An Excursion Into Statistical Learning
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An Excursion into Statistical Learning
Author: Pasquale De Marco
language: en
Publisher: Pasquale De Marco
Release Date: 2025-05-07
Embark on a journey into the realm of statistical learning, where data transforms into knowledge and insights emerge from uncertainty. "An Excursion into Statistical Learning" is a comprehensive guide, meticulously crafted to unveil the power of statistical learning and empower you to harness its potential. Within these pages, you'll delve into the fundamental concepts of probability, the bedrock of statistical analysis. Explore probability axioms, conditional probability, Bayes' theorem, random variables, and probability distributions, gaining a solid foundation for understanding statistical inference. Unravel the intricacies of statistical inference, mastering point estimation, confidence intervals, hypothesis testing, and regression analysis. Discover how statistical models illuminate data, enabling you to draw informed conclusions and make data-driven decisions. Venture into the captivating world of machine learning, where algorithms learn from data, uncovering patterns and making predictions. Delve into supervised learning methods, such as decision trees, support vector machines, and random forests, unlocking their ability to make accurate predictions based on labeled data. Explore unsupervised learning methods, such as k-means clustering, hierarchical clustering, and principal component analysis, unveiling hidden structures and patterns within uncharted data. Recognize the significance of data preparation and exploration, the crucial steps that lay the foundation for successful statistical learning. Immerse yourself in data cleaning and preprocessing techniques, transforming raw data into a suitable format for analysis. Utilize exploratory data analysis methods, such as visualization and summary statistics, to uncover hidden insights and guide the selection of appropriate statistical models. Equip yourself with advanced statistical modeling techniques, venturing beyond the basics. Explore generalized linear models, time series analysis, survival analysis, and mixed-effects models, delving into their applications across diverse domains. Discover Bayesian statistics and graphical models, frameworks that incorporate prior knowledge and model complex dependencies. As you navigate the world of statistical learning, embrace the ethical and responsible use of these powerful techniques. Examine algorithmic bias, data privacy, and the paramount importance of transparency and interpretability in statistical models. Promote diversity and inclusion in the field of statistical learning, advocating for a responsible and ethical approach to data analysis. If you like this book, write a review on google books!
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