelines with scikit-learn
  • Experiment tracking
  • Model packaging
  • API deployment
  • Neural networks with Keras
  • CNNs and transfer learning
  • NLP basics
  • When to use deep learning
  • Monitoring and drift detection
  • Retraining strategies
  • Capstone project
  • Interview preparation
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Have questions about this programme?

Book a free introductory call. We will discuss your background, the syllabus and whether this is the right starting point.

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