Foundations of Machine Learning

What do you do when your data doesn't have labeled examples? In this module, you'll explore unsupervised learning, where algorithms find structure and insights in data all on their own. You'll master clustering techniques like K-Means and hierarchical clustering to group similar customers, products, or behaviors, and learn how to detect anomalies that could represent fraud or unusual events. By the end of this module, you'll be equipped with powerful tools to uncover hidden insights in your data that supervised methods might miss, expanding your toolkit for real-world data science challenges.

Did you know that data preparation often determines model success more than algorithm selection? In this essential module, you'll learn the critical skills of data preprocessing and feature engineering that separate novice from professional data scientists. We'll guide you through handling missing data, encoding categorical variables, scaling features, and selecting the most important attributes that will make your models shine. By mastering these techniques, you'll dramatically improve your models' accuracy and reliability, ensuring they perform well on real-world messy data that would otherwise cause less-prepared models to fail.

Let's figure out how to properly make forecasts from time-based data! In this module, you'll learn specialized techniques for working with time-dependent data like stock prices, sales forecasts, and sensor readings that traditional ML approaches can't handle effectively. You'll implement practical forecasting models using tools like ARIMA, Exponential Smoothing, and Facebook Prophet, understanding how to identify trends, seasonality, and other temporal patterns. By the end of this module, you'll be able to build accurate forecasting systems that can predict future values based on historical patterns, adding a powerful and in-demand skill to your machine learning toolkit.