Search result for Mit introduction to algorithms lectures Online Courses & Certifications
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Advanced Data Structures
by Aspen Olmsted- 0.0
9 Weeks
Introduction to Programming in C++ Introduction to Data Structures Students learn how to utilize and program these data structures through the lectures and the labs. C++ programming material is presented over eight weeks of interactive lectures with quizzes to assess your understanding of the material. This course focuses on the efficiency of different data structures to solve various computational problems....
$332
Big Data Applications: Machine Learning at Scale
by Alexey A. Dral , Vladimir Lesnichenko , Evgeny Frolov , Ilya Trofimov , Pavel Mezentsev , Emeli Dral- 3.8
Approx. 28 hours to complete
To become successful, you’d better know what kinds of problems can be solved with machine learning, and how they can be solved. Don’t know where to start? - learn how to work with texts; With these skills, you will be able to tackle many practical machine learning tasks. Introduction to large scale machine learning...
Genomic Data Science and Clustering (Bioinformatics V)
by Pavel Pevzner , Phillip Compeau- 4.2
Approx. 10 hours to complete
In the first half of the course, we will introduce algorithms for clustering a group of objects into a collection of clusters based on their similarity, a classic problem in data science, and see how these algorithms can be applied to gene expression data. Week 1: Introduction to Clustering Algorithms From Hard to Soft Clustering...
Java Programming: Principles of Software Design
by Robert Duvall , Owen Astrachan , Andrew D. Hilton , Susan H. Rodger- 4.6
Approx. 13 hours to complete
Learn how to create programming solutions that scale using Java interfaces. Use timing data to analyze empirical performance; Recognize the limitations of algorithms and Java programs in solving problems. Welcome to the Course Introduction Introduction Interfaces to Avoid Duplication Earthquakes: Sorting Algorithms Introduction Translating to Code Introduction Where To Go From Here...
Bioinformatics Capstone: Big Data in Biology
by Phillip Compeau , Pavel Pevzner- 4.1
Approx. 14 hours to complete
In this course, you will learn how to use the BaseSpace cloud platform developed by Illumina (our industry partner) to apply several standard bioinformatics software approaches to real biological data. Plus, hacker track students will have the option to build their own genome assembler and apply it to real data! Introduction Introduction...
Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming
by Tim Roughgarden- 4.8
Approx. 15 hours to complete
The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees). Introduction to Greedy Algorithms Application to Clustering Introduction and Motivation...
Scalable Machine Learning on Big Data using Apache Spark
by Romeo Kienzler- 3.8
Approx. 7 hours to complete
- make use of large scale compute clusters to apply machine learning algorithms on Petabytes of data using Apache SparkML Pipelines. - basic machine learning (optional introduction videos are provided in this course as well) Introduction to Apache Spark for Machine Learning on BigData Week 3: Introduction to Apache SparkML Introduction to SparkML Introduction to Clustering: k-Means...
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Algorithms for Searching, Sorting, and Indexing
by Sriram Sankaranarayanan- 4.6
Approx. 34 hours to complete
This course covers basics of algorithm design and analysis, as well as algorithms for sorting arrays, data structures such as priority queues, hash functions, and applications such as Bloom filters. Introduction to Randomization + Average Case Analysis + Recurrences Design basic algorithms to implement sorting, selection, and hash functions in heap data structures...
Deep Learning and Reinforcement Learning
by Mark J Grover , Miguel Maldonado- 4.7
Approx. 14 hours to complete
Introduction to Neural Networks Introduction to Neural Networks - Part 1 Introduction to Neural Networks - Part 2 Introduction to Neural Networks - Part 3 Introduction to Neural Networks - Part 4 Introduction to Neural Networks Notebook - Part 1 Introduction to Neural Networks Notebook - Part 2 Introduction to Backpropagation in Neural Networks - Part 1...
Nuclear Energy: Science, Systems and Society
by Jacopo Buongiorno , Anne White , Michael Short , John Parsons- 0.0
11 Weeks
Nuclear Energy: Science, Systems and Society offers an introduction to the basic physics of nuclear energy and radiation, with an emphasis on the unique attributes and challenges of nuclear energy as a low-carbon solution. Learners will be able to critically assess questions such as; "Can nuclear energy help to solve the climate change problem?...
$75