Learn Machine Learning in Python

A hands‑on Machine Learning subject for analysts and consultants who already know the basics of Python. Nine courses and projects covering regression, decision trees, k‑means clustering, feature engineering, advanced regression, classification and clustering, with two workplace projects on car pricing and loan default risk.

Curriculum

9 courses & projects
Total Learning
~19 hours
Level
Intermediate to Advanced
Credits
16 CPE/CPD

1M+ learners trained 4.7/5 average G2 rating 80% course completion rate

Why Machine Learning in Python?

Predictive analytics finance and consulting teams can actually ship.

Python is the language of modern machine learning, and finance and consulting teams are increasingly expected to work with the models, not just the outputs. This subject takes analysts who already know the basics of Python from regression through to advanced classification and clustering, with two workplace projects to lock the skill in.

Regression to advanced classification

Build a first ML algorithm in week one. By the end, deploy advanced classification with Naïve Bayes and Support Vector Machines, plus advanced clustering for fraud detection and customer segmentation.

Two workplace projects, not just lectures

A pricing strategy project for a Japanese carmaker re‑entering the US market, and a credit‑risk project for a bank identifying likely loan defaulters. Skills practiced on real business decisions.

Feature engineering, in detail

A dedicated course on the work that makes models actually useful. Feature scaling, feature selection and dimensionality reduction, taught with applied datasets.

Credentials that stand up

Every milestone is independently accredited by CPD, CPE and NASBA, so what learners earn here counts towards continuing professional development.

Learning Outcomes

What learners will be able to do by the end of the program.

Every outcome is mapped to a specific lesson and assessed through scenario‑based exercises. Learners walk away with practical ML skills they can apply on the next forecasting, segmentation or risk modelling task.

Build and evaluate regression models

Linear regression to predict numeric values, then advanced techniques (decision tree, KNN, support vector and logistic regression) for the cases linear can't handle.

Build classification models, basic to advanced

Decision trees as the easy‑to‑visualise starting point, then advanced classification with Naïve Bayes and Support Vector Machines for fraud detection and credit risk.

Cluster and segment data with confidence

K‑means clustering to start, then advanced clustering (single linkage and soft clustering) for fraud detection and segmenting customers in messy real datasets.

Engineer features that improve every model

Feature scaling for uniform inputs, feature selection for predictive power, dimensionality reduction for trimming noise. The work that turns a fragile model into a useful one.

Apply ML to a real pricing decision

A workplace project for a Japanese carmaker re‑entering the US market. Use ML to identify which factors should drive pricing so the team avoids the previous over‑pricing miss.

Apply ML to credit risk modelling

A workplace project for a bank. Use several classification algorithms to separate likely defaulters from customers who'll continue paying back their loans, with the explanation a credit committee actually needs.

The Curriculum

Nine courses and projects, in the order learners take them.

Machine Learning in Python is a single applied track. Nine items running from regression through advanced classification and clustering, with two workplace projects on car pricing and loan default risk. Roughly 19 hours end to end.

ML in Python · 1 of 9 Advanced

Regression Analysis in Python

Build a first machine learning algorithm in Python. Use linear regression to predict future numeric values from existing data, applied to a real business scenario.

3 hours 2.5 CPE/CPD

ML in Python · 2 of 9 Advanced

Decision Trees in Python

Classification algorithms predict outcomes with a few possible variations. Decision Trees are easy to understand and to visualise, the natural starting point for classification.

2.5 hours 2.0 CPE/CPD

ML in Python · 3 of 9 Advanced

K‑Means Clustering in Python

Deploy the k‑means clustering algorithm. Segment customers into separate groups so the business can tailor responses to each one.

1.5 hours 1.0 CPE/CPD

ML in Python · 4 of 9 Advanced

Feature Engineering

The work that makes models actually useful. Feature scaling for uniform inputs, feature selection for the most predictive power, and dimensionality reduction for trimming noise.

2.5 hours 2.0 CPE/CPD

ML in Python · 5 of 9 Advanced

Advanced Regression

Beyond linear. Nonparametric methods including decision tree regression, k‑nearest neighbours regression, and support vector regression, finishing with logistic regression.

1.5 hours 1.5 CPE/CPD

ML in Python · 6 of 9 Advanced Project

Improve a car company's pricing strategy

A workplace project. Help a Japanese carmaker re‑enter the US market. Their previous attempt failed on price, and ML can identify which factors should drive the new strategy.

1.5 hours 1.5 CPE/CPD

ML in Python · 7 of 9 Intermediate

Advanced Classification

Detect transaction anomalies and assess loan default risk. Two new algorithms (Naïve Bayes and Support Vector Machines) extend the classification toolkit beyond decision trees.

3 hours 2.5 CPE/CPD

ML in Python · 8 of 9 Intermediate

Advanced Clustering

Single Linkage Clustering and Soft Clustering go beyond k‑means. Identify fraud, segment customers more precisely, and handle the messy real datasets simple methods can't.

2 hours 1.5 CPE/CPD

ML in Python · 9 of 9 Advanced Project

Identify risk of default with predictive analytics

A workplace project. Help a bank separate likely loan defaulters from customers who'll keep paying. Use several classification algorithms and arrive at a model the credit committee can defend.

1.5 hours 1.5 CPE/CPD

Conclusion

Machine Learning in Python builds directly on Python Fundamentals. Both sit inside Kubicle's wider library, alongside subjects on AI Fundamentals, Excel, Power BI, SQL, Alteryx and financial modelling.