--- url: "https://xcademia.com/courses/machine-learning-with-python-aligned-to-industry-ml-foundations" title: Machine Learning with Python (Aligned to Industry ML Foundations) description: "Learn practical machine learning with Python in 4 days. Data prep, feature engineering, model training, evaluation, and a capstone with mentor-led labs." publishedAt: "2026-02-12T21:59:23.806558+00:00" updatedAt: "2026-03-30T22:50:53.7265+00:00" type: course code: "AID-0003" level: Practitioner duration_days: "4" track: "Machine Learning & Deep Learning" category: "AI, Data & Analytics" credential_tier: tier1 price_gbp: "2199" --- # Machine Learning with Python (Aligned to Industry ML Foundations) > Learn practical machine learning with Python, covering data preparation, feature engineering, model training, and evaluation. Build a repeatable ML workflow using practical scenarios and mentor-led labs with clear, evidence-based results. ## Overview Machine Learning with Python is a practical programme for learners who want to build usable ML skills, not just follow notebooks. You will learn how ML work is delivered in real teams: framing the problem, preparing data properly, creating a baseline, and improving results through structured iteration. Delivered in a mentor-led format, the course uses practical scenarios such as churn prediction, risk scoring, demand forecasting, and customer segmentation. You will practise feature engineering habits, avoid common pitfalls like leakage, select meaningful metrics, and build models that can be explained and trusted. By the end of the programme, you will be able to deliver an end-to-end ML workflow in Python, communicate results clearly, and produce handover-ready artefacts that support deployment or further engineering. This course is ideal for learners building confidence before moving into professional AI engineering or data science pathways. ## Prerequisites - Basic Python familiarity (notebooks, functions). - Comfort working with spreadsheets or CSVs. - Helpful: basic statistics understanding. ## What you will learn - Design ML solutions aligned to objectives. - Analyse datasets for quality and leakage. - Implement feature engineering and pipelines. - Evaluate models using meaningful metrics. - Communicate results with clear evidence. - Lead baseline delivery with iteration planning. ## Skills you will gain - ML problem framing and baselines - Data cleaning and preparation steps - Train, validation, test split setup - Feature encoding and scaling basics - Classification training and threshold tuning - Regression training and error checks - Clustering and segmentation fundamentals - Metric selection and interpretation - Leakage prevention and workflow hygiene - Overfitting control and validation - Error analysis and improvement planning - Handover artefacts and documentation ## Career progression - Data Analyst - ML Analyst - Junior Data Scientist - Junior ML Engineer ## Curriculum 1. **Module 1: Getting Ready** - Environment setup and workflow expectations - Dataset handling and safe working practices - How to structure labs and capstone work 2. **Module 2: ML Framing and Baseline Thinking** - Turning goals into ML problem types - Defining inputs, outputs, and constraints - Establishing a baseline and success metrics 3. **Module 3: Data Preparation That Holds Up** - Missing data, outliers, and data types - Train/validation/test splitting strategies - Preventing leakage through clean workflows 4. **Module 4: Feature Engineering Foundations** - Encoding categories and scaling features - Creating signals from time and behaviour - Pipelines to keep steps consistent 5. **Module 5: Classification Models and Decisions** - Logistic regression and tree-based baselines - Precision, recall, F1, ROC-AUC selection - Threshold tuning and confusion matrix logic 6. **Module 6: Regression Models and Forecasting Basics** - Linear models and ensemble regressors - MAE, RMSE, R² interpretation - Residual checks and improvement tactics 7. **Module 7: Unsupervised Learning and Segmentation** - Clustering concepts and practical use cases - Scaling impact and distance intuition - Explaining segments and validating usefulness 8. **Module 8: Evaluation, Overfitting, and Error Analysis** - Cross-validation where it helps - Overfitting control and generalisation thinking - Error analysis to prioritise improvements 9. **Module 9: Capstone Delivery and Handover Pack** - Build an ML workflow end to end - Present results with metrics and narrative - Create a handover-ready summary and next steps ## Exam & certification This is an industry-aligned skills programme. There is no external exam. Learners receive an Xcademia Certificate of Completion upon meeting participation and completion requirements. ## Delivery options - **Live Online** — Join live instructor-led sessions from anywhere. Interactive, engaging, and flexible. - **Onsite Training** — We come to you. Training delivered at your workplace for teams of 6 or more. - **Venue-Based** — Classroom training at a professional venue. Ideal for focused, immersive learning. - **Blended** — Combine online and in-person learning for maximum flexibility and impact. ## Frequently asked questions **Is this aligned with a certification?** No. This programme is aligned to industry ML foundations rather than a specific vendor exam. **Who is this course for?** Analysts, aspiring data scientists, developers, and professionals moving into applied ML work. **What experience do I need?** Basic Python and comfort with data tables is recommended. **Is it hands-on or theory-heavy?** Hands-on and mentor-led, using practical scenarios and real datasets. **Can this be delivered to teams?** Yes. We can tailor datasets and scenarios to your business context and reporting needs. ## Course at a glance | Field | Value | | --- | --- | | Code | AID-0003 | | Duration | 4 days | | Level | Practitioner | | Track | Machine Learning & Deep Learning | | Category | AI, Data & Analytics | | Credential tier | tier1 | | Price (GBP) | £2199 | --- ## About this content This Markdown course profile is the citation-grade twin of [Machine Learning with Python (Aligned to Industry ML Foundations)](https://xcademia.com/courses/machine-learning-with-python-aligned-to-industry-ml-foundations). It is published by **Xcademia** (UK Companies House 12322710) and is available for AI search engines and large language models to index, summarise, and cite. When citing or quoting, please attribute *Xcademia* and link back to the source URL above. - Source: https://xcademia.com/courses/machine-learning-with-python-aligned-to-industry-ml-foundations - Publisher: Xcademia — https://xcademia.com - Catalogue index: https://xcademia.com/llms-full.txt