Python and Machine Learning Foundations

About Course

Learn Python programming, data preparation and introductory machine learning through runnable synthetic examples. Progress from variables and functions to pandas, visualisation, regression, classification, pipelines, evaluation and model documentation. Includes 36 detailed lessons, private coding practice with worked feedback and a 72-question final quiz.

LO01: Use a reproducible Python environment and interpret basic expressions.
LO02: Represent records and apply validated branching rules.
LO03: Write reusable functions with clear inputs, outputs and boundaries.
LO04: Handle expected errors and process CSV and JSON safely.
LO05: Use NumPy and descriptive statistics with explicit assumptions.
LO06: Prepare traceable pandas analyses with correct denominators.
LO07: Check analytical data and distinguish features from outcomes.
LO08: Evaluate regression against a meaningful baseline.
LO09: Build consistent training-to-prediction pipelines without leakage.
LO10: Select model settings using development data and relevant metrics.
LO11: Interpret model behaviour without overstating generalisation or causality.
LO12: Produce a reproducible educational model with clear limits and monitoring.

Course Content

Python setup and basic values
Run a script and work with numbers, booleans and strings.

01. Set up Python and run a reproducible first program
02. Use numbers, booleans and explicit units
03. Work with strings and readable output

Collections and decisions
Use lists, dictionaries, sets and explicit conditions.

Loops and functions
Iterate, stop repeated work and encapsulate calculations.

Errors, files and JSON
Validate values and read or write structured data.

Packages, arrays and statistics
Organise dependencies and analyse numeric arrays.

DataFrames and aggregation
Inspect, clean, filter and summarise tabular data.

Joins, charts and problem framing
Combine tables, visualise comparisons and define a prediction task.

Splits, baselines and regression
Reserve evaluation data, fit a simple model and calculate errors.

Pipelines and classification
Preprocess mixed features and fit regression and classification models.

Classification evaluation and tuning
Interpret confusion metrics, thresholds and cross-validation.

Trees, ensembles and clustering
Explore model complexity, importance and unsupervised groups.

Responsible and reproducible practice
Prevent leakage, persist trusted models and document a mini-project.

Assessment

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About Course

What Will You Learn?

Course Content

Python setup and basic values Run a script and work with numbers, booleans and strings.

01. Set up Python and run a reproducible first program

02. Use numbers, booleans and explicit units

03. Work with strings and readable output

Collections and decisions Use lists, dictionaries, sets and explicit conditions.

04. Use lists, tuples and copies

05. Use dictionaries and sets for records and uniqueness

06. Write conditions and boundary checks

Loops and functions Iterate, stop repeated work and encapsulate calculations.

07. Iterate with for loops and aggregations

08. Use while loops with a finite stop condition

09. Write functions with clear inputs and outputs

Errors, files and JSON Validate values and read or write structured data.

10. Handle errors and validate external values

11. Read and write CSV files with clear paths

12. Parse JSON and inspect API-shaped data

Packages, arrays and statistics Organise dependencies and analyse numeric arrays.

13. Organise modules, packages and dependencies

14. Use NumPy arrays, shapes and masks

15. Interpret descriptive statistics and outliers

DataFrames and aggregation Inspect, clean, filter and summarise tabular data.

16. Load and inspect a pandas DataFrame

17. Clean data with a documented rule

18. Filter, group and aggregate business data

Joins, charts and problem framing Combine tables, visualise comparisons and define a prediction task.

19. Join tables and check cardinality

20. Create a chart that supports a clear question

21. Define a machine-learning problem and synthetic dataset

Splits, baselines and regression Reserve evaluation data, fit a simple model and calculate errors.

22. Split data and establish a baseline

23. Fit and interpret a simple linear regression

24. Evaluate regression errors with MAE and RMSE

Pipelines and classification Preprocess mixed features and fit regression and classification models.

25. Preprocess numeric and categorical features in a pipeline

26. Train a multiple-feature regression pipeline

27. Build a binary classification model

Classification evaluation and tuning Interpret confusion metrics, thresholds and cross-validation.

28. Read a confusion matrix, precision, recall and F1

29. Handle imbalance and decision thresholds

30. Use cross-validation and controlled tuning

Trees, ensembles and clustering Explore model complexity, importance and unsupervised groups.

31. Recognise decision-tree overfitting

32. Use ensembles and inspect permutation importance

33. Explore clustering without known target labels

Responsible and reproducible practice Prevent leakage, persist trusted models and document a mini-project.

34. Prevent leakage and assess responsible use

35. Save a trusted model with its metadata

36. Complete a reproducible mini-project and model card