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Coursera – Bitcoin and Cryptocurrency Technologies (2016)

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Coursera – Bitcoin and Cryptocurrency Technologies (2016)

English | Size: 4.77 GB (5,125,519,347 Bytes)
Category: Business


There’s a lot of excitement about Bitcoin, but also a lot of confusion about what Bitcoin is and how it works. We’re offering this course focusing on the computer science behind Bitcoin to help cut through the hype and get to the core of what makes Bitcoin unique.
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Coursera – Algorithms, Part I – Kevin Wayne and Robert Sedgewick

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Coursera – Algorithms, Part I – Kevin Wayne and Robert Sedgewick
English | Size: 1.51 GB (1,622,615,266 bytes )
Category: Tutorial


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. [Read more…]

Coursera – Algorithms Part II by Kevin Wayne and Robert Sedgewick

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Coursera – Algorithms Part II by Kevin Wayne and Robert Sedgewick
English | Size: 1.79 GB (1,925,417,068 bytes )
Category: Tutorial


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. [Read more…]

Neural Networks for Machine Learning Coursera Video Lectures – Geoffrey Hinton [78 MP4]

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Neural Networks for Machine Learning Coursera Video Lectures – Geoffrey Hinton [78 MP4]
English | Size: 884.52 MB (927,486,283 bytes )
Category: Tutorial


Neural networks use learning algorithms that are inspired by our understanding of how the brain learns, but they are evaluated by how well they work for practical applications such as speech recognition, object recognition, image retrieval and the ability to recommend products that a user will like. As computers become more powerful, Neural Networks are gradually taking over from simpler Machine Learning methods. They are already at the heart of a new generation of speech recognition devices and they are beginning to outperform earlier systems for recognizing objects in images. The course will explain the new learning procedures that are responsible for these advances, including effective new procedures for learning multiple layers of non-linear features, and give you the skills and understanding required to apply these procedures in many other domains. [Read more…]

Coursera – Machine Learning – Stanford University

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Coursera – Machine Learning – Stanford University

English | Size: 1.62 GB (1,742,839,121 Bytes)
Category: CBTs


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.
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Coursera – Data Analysis and Statistical Inference

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Coursera – Data Analysis and Statistical Inference

English | Size: 1.28 GB (1,376,028,414 Bytes)
Category: CBTs


Recognize the importance of data collection, identify limitations in data collection methods, and determine how they affect the scope of inference.

Use statistical software (R) to summarize data numerically and visually, and to perform data analysis.
Have a conceptual understanding of the unified nature of statistical inference.
Apply estimation and testing methods (confidence intervals and hypothesis tests) to analyze single variables and the relationship between two variables in order to understand natural phenomena and make data-based decisions. [Read more…]

Coursera – Introduction to Computational Finance and Financial Econometrics

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Coursera – Introduction to Computational Finance and Financial Econometrics

English | Size: 3.91 GB (4,194,566,058 Bytes)
Category: Tutorial


Computing asset returns
Univariate random variables and distributions
Characteristics of distributions, the normal distribution, linear function of random variables, quantiles of a distribution, Value-at-Risk.
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Coursera – Genomic Data Science Specialization. [258 MP4, 256 TXT, 255 SRT, 150 PDF, 53 HTML, 49 R, 32 M3U, 20 RMD, 17 IPYN…

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Coursera – Genomic Data Science Specialization. [258 MP4, 256 TXT, 255 SRT, 150 PDF, 53 HTML, 49 R, 32 M3U, 20 RMD, 17 IPYNB, 3 GZ, 2 FA, 1 FASTQ]
English | Size: 3.80 GB (4,078,588,150 bytes )
Category: Medical


“This specialization covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, Python, R, Bioconductor, and Galaxy. The sequence is a stand alone introduction to genomic data science or a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics.” [Read more…]

Coursera – Launch Your Career in Data Science. [322 MP4, 450 PDF]

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Coursera – Launch Your Career in Data Science. [322 MP4, 450 PDF]
English | Size: 4.57 GB (4,905,860,828 bytes )
Category: Career


Downloaded from Coursera and segrageted.

“Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.”
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