Adams rod (44 Ergebnisse)

Zustand: Fine. 310 pp., paperback, fine. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country. Riddell, Brenda (illustrator).…

Paperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged. Riddell, Brenda (illustrator).

Paperback. Zustand: New. Riddell, Brenda (illustrator).

Paperback. Zustand: New. Riddell, Brenda (illustrator).

Paperback. Zustand: Brand New. 310 pages. 8.90x5.98x0.71 inches. In Stock. Riddell, Brenda (illustrator).

Paperback. Zustand: Brand New. 304 pages. 8.50x5.50x0.76 inches. In Stock.

Zustand: New. Riddell, Brenda (illustrator).

Zustand: As New. Unread book in perfect condition. Riddell, Brenda (illustrator).

Zustand: New. Riddell, Brenda (illustrator).

paperback. Zustand: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

paperback. Zustand: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Paperback. Zustand: New. This textbook serves as a companion to "A Mathematical Introduction to Data Science". It uses Python programming to provide a comprehensive foundation in the mathematics needed for data science. It is designed for anyone with a basic mathematical background, including students and self-learners interested in understanding the principles behind the computational algorithms used in data science. The focus of this book is to demonstrate how programming can aid in this understanding and be used in solving mathematical problems. It is written using Python as its programming language, but readers do not need prior knowledge of Python to benefit from it.Some examples from "A Mathematical Introduction to Data Science" are used to illustrate key concepts such as sets, functions, linear algebra, calculus, and probability and statistics, through Python programming, though it is not necessary to have seen the examples before. Further, this textbook shows how those mathematical concepts can be applied in widely used computational algorithms, such as Principal Component Analysis, Singular Value Decomposition, Linear Regression in two and more dimensions, Simple Neural Networks, Maximum Likelihood Estimation, Logistic Regression and Ridge Regression.This textbook is designed with the assumption that readers have no prior knowledge of Python but possess a basic understanding of programming concepts, such as control flow. Ideally, readers should have both this book and its companion, "A Mathematical Introduction to Data Science". However, those with a strong mathematical background and an interest in programming implementations can benefit from reading this textbook alone.…

Paperback. Zustand: New. This textbook serves as a companion to "A Mathematical Introduction to Data Science". It uses Python programming to provide a comprehensive foundation in the mathematics needed for data science. It is designed for anyone with a basic mathematical background, including students and self-learners interested in understanding the principles behind the computational algorithms used in data science. The focus of this book is to demonstrate how programming can aid in this understanding and be used in solving mathematical problems. It is written using Python as its programming language, but readers do not need prior knowledge of Python to benefit from it.Some examples from "A Mathematical Introduction to Data Science" are used to illustrate key concepts such as sets, functions, linear algebra, calculus, and probability and statistics, through Python programming, though it is not necessary to have seen the examples before. Further, this textbook shows how those mathematical concepts can be applied in widely used computational algorithms, such as Principal Component Analysis, Singular Value Decomposition, Linear Regression in two and more dimensions, Simple Neural Networks, Maximum Likelihood Estimation, Logistic Regression and Ridge Regression.This textbook is designed with the assumption that readers have no prior knowledge of Python but possess a basic understanding of programming concepts, such as control flow. Ideally, readers should have both this book and its companion, "A Mathematical Introduction to Data Science". However, those with a strong mathematical background and an interest in programming implementations can benefit from reading this textbook alone.…

Soft cover. Zustand: Fine. 4th Edition. 1 score (480 pages) ; 31 cm ; OCLC 51785991 ISBN 9780881883077, 0881883077 ; "Piano/vocal/guitar." ; color black photographic stiff paper wrappers ; Contents: All at once you love her -- Always -- Always on my mind -- And I love her -- Bali Ha'i -- Ben -- Bewitched -- Body and soul -- By the time I get to Phoenix -- Call me irresponsible -- Climb ev'ry mountain -- (They long to be) close to you -- Daddy's little girl -- A day in the life of a fool -- Didn't we -- A dream is a wish your heart makes --Endless love -- Feelings -- Fields of gold -- The first time ever I saw your face -- Fly me to the moon -- -- From a distance -- Georgia on my mind -- God only knows -- Good morning heartache -- Good night -- Hands of time -- Here's that rainy day -- How are things in Glocca Morra -- How deep is the ocean -- How deep is your love -- I can't get started with you -- I have dreamed -- I honestly love you -- I just called to say I love you -- I left my heart in San Francisco -- I love Paris -- I want you, I need you, I love you -- I won't last a day without you -- I'll be seeing you -- I'll be there -- I'll have to say I love you in a song -- I'll remember April -- I've got you under my skin -- I've grown accustomed to her face -- If I ruled the world -- If you go away -- Imagination -- In my room -- In the still of the night -- In the wee small hours of the morning -- Isn't it romantic? -- It might as well be spring -- It's a blue world -- Jesse -- The last waltz -- Little girl blue -- Longer -- Look to the rainbow -- Lost in the stars -- Love, look away -- Loving you -- Lullaby of the leaves -- Manhattan -- Memories of you -- Memory -- Midnight blue -- Mr. Wonderful -- Mona Lisa -- Moon River -- Moonlight in Vermont -- More -- My Cherie amour -- My funny valentine -- My one and only love -- My romance -- The nearness of you -- The night is young (and you're so beautiful) -- Oh, what a beautiful mornin' -- Old devil moon -- The party's over -- People -- People will say we're in love -- Picnic -- Polka dots and moonbeams -- The rainbow connection -- Rainy days and Mondays -- Red sails in the sunset -- Release me -- Return to me -- Ribbon in the sky -- September song -- She -- Ships -- Since I don't have you -- Sincerely -- Skylark -- Small world -- Smile -- So in love -- Some day my prince will come -- Some enchanted evening -- Something wonderful -- Somewhere, my love -- Spanish eyes -- Speak low -- Speak softly, love -- Stardust -- Stay with me -- Stella by starlight -- Tammy -- Tears in heaven -- Tenderly -- Tennessee waltz -- There's a small hotel -- These foolish things -- The things we did last summer -- Till there was you -- A time for us -- Time in a bottle -- Tonight, I celebrate my love -- True love -- Try to remember -- The twelfth of never -- Unchained melody -- Until it's time for you to go -- The very thought of you -- We kiss in a shadow -- We've only just begun -- What a wonderful world -- What kind of fool am I? -- What'll I do? -- When I fall in love -- When the world was young -- When you wish upon a star -- Where do I begin -- Where or when -- Who can I turn to -- Willow weep for me -- Woman -- Yellow days -- Yesterday -- Yesterday once more -- Yesterday, when I was young -- You are beautiful -- You are so beautiful -- You are the sunshine of my life -- You needed me -- Young at heart -- Younger than springtime ; FINE. Book. …

Paperback. Zustand: New. Riddell, Brenda (illustrator).

Paperback. Zustand: New. This textbook provides a comprehensive foundation in the mathematics needed for data science for students and self-learners with a basic mathematical background who are interested in the principles behind computational algorithms in data science. It covers sets, functions, linear algebra, and calculus, and delves deeply into probability and statistics, which are key areas for understanding the algorithms driving modern data science applications. Readers are guided toward unlocking the secrets of algorithms like Principal Component Analysis, Singular Value Decomposition, Linear Regression in two and more dimensions, Simple Neural Networks, Maximum Likelihood Estimation, Logistic Regression and Ridge Regression, illuminating the path from mathematical principles to algorithmic mastery.It is designed to make the material accessible and engaging, guiding readers through a step-by-step progression from basic mathematical concepts to complex data science algorithms. It stands out for its emphasis on worked examples and exercises that encourage active participation, making it particularly beneficial for those with limited mathematical backgrounds but a strong desire to learn. This approach facilitates a smoother transition into more advanced topics.The authors expect readers to be proficient in handling numbers in various formats, including fractions, decimals, percentages, and surds. They should also have a knowledge of introductory algebra, such as manipulating simple algebraic expressions, solving simple equations, and graphing elementary functions, along with a basic understanding of geometry including angles, trigonometry and Pythagoras' theorem. …

Zustand: New. Riddell, Brenda (illustrator).

Zustand: New.

Zustand: New.

Hardcover. Zustand: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Zustand: As New. Unread book in perfect condition.

Hardcover. Zustand: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Paperback. Zustand: Brand New. pap/psc edition. 330 pages. 9.25x6.10x9.17 inches. In Stock.

Paperback. Zustand: Brand New. 2216 pages. 9.00x6.00x1.80 inches. In Stock.

Paperback. Zustand: New. Riddell, Brenda (illustrator).

Paperback. Zustand: Brand New. 440 pages. 9.26x6.11x9.21 inches. In Stock.

Zustand: New.

Zustand: New.

Paperback. Zustand: New. This textbook serves as a companion to "A Mathematical Introduction to Data Science". It uses Python programming to provide a comprehensive foundation in the mathematics needed for data science. It is designed for anyone with a basic mathematical background, including students and self-learners interested in understanding the principles behind the computational algorithms used in data science. The focus of this book is to demonstrate how programming can aid in this understanding and be used in solving mathematical problems. It is written using Python as its programming language, but readers do not need prior knowledge of Python to benefit from it.Some examples from "A Mathematical Introduction to Data Science" are used to illustrate key concepts such as sets, functions, linear algebra, calculus, and probability and statistics, through Python programming, though it is not necessary to have seen the examples before. Further, this textbook shows how those mathematical concepts can be applied in widely used computational algorithms, such as Principal Component Analysis, Singular Value Decomposition, Linear Regression in two and more dimensions, Simple Neural Networks, Maximum Likelihood Estimation, Logistic Regression and Ridge Regression.This textbook is designed with the assumption that readers have no prior knowledge of Python but possess a basic understanding of programming concepts, such as control flow. Ideally, readers should have both this book and its companion, "A Mathematical Introduction to Data Science". However, those with a strong mathematical background and an interest in programming implementations can benefit from reading this textbook alone.…

Paperback. Zustand: New. This textbook serves as a companion to "A Mathematical Introduction to Data Science". It uses Python programming to provide a comprehensive foundation in the mathematics needed for data science. It is designed for anyone with a basic mathematical background, including students and self-learners interested in understanding the principles behind the computational algorithms used in data science. The focus of this book is to demonstrate how programming can aid in this understanding and be used in solving mathematical problems. It is written using Python as its programming language, but readers do not need prior knowledge of Python to benefit from it.Some examples from "A Mathematical Introduction to Data Science" are used to illustrate key concepts such as sets, functions, linear algebra, calculus, and probability and statistics, through Python programming, though it is not necessary to have seen the examples before. Further, this textbook shows how those mathematical concepts can be applied in widely used computational algorithms, such as Principal Component Analysis, Singular Value Decomposition, Linear Regression in two and more dimensions, Simple Neural Networks, Maximum Likelihood Estimation, Logistic Regression and Ridge Regression.This textbook is designed with the assumption that readers have no prior knowledge of Python but possess a basic understanding of programming concepts, such as control flow. Ideally, readers should have both this book and its companion, "A Mathematical Introduction to Data Science". However, those with a strong mathematical background and an interest in programming implementations can benefit from reading this textbook alone.…