This course offers a deep dive into the world of statistical analysis, equipping learners with cutting-edge techniques to understand and interpret data effectively. We explore a range of methodologies, from regression and classification to advanced approaches like kernel methods and support vector machines, all designed to enhance your data analysis skills. Our journey is guided by the well-known textbook "The Elements of Statistical Learning" by T. Hastie, R. Tibshirani, and J. Friedman. This course provides examples written in Python. Your system should have Python 3.8 or higher, as well as essential libraries such as NumPy, pandas, matplotlib, seaborn, scikit-learn, SciPy, and PyTorch. These tools not only support the learning process but also prepare you for real-world data analysis challenges. Whether you're aiming to refine your expertise or just starting out in the field of data science, this course provides the knowledge and tools to transform your understanding and application of statistical learning. It's a perfect blend of theory and practice, ideal for anyone looking to enhance their skills in data interpretation and analysis.
Instructor Welcome
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Skills You'll Learn
Bootstrapping, Artificial Neural Network, K-Means Clustering, Ridge/LASSO Regressions, K-Nearest Neighbors Algorithm (K-NN), Kernel Method, Linear Regression, Linear Discriminant Analysis (LDA), Splines, Logistic Regression, Maximum Likelihood Estimation, Bayesian Inference
From the lesson
Module 1: Statistical Learning - Terminology and Ideas
Taught By
Shahrzad Jamshidi