دانلود کتاب تحلیل چند متغیره و تکنیک‌های یادگیری ماشین، تحلیل ویژگی‌ها در علوم داده با استفاده از پایتون

This book offers a comprehensive first-level introduction to data analytics. The book covers multivariate analysis, AI/ML, and other computational techniques for solving data science problems using Python. The topics covered include a working introduction to programming with Python for data analytics, probability and statistics, hypothesis testing, correlation and regression, factor analysis, classification (including logistic regression, linear discriminant analysis, decision tree, and support vector machines), cluster analysis, survival analysis, general computational techniques (market basket analysis, social network analysis, and recommendation systems), machine learning, and deep learning. Many academic textbooks are available for teaching statistical applications using R, SAS, and SPSS. However, there is a need for textbooks that provide a comprehensive introduction to the emerging and powerful Python ecosystem, which is pervasive in data science and machine learning applications. The book offers a judicious mix of theory and practice, reinforced by over 100 tutorials coded in the Python programming language. The tutorials and examples conceptualize real-world problems using data curated from public domain datasets. It is designed to benefit any data science aspirant with a primary (higher secondary school level) understanding of programming and statistics. This book may be a supplementary textbook for first-level courses in applied statistics, multivariate analysis, machine learning, deep learning, data mining, and business analytics. It can also be used as a reference book by data analytics professionals.