April 08 2021

Complete Algorithms Complexity and Big O Notation Course

Complete Algorithms Complexity and Big O Notation Course

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 16 lectures (1h 57m) | Size: 582.2 MB


From beginner to professional in 2 hours!

What you'll learn:
Algorithm Time and Space Complexity Evaluation
Big O Notation
Interview Coding Tasks Review
Time Complexity Cases
Time Complexities Comparison
Mathematical Comparison of Functions
Typical Complexities Evaluation
log N Complexity
Strings and Complexity Evaluation
Recursive Algorithms Complexity
Amortized Analysis
Space Complexity

Requirements
Basic programming skills (you need to know how to write an "if-else" clause and a "for" loop)

Description
To develop effective code, each developer needs to know how to evaluate the complexity of the algorithms.

The course "Big O Notation. Algorithms Complexity Evaluation." in simple language explains the mathematics behind the complexity of algorithms, cases of complexity, the complexity of recursion, strings, amortized analysis and space complexity. In addition we solve 15 examples, some of which are found in interviews on Google, Facebook, Amazon.

We have reworked many books and articles to the most effective for perception and understanding form. As a result this course is independent by its nature and does not require studying of any additional materials. Basic programming skills is the only requirement to understand the course!

Important note: you can always pause the video and process into every aspect of the material in detail!

Who this course is for
Beginners who want to understand from scratch to professional what complexity evaluation is
Developers of any level who want to pass an interview at companies such as Google, Facebook, Apple
Developers who want to learn how to write effective code
Beginner developers curious about time complexity evaluation and Big O Notation

Homepage
https://www.udemy.com/course/algorithms-complexity/




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