Professional Certificate Programme in Generative AI and Machine Learning

Cluster customer data to identify distinct segments using unsupervised learning techniques. 

Master clustering algorithms (K-Means, Hierarchical), analyse clusters, and drive business decisions with data insights. 

 Skills: Clustering, unsupervised learning, customer segmentation, and data preprocessing. 

Classify different vehicle types (e.g., bus, saab, opel, van) using a neural network.  

Learn to preprocess vehicle image data, build and train neural networks for classification, and evaluate model performance for actionable insights.   

Skills: Image preprocessing, neural network design, model training, data visualization, and performance analysis. 

Analysing retail banking data to understand customer behaviours and predict churn.

Analyse customer behaviours to predict churn, build and enhance predictive models, and provide data-driven retention strategies using retail banking data.   

Skills: Data preprocessing, statistical analysis, machine learning, visualisation, and feature engineering. 

Develop a predictive model to estimate house prices based on multiple features using regression techniques. 

Master regression algorithms to build, evaluate, and interpret predictive models.   

Skills: Regression modelling, evaluation metrics (MSE, R-squared), feature engineering, and data preprocessing.

Analysing employee data to predict attrition (exit status).

Analyse factors driving employee attrition, forecast risks with predictive models, and deliver actionable insights for strategic workforce management.   

Skills: Data preprocessing, statistical analysis, machine learning, visualisation, and feature engineering 

Build Python scripts to automate data processing tasks and perform basic computations.

Learn Python syntax, build scripts, use control structures, and process data with Python libraries.   

Skills: Python programming, script development, debugging, and data handling. 

Build a predictive model that can identify factors that lead to a "satisfied" or "neutral or dissatisfied" outcome. 

Analyse factors impacting satisfaction, classify customer responses, and develop strategies to enhance the customer experience using predictive models.   

Skills: Data preparation, statistical analysis, machine learning, feature engineering, and visualisation. 

Perform data cleaning, feature engineering, and exploratory data analysis (EDA) to derive meaningful insights from the data and predict price 

Learn data cleaning, feature engineering, and EDA to build predictive models for price estimation and deliver actionable insights.   

Skills: Data preprocessing, feature transformation, statistical analysis, EDA, and regression model development. 

Build and evaluate a deep learning model using fundamental architectures, such as feedforward neural networks. Apply various evaluation metrics to assess the model's performance on a given dataset. 

Master deep learning concepts, evaluate model performance, compare architectures, and optimize for superior results.   

Skills: Deep learning, neural network training, evaluation metrics, model tuning, and overfitting analysis.