Algorithms in Motion

Algorithms in Motion
Algorithms in Motion

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 4h 11m | 2.4 GB
eLearning | Skill level: All Levels

Algorithms – established processes for solving computational problems – are the foundation of computer programming. Mastering the most important algorithms and learning to recognize where they should be applied are required skills for any developer. Algorithms in Motion introduces you to the world of algorithms and how to use them as effectively as possible through high-quality video-based lessons and real-world examples, so you can put what you learn into practice. Based on the best-selling book Grokking Algorithms, this video course brings classic algorithms to life!

An algorithm is a repeatable procedure for solving a problem. The algorithms you’ll use most often as a programmer have already been discovered, tested, and proven. This engaging course makes it easy to learn and use the most important algorithms effectively.

Algorithms in Motion teaches you how to apply common algorithms to the practical problems you face every day as a programmer. Following the expert guidance of liveVideo instructor Beau Carnes, you’ll start with the basics, including Big O notation, fundamental data structures, and recursion. Then, you’ll explore problem-solving techniques, that will empower you to see the algorithm you need in the task you’re trying to accomplish. Finally, you’ll finish the course by applying more advanced algorithms, such as hash tables, graph algorithms, and K-nearest.

This easy-to-follow video course is perfect for self-taught programmers, engineers, or anyone who wants to brush up on classic algorithms.


  • Search, sort, and graph algorithms
  • Breadth-first search
  • Performance concerns
  • Implementing algorithms in Python
+ Table of Contents

01 Introduction
02 Binary Search
03 Big O Notation
04 Arrays and Linked Lists
05 Selection Sort
06 Recursion
07 The Stack
08 Divide and Conquer
09 Quicksort
10 Big O Notation Revisited
11 Hash Functions
12 Use Cases
13 Collisions
14 Performance
15 Graph Introduction
16 Implementing the graph
17 Working with Dijkstra’s algorithm
18 Trading for a piano
19 Implementing Dijkstra’s algorithm
20 Greedy Algorithm Examples
21 NP Complete Problems
22 The Knapsack Problem
23 The Knapsack Problem FAQ
24 Longest Common Substring
25 Classifying Oranges vs Grapefruits
26 Regression and Features
27 Introduction to Machine Learning
28 Trees
29 Inverted Indexes
30 The Fourier Transform
31 Parallel Algorithms
32 MapReduce
33 Bloom Filters and HyperLogLog
34 SHA Algorithms
35 Locality Sensitive Hashing
36 Diffie Hellman Key Exchange
37 Linear Programming

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