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98 Algorithms Training Courses

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The 7 Essential Steps To Getting Your Dream Career

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Tom Cassidy - Internationally Published Author, Thought Engineer, Reasonable Polymath, using algorithms to help people crack the deepest issues of their lives: self-worth, self-belief, goal-achieving, feeling good, achieving susta…

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Intro to Theoretical Computer Science

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Solving Hard Problems This class teaches you about basic concepts in theoretical computer science—such as NP-completeness—and what they imply for… Class Summary This class teaches you about basic concepts in theoretical computer s…

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Probabilistic Graphical Models 2: Inference

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About this course: Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: …

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C++ For C Programmers, Part A

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Created by: University of California, Santa Cruz Taught by: Ira Pohl, Professor Computer Science Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.3 stars Average User Rating 4.3See wh…

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On Strategy : What Managers Can Learn from Great Philosophers

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About the Course The business world is at first sight a world of numbers. Accounting, algorithms, processes, quarterly figures, market shar…

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Computational Neuroscience

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We will make use of Matlab/Octave/Python demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and begin…

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Approximation Algorithms Part II

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About this course: Approximation algorithms, Part 2 This is the continuation of Approximation algorithms, Part 1. Here you will learn linear programming duality applied to the design of some approximation algorithms, and semidefin…

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Finding Hidden Messages in DNA (Bioinformatics I)

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About this course: Named a top 50 MOOC of all time by Class Central! This course begins a series of classes illustrating the power of compu…

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System Validation (2): Model process behaviour

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It is the next logical step in computer science and improving software development in general. It allows automated analysis based on behavioural models of a system to see if a system works correctly. We want to guarantee that the …

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Approximation Algorithms Part I

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About this course: Approximation algorithms, Part I How efficiently can you pack objects into a minimum number of boxes? How well can you cluster nodes so as to cheaply separate a network into components around a few centers? Thes…

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Algorithmic Thinking (Part 1)

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As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand intera…

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Genome Sequencing (Bioinformatics II)

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We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics. In the first half of the course, we will see that biologists cannot read the 3 billion nucleotides of a human genome…

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Practical Machine Learning

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About this course: One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an …

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Interactive Computer Graphics

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About this course: Computer graphics can be a powerful tool for supporting visual problem solving, and interactivity plays a central role i…

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Cloud Computing Concepts, Part 1

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About this course: Cloud computing systems today, whether open-source or used inside companies, are built using a common set of core techniques, algorithms, and design philosophies – all centered around distributed systems. Learn …