14
Live Sessions
90 mins
Session Length
Foundation
Level
Professionals
Suitable For
Live Online
Delivery
UK Time
Time Zone
LEARNING OBJECTIVE
By the end of this course, you will be able to
Build and train a classification or regression model using scikit-learn, then evaluate it honestly rather than trusting the first accuracy score.
Configure a data preprocessing and feature engineering pipeline that prepares a real dataset for modelling, not a toy one.
Justify why supervised, unsupervised or reinforcement learning fits a given problem, and defend that choice against the alternatives.
Automate model evaluation using cross-validation so your results hold up beyond a single lucky train-test split.
Present your finished model and its evaluation metrics in a portfolio piece that a data science hiring manager can actually assess.
TARGET LEARNER
Who this course is for
For professionals ready to stop watching AI reshape every industry and start building real machine learning capability.
Career starters and professionals targeting data science or machine learning roles who want practical model-building experience on real datasets not theoretical lectures.
Python developers who want to add supervised and unsupervised ML to their skillset and open roles they cannot currently access with development skills alone.
Professionals in data-adjacent roles who want to move beyond general awareness of AI and develop the applied capability that justifies a salary increase or role change.
If you see yourself in one of these, this course is built for you.
Prerequisites
What you need
No prior machine learning or AI experience is required.
Basic Python knowledge — variables, functions, and loops — is needed before joining.
A laptop with Python and Jupyter Notebook installed is required.
Google Colab can be used as an alternative and no specialist hardware is needed.
A willingness to work through real datasets and build models during sessions is expected.
COURSE OVERVIEW
What this course is about
AI and Machine Learning Fundamentals is a structured, instructor-led programme for learners who want practical machine learning capability, not just AI theory. The course covers supervised and unsupervised learning, model evaluation, and Python for data science in depth, combining conceptual grounding with a complete end-to-end project throughout every session.
The programme runs across 14 one-and-a-half-hour live sessions over 7 weeks, progressing through an introduction to machine learning, and Python for data science before finishing with data preparation, supervised and unsupervised learning, model evaluation, and a final ML project. Every topic is applied to a real dataset, not a toy example.
Basic Python knowledge is assumed throughout the course, with no prior machine learning experience required. A model is trained and evaluated from session one so learners are working with real scikit-learn code throughout, not just following diagrams.
What the course covers:
Machine Learning Overview: key concepts and where each approach applies.
Python for Data Science: NumPy, pandas, and matplotlib.
Data Preparation: cleaning and exploratory data analysis.
Modelling: supervised and unsupervised learning, and honest model evaluation.
Final Project: a complete, evaluated machine learning model.
CIRRICULUM
7 Modules. 14 live Sessions. Hundreds of small wins.
The curriculum starts with an overview of machine learning and Python for data science, then moves through data preparation before finishing with supervised and unsupervised learning, model evaluation, and a final ML project.
01
Introduction to Machine Learning
Learn the machine learning landscape supervised, unsupervised, and reinforcement learning the types of problems each solves, and apply that framework to classify a set of real business problems by ML approach.
02
Python for Data Science
Learn data manipulation and visualisation with pandas, NumPy, and matplotlib and apply them to explore and summarise a real dataset you’ll use as the foundation for every model you build in the course.
03
Data Preparation and EDA
Learn data preparation and exploratory data analysis cleaning, encoding categorical variables, feature scaling, and identifying patterns and apply them to prepare your real dataset for modelling.
04
Supervised Learning
Learn supervised learning algorithms classification and regression with scikit-learn and apply them to train your first classification and regression models on real datasets with real predictions.
05
Model Evaluation
Learn model evaluation metrics accuracy, precision, recall, F1, confusion matrix, and cross-validation and apply them to evaluate and compare your trained models on real held-out test data.
06
Unsupervised Learning
Learn unsupervised learning K-means clustering, hierarchical clustering, and PCA for dimensionality reduction and apply them to find structure in unlabelled data from a real business dataset.
07
Final ML Project
Learn how to structure and deliver a complete end-to-end ML project from raw data to evaluated model and apply all course concepts to build a portfolio project you’ll present to the cohort.
Course Types
We offer three structured learning paths based on your goals:
Crash Course (Fast-Track)
Quick, intensive courses designed to teach specific skills efficiently. Ideal for those upskilling fast or preparing for certifications.
DeepDive Program (Full Mastery)
Comprehensive, step-by-step learning for full mastery. For beginners and professionals seeking long-term, deep expertise.
MentorConnect (One-on-One)
Personalised mentorship with real-world guidance. Best for those who thrive with direct, expert-to-learner coaching.
Learning Approaches
We recognize that everyone learns differently, so we offer flexible learning formats to fit your needs:
One-on-One Training
Personalized, instructor-led coaching tailored to your learning speed.
Best for career-specific coaching or specialized training needs.
Small Batch Classes (2-5 learners)
Interactive, discussion-based learning in small groups.
Encourages collaboration, teamwork, and peer-to-peer engagement.
FAQ
1
Who is this course designed for?
This course is designed for working professionals who want to add machine learning to their technical skillset, developers building AI-adjacent products, and anyone who wants to move from AI curiosity to practical capability with Python and scikit-learn.
2
What prior experience do I need?
Basic Python knowledge variables, functions, and loops. No prior machine learning experience is required.
3
What will I be able to do by the end?
You will be able to prepare and clean datasets with pandas, train classification and regression models with scikit-learn, evaluate models using precision, recall, F1, and confusion matrices, and build a complete end-to-end ML project for professional presentation.
4
How are the sessions structured?
Seven modules, 1.5 hours per session, 14 sessions over seven weeks. Real datasets are used throughout every session.
5
What is EngagePro?
Algorithm summaries, Python code examples, and visualisation notebooks per module. Tasks involve applying each algorithm to a real-world dataset. Dr Amara Osei reviews every submission with a model performance assessment and data quality notes.
6
What tools do I need?
A laptop with Python and Jupyter Notebook installed. scikit-learn, pandas, and matplotlib are all free.
7
Will I receive a certificate?
Yes the ITLearnner AI and ML Fundamentals Certificate on completing the course and project portfolio.
8
Will I need a powerful computer or GPU to run machine learning models?
No. The course is designed to run comfortably on standard laptops using cloud-based tools and lightweight datasets. Where GPU-heavy operations are demonstrated, ITLearnner uses hosted environments such as Google Colab so you can follow along without specialist hardware.
9
Which Python libraries will I learn during this course?
The course covers NumPy and Pandas for data handling, Matplotlib and Seaborn for visualisation, and scikit-learn for building and evaluating machine learning models. These are the core libraries used by data scientists and ML engineers across industry, making the skills directly transferable to real projects.
10
Is this course a good preparation for more advanced AI courses or certifications?
Yes. This course is designed as a solid foundation that prepares you for courses covering deep learning, natural language processing, and AI engineering. It also provides good grounding for cloud AI certifications from AWS, Google, and Microsoft, which typically expect familiarity with core ML concepts.
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