Data Analysis
Collect, clean and analyse data with spreadsheets and Python so you can answer business questions.
Machine Learning training
Train and evaluate models that learn from data, using Python and standard machine learning libraries.
Course overview
Learn how to build models that learn from data. Using Python, you will prepare data, train and evaluate common models, and understand when each approach is the right choice.
Requirements
- Basic Python knowledge
- School-level maths
- A computer
What you will learn
- Prepare data for modelling
- Train regression and classification models
- Evaluate and improve model performance
- Explain model results clearly
Practical projects
- A house price predictor
- A customer churn classifier
Skills you will gain
- Python
- scikit-learn
- Data preparation
- Model evaluation
- Feature engineering
Career and application opportunities
- Junior machine learning engineer
- Data scientist (with further study)
- Analyst using predictive models
Curriculum
1Machine learning foundations
- What machine learning is
- Types of machine learning
- The ML workflow
- Setting up Python tools
2Data preparation
- Cleaning data
- Feature engineering
- Splitting train and test sets
- Scaling and encoding
3Supervised learning
- Linear regression
- Logistic regression
- Decision trees and random forests
- K-nearest neighbours
4Evaluation and improvement
- Accuracy, precision and recall
- Overfitting and underfitting
- Cross-validation
- Tuning models
5Beyond the basics
- Clustering
- Intro to neural networks
- Saving and using models
- Capstone project
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