1. ML workflow and data preparation
Frame prediction problems, clean data and split datasets correctly.
Home/All Courses/Machine Learning with Python
— Craskills flagship course · TECH & CAREER
Machine Learning with Python
Build practical capability from foundations to advanced application in Machine Learning with Python.
CSDesigned by Craskills Learning and Development Solutions
Contextual learningAdapted to participant roles
Hands-on practiceRelevant workplace situations
9 learning modulesClear, practical architecture
Offline / OnlineFlexible delivery options
CustomisableAligned to organisational goals
About this courseBuilt around the moments
Built around the moments
that matter at work.
A progressive, instructor-led pathway in Machine Learning with Python. Start at the level matching your prior experience, practise with guided exercises, and develop a reviewed project. Each level is part of the same course; scope, duration and entry assessment are agreed before enrolment.
Designed for
Python users and learners with introductory statisticsWhat you’ll learnCapabilities you can
Capabilities you can
apply immediately.
01Train regression and classification models with simple feature engineering.
02Apply clustering and dimensionality reduction with cautious interpretation.
03Develop a reproducible model, evaluation report and monitored deployment prototype.
Three levels · One courseBuild from foundations
Build from foundations
to advanced practice.
Choose your entry level after an initial skills discussion. Complete the preceding level or demonstrate equivalent skills before progressing. Advanced here means advanced within this pathway, not a guarantee of professional mastery.
Beginner3 modules
Entry guidance: Review the audience requirements above; this level begins with subject foundations.
2. Statistical foundations
Interpret distributions, uncertainty, correlation and evaluation baselines.
3. Supervised learning
Train regression and classification models with simple feature engineering.
Progress review: Practical exercise, feedback and a demonstration of the level’s learning outcomes. Duration is agreed for the selected level and audience.
Intermediate3 modules
Entry guidance: Beginner-level skills or equivalent practical experience.
1. Model evaluation
Choose suitable metrics, cross-validation and error analysis.
2. Feature engineering and pipelines
Build reproducible preprocessing and training pipelines.
3. Unsupervised learning
Apply clustering and dimensionality reduction with cautious interpretation.
Progress review: Practical exercise, feedback and a demonstration of the level’s learning outcomes. Duration is agreed for the selected level and audience.
Advanced3 modules
Entry guidance: Intermediate-level skills and the ability to complete applied exercises independently.
1. Tuning and responsible modelling
Control leakage, assess fairness and document limitations.
2. Model serving and monitoring
Deploy inference interfaces and monitor drift and performance.
3. ML capstone
Develop a reproducible model, evaluation report and monitored deployment prototype.
Progress review: Practical exercise, feedback and a demonstration of the level’s learning outcomes. Duration is agreed for the selected level and audience.
Learning experienceLess presentation.
Less presentation.
More participation.
Participants work with relevant situations, guided practice, peer discussion, reflection and practical action planning.
Contextual cases
Scenarios and examples connected to participant roles and business reality.
Guided practice
Structured exercises that make new behaviours safer to try and easier to repeat.
Application tools
Frameworks, checklists, templates and action plans designed for use after the course.
Completion credentialCertificate
Certificate
of participation.
Certificate available, subject to the agreed delivery design and participation requirements.
CRASKILLS.Certificate of
Participation
Participation
Delivery optionsRight-sized for your
Right-sized for your
learning objective.
Beginner foundation pathway
Instructor-led • Offline / Online • Customisable
Intermediate applied practitioner pathway
Instructor-led • Offline / Online • Customisable
Advanced project pathway
Instructor-led • Offline / Online • Customisable
Complete three-level learning journey
Instructor-led • Offline / Online • Customisable
Continue building capability
Related courses in this skill area.
AI for Human Resources Professionals
Applied AI across the employee lifecycle.
View course →AI-Enabled Talent Acquisition
Responsible AI for modern recruitment.
View course →AI for Managers and Business Leaders
Practical AI for decisions and productivity.
View course →Frequently asked questionsUseful answers
Useful answers
before you begin.
Is this course suitable for beginners?+
The beginner level introduces this subject. Review the audience requirements: some technical pathways require programming or mathematics. Intermediate and advanced entry requires equivalent skills or completion of the preceding level.
Can this course be customised for our organisation?+
Yes. Examples, activities, terminology, case situations, duration and outcomes can be contextualised for your industry and participant roles.
Can the course be delivered online and offline?+
Available delivery modes for this course are Offline / Online. The final format is agreed around group size, location and learning objectives.
Will participants receive a certificate?+
Certificate available. Any participation or completion requirements are confirmed in the agreed course proposal.
How can we request a proposal?+
Use the enquiry button and share your audience, preferred dates, location, delivery mode and expected outcomes. The Craskills team will recommend an appropriate design.
— Let’s shape the right experience
Bring this course
to your people.
Share your audience, location, preferred duration, current challenges and expected outcomes.
Request a course proposal →