Machine Learning in Python
About This Course
Four days that take you from the Python language itself to predictive models you can defend. The course is built for people who need the whole path rather than a slice of it: the programming, the data handling, the numerical work, and then the machine learning that depends on all three.
The first half is about getting data under control. You learn Python through worked examples rather than syntax tables, then spend a day pulling data out of the spreadsheets, databases and APIs where it actually lives, reshaping it and charting it.
The second half turns that into modelling. Day 3 covers the numerical groundwork with NumPy, along with the clustering and outlier detection that usually come first when data arrives without labels. Day 4 is scikit-learn end to end, and gives real time to validation and feature engineering, because those separate a model that works from one that only looked good on the training set.
Who This Course Is For
Analysts, engineers, scientists and researchers who need to build predictive models but do not yet write Python. It suits people who currently do this work in spreadsheets, in another language, or with a tool that has run out of room, and who want a single course covering the language, the data handling and the modelling.
If you already write Python and use Pandas or Polars, Python for Machine Learning covers the modelling alone in two days.
Prerequisites
Some familiarity with programming concepts in any language is assumed. You do not need to have written Python before, and no prior machine learning experience is expected.
No mathematics beyond what your own work already requires.
What You'll Learn
- Write Python to load, clean and reshape the data your work depends on.
- Move data between the formats and systems your organisation already uses, including spreadsheets, databases and web APIs.
- Build predictive models with scikit-learn, covering regression, classification and clustering.
- Validate models honestly and read the diagnostics that show where one is failing.
- Engineer features from messy data, and find the outliers that distort results.
- Deploy a trained model and monitor it for degradation in production.
Course Syllabus
Day 1: Python basics
- The core language, taught through worked examples
- Working effectively in Jupyter
Day 2: Handling, analysing and presenting data
- Tabular data analysis with Pandas or Polars, and the common data formats
- Retrieving data from databases and web APIs
- Interactive visualisation
Day 3: Further data analytics
- Array and matrix computing with NumPy
- Simulation and regression
- Clustering, and finding the unusual points in your data
Day 4: Machine learning
- Regression, classification and time-series forecasting
- Building and validating models with scikit-learn
- Feature engineering and selection
- Putting models into production