**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.

Inside:

- 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