Data scientists and data analysts often engage in prediction and machine learning as part of their routine tasks. In this course, you will dive into the fundamental aspects of constructing and implementing prediction functions, focusing on real-world applications. The course will establish a solid foundation by exploring essential concepts like training and test sets, overfitting, and error rates.
Furthermore, a diverse array of model-based and algorithmic machine-learning techniques will be introduced, encompassing regression, classification trees, Naive Bayes, and random forests. A comprehensive understanding of the entire process of constructing prediction functions will be gained, including data collection, feature generation, algorithm selection, and evaluation.
🆓 Free to Audit
🕒 Approx. 8 Hours
🧾 Paid Certificate Available Upon Completion
🎓 Offered by John Hopkins University via Coursera
