9781492041139 - Data Science from Scratch: First Principles with Python von Grus, Joel (16 Ergebnisse)

Zustand: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.

Paperback. Zustand: Very Good.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.

Zustand: New. In.

Zustand: New. 2019. 2nd New edition. Paperback. . . . . . Books ship from the US and Ireland.

Zustand: New. pp. 406.

Paperback. Zustand: Brand New. 2nd edition. 384 pages. 9.25x7.00x1.00 inches. In Stock.

Zustand: NEW.

Zustand: New. With this updated second edition, you ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.&Uumlber den AutorrnrnJoel Grus is a research engineer at the Allen Institute f.

Taschenbuch. Zustand: Neu. Data Science from Scratch | First Principles with Python | Joel Grus | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2019 | O'Reilly Media | EAN 9781492041139 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Neuware -To really learn data science, you should not only master the toolsdata science libraries, frameworks, modules, and toolkitsbut also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today's messy glut of data.- Get a crash course in Python- Learn the basics of linear algebra, statistics, and probabilityand how and when they're used in data science- Collect, explore, clean, munge, and manipulate data- Dive into the fundamentals of machine learning- Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering- Explore recommender systems, natural language processing, network analysis, MapReduce, and databasesLibri GmbH, Europaallee 1, 36244 Bad Hersfeld Englisch.

Taschenbuch. Zustand: Neu. Neuware - To really learn data science, you should not only master the toolsdata science libraries, frameworks, modules, and toolkitsbut also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today's messy glut of data.- Get a crash course in Python- Learn the basics of linear algebra, statistics, and probabilityand how and when they're used in data science- Collect, explore, clean, munge, and manipulate data- Dive into the fundamentals of machine learning- Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering- Explore recommender systems, natural language processing, network analysis, MapReduce, and databases.

Zustand: Gut. Zustand: Gut | Seiten: 384 | Sprache: Englisch | Produktart: Bücher | To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today's messy glut of data.- Get a crash course in Python- Learn the basics of linear algebra, statistics, and probability—and how and when they're used in data science- Collect, explore, clean, munge, and manipulate data- Dive into the fundamentals of machine learning- Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering- Explore recommender systems, natural language processing, network analysis, MapReduce, and databases.

Taschenbuch. Zustand: Neu. Neuware -To really learn data science, you should not only master the toolsdata science libraries, frameworks, modules, and toolkitsbut also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today's messy glut of data.- Get a crash course in Python- Learn the basics of linear algebra, statistics, and probabilityand how and when they're used in data science- Collect, explore, clean, munge, and manipulate data- Dive into the fundamentals of machine learning- Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering- Explore recommender systems, natural language processing, network analysis, MapReduce, and databases Englisch.