Description
Predictive Analytics Using Machine Learning is designed to turn data into foresight. In this project-driven course, you’ll learn to apply machine learning algorithms to real-world business and scientific problems to forecast outcomes, optimize operations, and make strategic decisions. Starting with the basics of supervised learning, you’ll build predictive models using linear regression, decision trees, random forests, k-nearest neighbors, and gradient boosting. You’ll also learn how to evaluate model performance using cross-validation, precision, recall, and ROC curves. With Python and scikit-learn as your core tools, you’ll explore the entire predictive modeling pipeline—from data preprocessing and feature engineering to model selection and fine-tuning. The course also emphasizes practical implementation, teaching you how to deploy models and interpret the business value behind predictions. Whether you’re aiming to predict customer churn, sales trends, or equipment failure, this course provides a robust foundation in data-driven forecasting. It’s ideal for data analysts, business professionals, and developers looking to move beyond descriptive analytics into the realm of actionable insights.
Rufus –
What stood out to me was how well the course explained model selection and performance metrics like ROC AUC and F1-score. It filled in a lot of knowledge gaps and made me more confident in applying machine learning in my projects.
Ronke –
This course showed me how to turn historical data into actionable insights. Using predictive models to forecast sales trends and campaign performance is now part of my daily workflow.
Azunwena –
Even with my technical background, I appreciated how clearly the course explained concepts like feature engineering and overfitting. The assignments were practical, and I now use what I learned to enhance app functionality with smart predictions.
Itoro –
The balance between theory and hands-on practice was perfect. I appreciated the real-world case studies and the Python walkthroughs. I was able to build my first predictive model for customer churn after just a few modules.
Adeyinka –
This course gave me exactly what I needed to move from descriptive to predictive analytics. The instructor made complex concepts like regression, decision trees, and model evaluation feel easy to grasp.