Search result for Graph algorithms Online Courses & Certifications
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Algebra & Algorithms
by Дмитрий ИльинскийTop Instructor , Alex Dainiak- 0.0
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Approx. 30 hours to complete
Algebra is one of the definitive and oldest branches of mathematics, and design of computer algorithms is one of the youngest. Graph Reachability and Distances via Matrix Multiplication Computing Transitive Closure of a Graph Design efficient algorithms problems in graph theory related to distances and matchings based on fast matrix computations and randomization....
Competitive Programming for Beginners
by Filipp Rukhovich , Ilia Stepanov , Oleg Hristenko , Vladislav Nevstruev- 0.0
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Approx. 153 hours to complete
After this course, you will learn what types of problems you will have to solve at the competitions, what is the effective program, how to estimate the algorithms efficiency, how to use basic algorithms and ideas during the problems solution. Linear Algorithms Graph Theory Ways of graph storage Basic Algorithms Number and Graph Theories...
Learn By Example : Apache Flink
by Loony Corn- 3.5
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3 hours on-demand video
Flink is a stream processing technology with added capability to do lots of other things like batch processing, graph algorithms, machine learning etc. 6) Applying ML algorithms on the fly using Flink-ML 7) Representing Graph data using Gelly...
$12.99
I/O-efficient algorithms
by Mark de Berg- 4.6
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Approx. 10 hours to complete
- O-notation, Ω-notation, Θ-notation; how to analyze algorithms - Graph terminology, representations of graphs (adjacency lists and adjacency matrix), basic graph algorithms (BFS, DFS, topological sort, shortest paths) Why I/O-efficient Algorithms Analyzing algorithms in the I/O-model Analyzing algorithms in the I/O-model, II Cache-aware versus cache-oblivious algorithms Designing cache-aware and cache-oblivious algorithms Designing cache-aware and cache-oblivious algorithms...
From 0 to 1 : Spark for Data Science with Python
by Loony Corn- 4.2
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8.5 hours on-demand video
Machine Learning and Data Science : Spark's core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We'll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets....
$12.99
Probabilistic Graphical Models 1: Representation
by Daphne Koller- 4.6
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Approx. 67 hours to complete
These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more....
Scalable programming with Scala and Spark
by Loony Corn- 4.4
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9 hours on-demand video
Machine Learning and Data Science : Spark's core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We'll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets....
$12.99
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Algorithms: Design and Analysis, Part 1
by Tim Roughgarden- 0.0
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6 Weeks
Specific topics in the course include: "Big-oh" notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), randomized algorithms (QuickSort, contraction algorithm for min cuts), data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of BFS and DFS, connectivity, shortest paths). Randomized algorithms (QuickSort, contraction algorithm for min cuts)...
$149
From 0 to 1: Data Structures & Algorithms in Java
by Loony Corn- 4.2
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15 hours on-demand video
This is an animated, visual and spatial way to learn data structures and algorithms when we can close our eyes and see itMore than most other concepts, Data Structures and Algorithms are best learnt visually. Big-O notation and complexityStacksQueuesTreesHeapsGraphs and Graph AlgorithmsLinked listsSortingSearching...
$14.99
Genome Sequencing (Bioinformatics II)
by Pavel Pevzner , Phillip Compeau- 4.6
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Approx. 17 hours to complete
In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will see how brute force algorithms that try every possible solution are able to identify naturally occurring antibiotics so that they can be synthesized in a lab....
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