Essential math data science par nield thomas (40 résultats)
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Etat : Used - Very Good. Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along…the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career.Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market.
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Paperback. Etat : New. To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.Practical examples…with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to:Recognize the nuances and pitfalls of probability mathMaster statistics and hypothesis testing (and avoid common pitfalls)Discover practical applications of probability, statistics, calculus, and machine learningIntuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and addedPerform calculus derivatives and integrals completely from scratch in PythonApply what you've learned to machine learning, including linear regression, logistic regression, and neural networks.
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Paperback or Softback. Etat : New. Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics. Book.
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Paperback. Etat : New. To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.Practical examples…with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to:Recognize the nuances and pitfalls of probability mathMaster statistics and hypothesis testing (and avoid common pitfalls)Discover practical applications of probability, statistics, calculus, and machine learningIntuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and addedPerform calculus derivatives and integrals completely from scratch in PythonApply what you've learned to machine learning, including linear regression, logistic regression, and neural networks.
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Paperback. Etat : new. Paperback. To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.Practica…l examples with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to:Recognize the nuances and pitfalls of probability mathMaster statistics and hypothesis testing (and avoid common pitfalls)Discover practical applications of probability, statistics, calculus, and machine learningIntuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and addedPerform calculus derivatives and integrals completely from scratch in PythonApply what you've learned to machine learning, including linear regression, logistic regression, and neural networks To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Paperback. Etat : New. To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.Practical examples…with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to:Recognize the nuances and pitfalls of probability mathMaster statistics and hypothesis testing (and avoid common pitfalls)Discover practical applications of probability, statistics, calculus, and machine learningIntuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and addedPerform calculus derivatives and integrals completely from scratch in PythonApply what you've learned to machine learning, including linear regression, logistic regression, and neural networks.
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Taschenbuch. Etat : Neu. Neuware -Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural network…s. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: - Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning - Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon - Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance - Manipulate vectors and matrices and perform matrix decomposition - Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks - Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market Englisch.
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Taschenbuch. Etat : Neu. Neuware -Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural network…s. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: - Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning - Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon - Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance - Manipulate vectors and matrices and perform matrix decomposition - Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks - Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market Englisch.
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Taschenbuch. Etat : Neu. Neuware -Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural network…s. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: - Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning - Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon - Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance - Manipulate vectors and matrices and perform matrix decomposition - Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks - Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market.
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Etat : New. Über den AutorThomas Nield is the founder of Nield Consulting Group as well as an instructor at O Reilly Media and University of Southern California. He enjoys making technical content relatable and relevant to those unfamiliar or i.
