AI200: Applied Machine Learning (Synchronous E-Learning)
About This Course
This course is designed to train learners who aspire to develop practical, hands-on skills in Machine Learning. Throughout the course, you will develop, evaluate, and refine machine learning models to solve real-world problems and drive smarter decisions.
Through structured in-class exercises and the capstone project, learners will perform end-to-end Machine Learning workflows, which includes data preparation, predictive modelling, model tuning and evaluation, while adopting industry best practices and using state-of-the-art techniques.
Ideal for aspiring data scientists, analysts, and professionals ready to go beyond basic analytics, this course empowers you to turn complex datasets into accurate predictions and actionable outcomes, bridging the gap between theory and application in today’s data-driven economy.
What You'll Learn
- Perform data wrangling to prepare structured datasets for analysis
- Generate descriptive statistics and data visualisations to identify patterns, distributions, and outliers that inform modelling decisions
- Implement machine learning algorithms for classification and regression tasks to solve prediction problems
- Utilise ensemble techniques and feature engineering to enhance model performance
- Evaluate and tune models based on performance metrics
- Apply clustering algorithms to discover hidden patterns in unlabeled data
- Produce suitable user recommendations with Recommender Systems modelling techniques
Learners will be equipped with practical, end-to-end skills in data preparation, statistical modelling, supervised and unsupervised machine learning, and model optimisation, enabling them to build, evaluate, and iterate on intelligent systems that generate data-driven insights and predictions.
Entry Requirements
Learners must have completed our AI100: Python Programming and Data Visualisation course; OR
Learners must possess:
1. English Language Proficiency:
- Able to communicate clearly in spoken and written English
2. Digital Literacy:
- Familiarity with digital tools (access email communications, perform web browsing, etc.)
3. Python Programming Proficiency:
- Familiarity with Python programming syntax and functions