Principles of Machine Learning

Catalog description

With the exponential growth of both available data and computing power, Machine Learning becomes increasingly important and essential knowledge. This course introduces the concept of Machine Learning, commonly used Machine Learning algorithms, and the available tools using Python libraries such as NumPy, SciPy (Scikit-learn), and Panda. Different types of learning algorithms including supervised learning, unsupervised learning, and reinforcement learning are discussed. Some common Machine Learning algorithms are examined in applications with example problems – training the data, finding a model, and making predictions. Practices are done in Python coding. Other topics covered are data visualization, training/testing data and making predictions from the model, model evaluation and parameter tuning.