Learn machine learning origins, principles, and practical applications, as well as implementation via the Python programming language. Students will learn to train a model, evaluate its performance, and improve its performance.
Collect, explore, visualize, and prepare data for machine learning problems using Python
Understand how machine learning algorithms make predictions
Identify appropriate machine learning algorithms for your project
Train, evaluate, monitor, and improve machine learning models
Implement machine learning solutions
About this course:
This course introduces machine learning using Python. Students will learn structured and unstructured data processing, linear regression modeling and non-linear modeling methods used in machine learning algorithm development, optimization techniques, neural networks, and deep learning. This field is made possible due to the rapid and simultaneous evolution of available data, statistical methods, and computing power. Students learn the origins and practical applications of machine learning, how knowledge is defined and represented by computers, and the basic concepts that differentiate machine learning approaches. Machine learning algorithms can be divided into two main groups: supervised learners who are used to construct predictive models and unsupervised learners who are used to build descriptive models. Students learn the classification, numeric predictor, pattern detection, and clustering algorithms. Students learn to train a model, evaluate its performance, and improve its performance. Algorithm uses are illustrated with real-world cases, such as breast cancer diagnosis, spam filtering, identifying bank loan risk, predicting medical expenses, estimating wine quality, identifying groceries frequently purchased together, and finding teen market segments.
COM SCI X 450.1 Introduction to Data Science or consent of instructor.