About This Learning Path

This learning path brings you from Python foundations to deploying reliable machine learning models that solve real business problems. You’ll start by getting comfortable with Python, Jupyter, and core libraries such as NumPy, Pandas, and Matplotlib before moving into data preparation, joins, aggregation, and cleaning techniques that make raw data analysis‑ready. From there, you’ll progress through supervised and unsupervised learning, including linear regression, decision trees, k‑means clustering, and feature engineering methods like scaling, selection, and dimensionality reduction. You’ll then extend your toolkit with advanced topics such as logistic regression, Naive Bayes, support vector machines, and modern clustering approaches. Throughout, Kubicle’s applied learning model keeps the focus on practice: you’ll automate data collection from live sources and complete industry‑style projects—pricing strategy, loan default risk, and operational forecasting—that demonstrate measurable value to stakeholders. By the end, you’ll be able to structure machine learning workflows, evaluate model performance, and translate insights into action for finance, consulting, and operations teams.