De-Mystifying Math and Stats for Machine Learning : Mastering the Fundamentals of Mathematics and Statistics for Machine Learning
Enregistré dans:
| Auteur principal: | |
|---|---|
| Support: | E-Book |
| Langue: | Anglais |
| Publié: |
Birmingham :
Packt Publishing.
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| Autres localisations: | Voir dans le Sudoc |
| Résumé: | Unlock the secrets of math and statistics to elevate your machine learning skills. This comprehensive course covers key concepts, from central tendency to gradient descent, essential for any aspiring data scientist.Key FeaturesDetailed exploration of key mathematical and statistical concepts for Machine Learning.Logical flow from basic to advanced topics for seamless knowledge building.Engaging materials designed to enhance learning and retention.Book DescriptionBeginning with basic concepts like central tendency, dispersion, and types of distribution, this course will help you build a robust understanding of data analysis. It progresses to more advanced topics, including hypothesis testing, outliers, and the intricacies of dependent versus independent variables, ensuring you grasp the statistical tools necessary for data-driven decision-making. Moving ahead, you'll explore the mathematical frameworks crucial for machine learning algorithms. Learn about the significance of percentiles, the distinction between population and sample, and the vital role of precision versus accuracy in data science. Chapters on linear algebra and regression will enhance your ability to implement and interpret complex models, while practical lessons on measuring algorithm accuracy and understanding key machine learning concepts will round out your expertise. The course culminates with an in-depth look at specific machine learning techniques such as decision trees, k-nearest neighbors (kNN), and gradient descent. Each chapter builds on the last, guiding you through a logical progression of knowledge and skills. By the end, you will have not only mastered the theoretical aspects but also gained practical insights into applying these techniques in real-world scenarios.What you will learnMaster the fundamentals of central tendency and dispersion.Understand the different types of data distributions.Differentiate between precision and accuracy in data analysis.Conduct hypothesis testing and identify outliers.Apply linear algebra and regression techniques in machine learning.Implement decision trees, kNN, & gradient descent algorithms.Who this book is forThis course is designed for technical professionals, data analysts, and aspiring data scientists who are keen to deepen their understanding of the mathematical and statistical principles behind machine learning. Ideal for those with a basic grasp of algebra and statistics, this course will elevate your data analysis capabilities and enhance your proficiency in developing and fine-tuning machine learning models. Familiarity with programming concepts is recommended to fully benefit from the course content |
| Accès en ligne: | Accès à l'E-book |
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| 245 | 1 | 0 | |a De-Mystifying Math and Stats for Machine Learning : |b Mastering the Fundamentals of Mathematics and Statistics for Machine Learning |c Seaport Ai. |
| 264 | 1 | |a Birmingham : |b Packt Publishing. | |
| 264 | 2 | |a Paris : |b Cyberlibris, |c 2024. | |
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| 520 | |a Unlock the secrets of math and statistics to elevate your machine learning skills. This comprehensive course covers key concepts, from central tendency to gradient descent, essential for any aspiring data scientist.Key FeaturesDetailed exploration of key mathematical and statistical concepts for Machine Learning.Logical flow from basic to advanced topics for seamless knowledge building.Engaging materials designed to enhance learning and retention.Book DescriptionBeginning with basic concepts like central tendency, dispersion, and types of distribution, this course will help you build a robust understanding of data analysis. It progresses to more advanced topics, including hypothesis testing, outliers, and the intricacies of dependent versus independent variables, ensuring you grasp the statistical tools necessary for data-driven decision-making. Moving ahead, you'll explore the mathematical frameworks crucial for machine learning algorithms. Learn about the significance of percentiles, the distinction between population and sample, and the vital role of precision versus accuracy in data science. Chapters on linear algebra and regression will enhance your ability to implement and interpret complex models, while practical lessons on measuring algorithm accuracy and understanding key machine learning concepts will round out your expertise. The course culminates with an in-depth look at specific machine learning techniques such as decision trees, k-nearest neighbors (kNN), and gradient descent. Each chapter builds on the last, guiding you through a logical progression of knowledge and skills. By the end, you will have not only mastered the theoretical aspects but also gained practical insights into applying these techniques in real-world scenarios.What you will learnMaster the fundamentals of central tendency and dispersion.Understand the different types of data distributions.Differentiate between precision and accuracy in data analysis.Conduct hypothesis testing and identify outliers.Apply linear algebra and regression techniques in machine learning.Implement decision trees, kNN, & gradient descent algorithms.Who this book is forThis course is designed for technical professionals, data analysts, and aspiring data scientists who are keen to deepen their understanding of the mathematical and statistical principles behind machine learning. Ideal for those with a basic grasp of algebra and statistics, this course will elevate your data analysis capabilities and enhance your proficiency in developing and fine-tuning machine learning models. Familiarity with programming concepts is recommended to fully benefit from the course content | ||
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