Core Concepts In Statistical Learning
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Core Concepts in Statistical Learning
"Core Concepts in Statistical Learning" serves as a comprehensive introduction to fundamental techniques and concepts in statistical learning, tailored specifically for undergraduates in the United States. This book covers a broad range of topics essential for students looking to understand the intersection of statistics, data science, and machine learning. The book explores major topics, including supervised and unsupervised learning, model selection, and the latest algorithms in predictive analytics. Each chapter delves into methods like decision trees, neural networks, and support vector machines, ensuring readers grasp theoretical concepts and apply them to practical data analysis problems. Designed to be student-friendly, the text incorporates numerous examples, graphical illustrations, and real-world data sets to facilitate a deeper understanding of the material. Structured to support both classroom learning and self-study, it is a versatile resource for students across disciplines such as economics, biology, engineering, and more. Whether you're an aspiring data scientist or looking to enhance your analytical skills, "Core Concepts in Statistical Learning" provides the tools needed to navigate the complex landscape of modern data analysis and predictive modeling.
Mastering Machine Learning: Essential Concepts and Techniques
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Machine Learning in Python
Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used. The chapters on penalized linear regression and ensemble methods dive deep into each of the algorithms, and you can use the sample code in the book to develop your own data analysis solutions. Machine learning algorithms are at the core of data analytics and visualization. In the past, these methods required a deep background in math and statistics, often in combination with the specialized R programming language. This book demonstrates how machine learning can be implemented using the more widely used and accessible Python programming language. Predict outcomes using linear and ensemble algorithm families Build predictive models that solve a range of simple and complex problems Apply core machine learning algorithms using Python Use sample code directly to build custom solutions Machine learning doesn't have to be complex and highly specialized. Python makes this technology more accessible to a much wider audience, using methods that are simpler, effective, and well tested. Machine Learning in Python shows you how to do this, without requiring an extensive background in math or statistics.