Coursera Algorithms

Coursera: Algorithms - Princeton University (Part I + Part II) [repost]  eBooks & eLearning

Posted by ParRus at Oct. 24, 2016
Coursera: Algorithms - Princeton University (Part I + Part II) [repost]

Coursera: Algorithms - Princeton University
WEBRip | English | MP4 + PDF Guides + Excercises files | 960 x 540 | AVC ~76.2 kbps | 29.970 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | 28:38:37 | 2.78 GB
Genre: eLearning Video / Computer Science, Development, Programming

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. An introduction to fundamental data types, algorithms, and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Specific topics covered include: union-find algorithms; basic iterable data types (stack, queues, and bags); sorting algorithms (quicksort, mergesort, heapsort) and applications; priority queues; binary search trees; red-black trees; hash tables; and symbol-table applications.

Coursera - Algorithms: Design and Analysis, Part 1 (2013) [repost]  eBooks & eLearning

Posted by ParRus at May 3, 2015
Coursera - Algorithms: Design and Analysis, Part 1 (2013) [repost]

Coursera - Algorithms: Design and Analysis, Part 1 (2013)
English | MP4 + PDF slides | 960 x 540 | AVC ~22.2 kbps | 15 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English (srt) | ~19 hours | 1.24 GB
Genre: eLearning Video / Programming, Algorithm

In this course you will learn several fundamental principles of algorithm design. You'll learn the divide-and-conquer design paradigm, with applications to fast sorting, searching, and multiplication. You'll learn several blazingly fast primitives for computing on graphs, such as how to compute connectivity information and shortest paths. Finally, we'll study how allowing the computer to "flip coins" can lead to elegant and practical algorithms and data structures.

Stanford University: Coursera - Algorithms: Design and Analysis, Part 2 (2013)  eBooks & eLearning

Posted by ParRus at June 6, 2013
Stanford University: Coursera - Algorithms: Design and Analysis, Part 2 (2013)

Stanford University: Coursera - Algorithms: Design and Analysis, Part 2
English | MP4 | 960 x 540 | AVC ~22.1 kbps | 15 fps
AAC | 122 Kbps | 44.1 KHz | 2 channels | Subs: English (srt) | 19:04:49 | 1.52 GB
Genre: eLearning Video / Computer Science: Theory

In this course you will learn several fundamental principles of advanced algorithm design. You'll learn the greedy algorithm design paradigm, with applications to computing good network backbones (i.e., spanning trees) and good codes for data compression. You'll learn the tricky yet widely applicable dynamic programming algorithm design paradigm, with applications to routing in the Internet and sequencing genome fragments.

Coursera - Algorithms: Design and Analysis Part 1 (2013)  eBooks & eLearning

Posted by house23 at April 11, 2013
Coursera - Algorithms: Design and Analysis Part 1 (2013)

Coursera - Algorithms: Design and Analysis Part 1 (2013)
MP4 | AVC 21kbps | English | 960x540 | 15fps | 19 hours | AAC stereo 114kbps | 1.24 GB
Genre: Video Training

In this course you will learn several fundamental principles of algorithm design. You'll learn the divide-and-conquer design paradigm, with applications to fast sorting, searching, and multiplication. You'll learn several blazingly fast primitives for computing on graphs, such as how to compute connectivity information and shortest paths. Finally, we'll study how allowing the computer to "flip coins" can lead to elegant and practical algorithms and data structures. Learn the answers to questions such as: How do data structures like heaps, hash tables, bloom filters, and balanced search trees actually work, anyway? How come QuickSort runs so fast? What can graph algorithms tell us about the structure of the Web and social networks? Did my 3rd-grade teacher explain only a suboptimal algorithm for multiplying two numbers?

Coursera - Mining Massive Datasets (2016)  eBooks & eLearning

Posted by ParRus at Dec. 25, 2016
Coursera - Mining Massive Datasets (2016)

Coursera - Mining Massive Datasets (2016)
Stanford University with Jure Leskovec, Anand Rajaraman, Jeff Ullman

WEBRip | English | MP4 + PDF Guides | 960 x 540 | AVC ~76.7 kbps | 29.970 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English (.srt) | 20:04:35 | 1.88 GB
Genre: eLearning Video / Data Science and Big Data

We introduce the participant to modern distributed file systems and MapReduce, including what distinguishes good MapReduce algorithms from good algorithms in general. The rest of the course is devoted to algorithms for extracting models and information from large datasets. Participants will learn how Google's PageRank algorithm models importance of Web pages and some of the many extensions that have been used for a variety of purposes.

Coursera - From GPS and Google Maps to Spatial Computing, University of Minnesota  eBooks & eLearning

Posted by ParRus at June 20, 2016
Coursera - From GPS and Google Maps to Spatial Computing, University of Minnesota

Coursera - From GPS and Google Maps to Spatial Computing
WEBRip | English | MP4 + work files | 960 x 540 | AVC ~151 kbps | 29.970 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English | ~13 hours | 1.52 GB
Genre: eLearning Video / Science, Technology

From Google Maps to consumer global positioning system (GPS) devices, spatial technology shapes many lives in both ordinary and extraordinary ways. Thanks to spatial computing, a hiker in Yellowstone and a taxi driver in Manhattan can know precisely where they are, discover nearby points of interest and learn how to reach their destinations. Spatial computing technology is what powers the Foursquare check-in, the maps app on your smartphone, the devices used by scientists to track endangered species, the routing directions that help you get from point A to point B, the precision agriculture technology that is revolutionizing farming, and the augmented reality devices like Google Glass that may soon mediate our interaction with the real world.

Coursera - Coding the Matrix: Linear Algebra through Computer Science Applications  eBooks & eLearning

Posted by house23 at June 19, 2016
Coursera - Coding the Matrix: Linear Algebra through Computer Science Applications

Coursera - Coding the Matrix: Linear Algebra through Computer Science Applications
MP4 | AVC 271kbps | English | 960x540 | 30fps | 11 hours | AAC stereo 127kbps | 2.77 GB
Genre: Video Training

Learn the concepts and methods of linear algebra, and how to use them to think about computational problems arising in computer science. Coursework includes building on the concepts to write small programs and run them on real data.

Coursera - Process Mining: Data Science in Action [repost]  eBooks & eLearning

Posted by ParRus at Feb. 18, 2016
Coursera - Process Mining: Data Science in Action [repost]

Coursera - Process Mining: Data Science in Action
WEBRip | English | MP4 | 960 x 540 | AVC ~151 kbps | 29.970 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | 13:23:25 | 1.68 GB
Genre: eLearning Video / Computer Science, Engineering and Technology

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining.

Coursera - Machine Learning (2015)  eBooks & eLearning

Posted by house23 at Feb. 17, 2016
Coursera - Machine Learning (2015)

Coursera - Machine Learning (2015)
MP4 | AVC 32kbps | English | 960x540 | 15fps | 19h 53mins | AAC stereo 128kbps | 1.52 GB
Genre: Video Training

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.

Coursera - Introduction to Data Science  eBooks & eLearning

Posted by house23 at Feb. 16, 2016
Coursera - Introduction to Data Science

Coursera - Introduction to Data Science
MP4 | AVC 88kbps | English | 960x540 | 30fps | 16h 03mins | AAC stereo 113kbps | 3.88 GB
Genre: Video Training

Commerce and research is being transformed by data-driven discovery and prediction. Skills required for data analytics at massive levels – scalable data management on and off the cloud, parallel algorithms, statistical modeling, and proficiency with a complex ecosystem of tools and platforms – span a variety of disciplines and are not easy to obtain through conventional curricula. Tour the basic techniques of data science, including both SQL and NoSQL solutions for massive data management (e.g., MapReduce and contemporaries), algorithms for data mining (e.g., clustering and association rule mining), and basic statistical modeling (e.g., linear and non-linear regression).