Coursera Algorithms - Princeton University (Part I + Part II) .

Coursera Algorithms - Princeton University (Part I + Part II) .

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.
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C++ Series Data Structures and Algorithms .

C++ Series Data Structures and Algorithms .

C++ Series: Data Structures and Algorithms
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 4 Hours 11M | 877 MB
Genre: eLearning | Language: English

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LINKEDIN LEARNING Applied Machine Learning: Algorithms

LINKEDIN LEARNING Applied Machine Learning: Algorithms

LINKEDIN LEARNING APPLIED MACHINE ALGORITHMS-BiFiSO
English | Size: 348.97 MB
Category: Tutorial

What you'll learn
Models vs. algorithms
Cleaning continuous and categorical variables
Tuning hyperparameters
Pros and cons of logistic regression
Fitting a support vector machines model
When to consider using a multilayer perceptron model
Using the random forest algorithm
Fitting a basic boosting model
In the first installment of the Applied Machine Learning series, instructor Derek Jedamski covered foundational concepts, providing you with a general recipe to follow to attack any machine learning problem in a pragmatic, thorough manner. In this course-the second and final installment in the series-Derek builds on top of that architecture by exploring a variety of algorithms, from logistic regression to gradient boosting, and showing how to set a structure that guides you through picking the best one for the problem at hand. Each algorithm has its pros and cons, making each one the preferred choice for certain types of problems. Understanding what actually drives each algorithm, as well as their benefits and drawbacks, can give you a significant competitive advantage as a data scientist.
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Coursera - Algorithms Design and Analysis - Stanford University (Part 1-2) .

Coursera - Algorithms Design and Analysis - Stanford University (Part 1-2) .

Coursera - Algorithms: Design and Analysis (Stanford University)
WEBRip | English | MP4 | 960 x 540 | AVC ~21.7 kbps | 15 fps
AAC | 113 Kbps | 44.1 KHz | 2 channels | ~30 hours | 3.66 GB
Genre: eLearning Video / Computer Science, Programming

Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This course is an introduction to algorithms for learners with at least a little programming experience. The course is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this course, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists.
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Python Machine Learning By Example- Implement Machine Learning Algorithms and Techniques to Build Intelligent Systems, 2nd Edition

Python Machine Learning By Example- Implement Machine Learning Algorithms and Techniques to Build Intelligent Systems, 2nd Edition

Python Machine Learning By Example- Implement Machine Learning Algorithms and Techniques to Build Intelligent Systems, 2nd Edition
English | Size: 249.77 MB
Category: Tutorial

Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learn
A surging interest in machine learning is due to the fact that it evolutionzies automation by learning patterns in data and using them to make predictions and decisions. Your ML journey starts with this book, as the second edition of the bestseller, Python Machine Learning By Example.
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Developing with Graph Algorithms .

Developing with Graph Algorithms .

Developing with Graph Algorithms
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 3 Hours | 3.85 GB
Genre: eLearning | Language: English

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Linkedin Learning Applied Machine Learning Algorithms-XCODE

Linkedin Learning Applied Machine Learning Algorithms-XCODE

Linkedin Learning Applied Machine Learning Algorithms-XCODE
English | Size: 346.31 MB
Category: Tutorial

In the first installment of the Applied Machine Learning series, instructor Derek Jedamski covered foundational concepts, providing you with a general recipe to follow to attack any machine learning problem in a pragmatic, thorough manner. In this course-the second and final installment in the series-Derek builds on top of that architecture by exploring a variety of algorithms, from logistic regression to gradient boosting, and showing how to set a structure that guides you through picking the best one for the problem at hand. Each algorithm has its pros and cons, making each one the preferred choice for certain types of problems. Understanding what actually drives each algorithm, as well as their benefits and drawbacks, can give you a significant competitive advantage as a data scientist.
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A Practical Guide to Algorithms with JavaScript (2018) .

A Practical Guide to Algorithms with JavaScript (2018) .

A Practical Guide to Algorithms with javascript (2018) - Frontend Masters
1080p WEB-DL | mkv | H.265 HEVC @ 535 Kbps | 1920 x 1080 | 4h 7min | 842 MB
Audio: English AAC LC 2.0 @ 160 Kbps, 48.0 kHz
Genres: eLearning Video, Development, Algorithms, javascript

Bored by the academic approach of most data structures and algorithms courses? This is for you! You'll learn to solve algorithms and analyze space and time complexity in both an interview setting and in your day-to-day development. Following along with the course, you'll practice algorithms with common interview questions using a handful of algorithm techniques. Take a practical look at recursion and learn to optimize your solutions using divide-and-conquer. Implement merge sort and quicksort and understand the tradeoffs of both approaches. Plus, get started with dynamic programming and memorization!
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Learning Data Structures & Algorithms in Python from Scratch .

Learning Data Structures & Algorithms in Python from Scratch .

Learning Data Structures & Algorithms in Python from Scratch
.MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | 1.78 GB
Duration: 5.5 hours | Genre: eLearning Video | Language: English

Understand, Learn and Implement Data Structures in Python

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Udacity - Data Structures and Algorithms Nanodegree (2019) .

Udacity - Data Structures and Algorithms Nanodegree (2019) .

Udacity - Data Structures and Algorithms Nanodegree (2019)
WEBRip | English | MP4 | 1280 x 720 | AVC ~1280 Kbps | 29.970 fps
AAC | 126 Kbps | 44.1 KHz | 2 channels | 07:49:24 | 2.15 GB
Genre: Video Tutorial

In this course you will learn data structures and algorithms by solving 80+ practice problems. You will begin each course by learning to solve defined problems related to a particular data structure and algorithm. By the end of each course, you would be able to evaluate and assess different data structures and algorithms for any open-ended problem and implement a solution based on your design choices.
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