Data Insights and Machine Learning Units
Adult Skills Fund course
A technical programme designed for anyone looking to build advanced data capability and unlock deeper insight from their data. You focus on the practical application of data analytics and machine learning, giving you the tools and techniques needed to prepare data, build models and extract meaningful insight.
This course is delivered through hands-on sessions using tools such as Python, Jupyter Notebooks and Power BI, you cover core areas including data preparation, machine learning modelling and natural language processing. You work with real-world datasets to develop skills in cleaning and structuring data, building and evaluating models, and analysing both structured and unstructured data.
The course supports anyone looking to strengthen analytical capability, improve forecasting and make more informed, data-driven decisions. It focuses on developing technical skills that can be applied directly to live projects, helping teams move beyond reporting into advanced analytics and machine learning.
Designed for:
Data professionals, analysts, developers and technical teams with some existing experience, looking to advance their skills in machine learning, data processing and applied analytics.
Data Cleaning and Preparation for Machine Learning
Unlock the power of reliable, high‑quality data with this hands‑on, two‑day course focused on the essential foundations of machine learning: data cleansing and preparation. Designed for analysts, aspiring data scientists, and anyone working with data, you take a practical deep dive into the techniques and tools needed to prepare real‑world datasets for accurate, trustworthy machine learning models.
You work in Power BI or Python (Jupyter Notebooks), combining theory with guided practice to build confidence in modern data‑wrangling skills. Across two immersive days, you learn how to identify and handle missing values, remove duplicates, resolve inconsistencies, detect and treat outliers, transform and encode data, and enhance datasets using enrichment techniques. You explore strategies for managing imbalanced data, including the use of SMOTE for synthetic oversampling.
By the end of the course, you are equipped with the practical knowledge to clean, validate, integrate, and prepare datasets that drive powerful machine learning insights – setting you up for success in any data‑driven environment.
Introduction to Machine Learning
Accelerate your journey into AI with this intensive, hands on introduction to machine learning course. Designed for professionals who want to move beyond dashboards and automation tools, you gain the practical Python skills needed to build, tune, and deploy powerful machine learning models from scratch.
Throughout this expert led training, you unlock the core techniques used by data scientists every day, from regression, classification, clustering, and PCA to model evaluation, feature engineering, and hyperparameter optimisation. You also explore time series forecasting models like ARIMA, giving you the ability to predict trends and make data driven decisions with confidence.
Unlike AutoML tools with limited control or customisation, this course is delivered entirely in Python using Jupyter Notebook or Visual Studio Code, giving you complete freedom to understand, adapt and refine every model you build.
Whether you're levelling up your analytics career or stepping into machine learning for the first time, this course equips you with practical, job ready skills you can apply immediately in real world projects.
Free Text Theme Development
Unlock the power hidden in free text data with this one day deep dive into Natural Language Processing (NLP). Designed for anyone who wants to move beyond structured datasets, this hands on Python only session shows you how to extract themes, patterns, and insights from unstructured text with modern NLP techniques.
Across six hours of focused theory and practical coding, you learn how to clean and prepare raw text, remove noise such as special characters and URLs, tokenise language, and apply stemming or lemmatisation. You explore topic modelling methods including Latent Dirichlet Allocation (LDA) and GSDMM, ideal for analysing longer documents or short form text like surveys, comments and chat logs.
By the end of the course, you know how to transform messy free text into meaningful themes that support better reporting, sentiment understanding, and machine learning workflows.
Perfect for anyone ready to expand their analytical toolkit into the world of NLP.