Data Structures and Algorithms with Python (Undergraduate Topics in Computer Science)

Data Structures and Algorithms with Python (Undergraduate Topics in Computer Science)

E- BookalgorithmsComputer sciencedata structuresprogrammingpython

English | ISBN: 9783319130712 | 369 pages | January 13, 2015 | True PDF | 12.82 MB This textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms supported by examples that bring meaning to the

Data Structures and Algorithms: Ace technical coding interviews (Nanodegree Program)

Data Structures and Algorithms: Ace technical coding interviews (Nanodegree Program)

Udacityalgorithmsdata structuresnanodegree programtechnical interview

Udacity | Duration: 7 h 49 m | Video: H264 1280×720 | Audio: AAC 44,1 kHz 2ch | 2,15 GB | Language: English + .vtt | 2019 Get hands-on practice with over 80 data structures and algorithm exercises and guidance from a dedicated mentor to help prepare you for interviews and on-the-job scenarios Learn different

Graph Algorithms: Practical Examples in Apache Spark and Neo4j

Graph Algorithms: Practical Examples in Apache Spark and Neo4j

E- BookalgorithmsApacheApache SparkGraphNeo4jPractical examples

English | 2019 | ISBN: 1492047686 | 241 Pages | PDF | 13 MB Learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions and enhance your machine learning models. With this practical guide,developers and data scientists will discover how graph analytics deliver value, whether they’re used for building

Data Science Algorithms in a Week: Top 7 algorithms for computing, data analysis, and machine learning

Data Science Algorithms in a Week: Top 7 algorithms for computing, data analysis, and machine learning

E- BookalgorithmscomputingData analysisdata sciencemachine learning

English | September 20th, 2017 | ISBN: 1787284581 | 210 pages | EPUB |2.09 MB Machine learning applications are highly automated and self-modifying, and they continue to improve over time with minimal human intervention as they learn with more data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have