Applied Machine Learning with Python gives you hands-on experience in building predictive models using real-world datasets. You’ll learn how to apply linear regression, logistic regression, support vector machines (SVMs), and cross-validation techniques to solve complex data challenges confidently. Guided step by step through Python’s Scikit-Learn library, you’ll explore dataset preparation, feature engineering, model evaluation, and hyperparameter tuning.

By the end of this course, you’ll be able to select and train the right model for regression and classification problems, interpret learning curves, assess performance metrics such as F1 and AUC, and improve model accuracy through regularisation and tuning. Each concept is reinforced with practical projects — from predicting housing prices to classifying Titanic survivors — ensuring you understand both the “how” and “why” behind each algorithm.

Whether you’re preparing for a data science role, enhancing your analytics skills, or applying machine learning in business, this course provides both foundational understanding and applied technical depth. You’ll also gain the confidence to analyse model behaviour, visualise performance using Python, and communicate results effectively to stakeholders.

Upon completion, learners will receive a free course completion certificate to recognise their achievement. For those seeking to enhance their professional portfolio, premium printed certificates and academic transcripts are also available for purchase. Students benefit from 24/7 email support provided by our 5-star rated instructor assistance team, ensuring continuous guidance throughout their learning journey.