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This course is about data structures and algorithms. We are going to implement the problems in Java, but I try to do it as generic as possible: so the core of the algorithms can be used in C++ or Python. The course takes approximately 7 hours to complete. I highly recommend typing out these data structures several times on your own in order to get a good grasp of it.
Section 1:
- what are prefix trees (tries)
- basics operations: insertion, sorting and autocomplete
- longest common prefix problem
- prefix trees applications in networking
Section 2:
- what are ternary search trees
- basic operations: insertion and retrieval
Section 3:
- substring search algorithms
- brute-force substring search
- Boyer-Moore substring search
- Rabin-Karp algorithm
Section 4:
- strings in programming
- prefixes and suffixes
- longest common prefix and longest repeated substring problem
Section 5:
- basic sorting algorithms
- bubble sort and selection sort
- insertion sort and shell sort
- quicksort and merge sort
Section 6:
- what is data compression
- run length encoding
- Huffman-encoding
- LZW compression and decompression
First, we are going to discuss prefix trees: modern search engines for example use these data structures quite often. When you make a google search there is an autocomplete feature because of the underlying trie data structure. It is also good for sorting: hashtables do not support sort operation but on the other hand, tries do support.
Substring search is another important field of computer science.You will learn about Boyer-Moore algorithm and we will discuss brute-force approach as well as Raabin-Karp method.
The next chapter is about sorting. How to sort an array of integers, doubles, strings or custom objects? We can do it with bubble sort, insertion sort, mergesort or quicksort. You will learn a lot about the theory as well as the concrete implementation of these important algorithms.
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The last lectures are about data compression: run-length encoding, Huffman encoding and LZW compression.
Thanks for joining the course, let's get started!
- This course is meant for university students with quantitative background (mathematics, computer science) but anyone with core java knowledge can get a good grasp of the lectures
English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 7.5 | 1.00 GB
eLearning | Skill level: All Levels
eLearning | Skill level: All Levels
Data compression, tries, substring search and sorting
This course is about data structures and algorithms. We are going to implement the problems in Java, but I try to do it as generic as possible: so the core of the algorithms can be used in C++ or Python. The course takes approximately 7 hours to complete. I highly recommend typing out these data structures several times on your own in order to get a good grasp of it.
First, we are going to discuss prefix trees: modern search engines for example use these data structures quite often. When you make a google search there is an autocomplete feature because of the underlying trie data structure. It is also good for sorting: hashtables do not support sort operation but on the other hand, tries do support.
Substring search is another important field of computer science.You will learn about Boyer-Moore algorithm and we will discuss brute-force approach as well as Raabin-Karp method.
The next chapter is about sorting. How to sort an array of integers, doubles, strings or custom objects? We can do it with bubble sort, insertion sort, mergesort or quicksort. You will learn a lot about the theory as well as the concrete implementation of these important algorithms.
The last lectures are about data compression: run-length encoding, Huffman encoding and LZW compression.
What Will I Learn?
- Grasp the fundamentals of algorithms and data structures
- Develop your own algorithms that best fit to the personal need
- Detect non-optimal code snippets
- Get to know data compression
- Get to know sorting algorithms
Introduction
1 Introduction
2 Complexity theory
1 Introduction
2 Complexity theory
Prefix Tree – Tries
3 Prefix tree introduction – basics
4 Prefix tree introduction – insert
5 Prefix tree introduction – sorting
6 Prefix tree introduction – autocomplete
7 Prefix tree introduction – trie as a hash table
8 Prefix tree introduction – hashing versus tries
9 Prefix tree introduction – applications
10 Prefix tree implementation – insert_ search I
11 Prefix tree implementation – insert_ search II
12 Prefix tree implementation – insert_ search III
13 Prefix tree implementation – as a map
14 Prefix tree implementation – autocomplete
15 Prefix tree implementation – sorting
16 Longest common prefix
17 Longest common prefix in networking
3 Prefix tree introduction – basics
4 Prefix tree introduction – insert
5 Prefix tree introduction – sorting
6 Prefix tree introduction – autocomplete
7 Prefix tree introduction – trie as a hash table
8 Prefix tree introduction – hashing versus tries
9 Prefix tree introduction – applications
10 Prefix tree implementation – insert_ search I
11 Prefix tree implementation – insert_ search II
12 Prefix tree implementation – insert_ search III
13 Prefix tree implementation – as a map
14 Prefix tree implementation – autocomplete
15 Prefix tree implementation – sorting
16 Longest common prefix
17 Longest common prefix in networking
Ternary Search Tree
18 Ternary search tree introduction – basics
19 Ternary search tree introduction – insert
20 Ternary search tree introduction – get
21 Ternary search tree introduction – applications
22 Ternary search tree implementation I
23 Ternary search tree implementation II
18 Ternary search tree introduction – basics
19 Ternary search tree introduction – insert
20 Ternary search tree introduction – get
21 Ternary search tree introduction – applications
22 Ternary search tree implementation I
23 Ternary search tree implementation II
Substring Search
24 Brute-force search introduction
25 Brute-force search implementation
26 Boyer-Moore search introduction
27 Boyer-Moore search example
28 Boyer-Moore implementation I
29 Boyer-Moore implementation II
30 Boyer-Moore implementation III
31 Rabin-Karp algorithm introduction
24 Brute-force search introduction
25 Brute-force search implementation
26 Boyer-Moore search introduction
27 Boyer-Moore search example
28 Boyer-Moore implementation I
29 Boyer-Moore implementation II
30 Boyer-Moore implementation III
31 Rabin-Karp algorithm introduction
Strings
32 Strings introduction
33 String reversion
34 Suffixes
35 Prefixes
36 Longest common prefix
37 Longest repeated substring problem
32 Strings introduction
33 String reversion
34 Suffixes
35 Prefixes
36 Longest common prefix
37 Longest repeated substring problem
Basic Sorting Algorithms
38 Sorting introduction
39 Adaptive sorting algorithms
40 Bogo sort introduction
41 Bogo sort implementation
42 Sleep sort
43 Bubble sort introduction
44 Bubble sort implementation
45 Selection sort introduction
46 Selection sort implementation
47 Insertion sort introduction
48 Insertion sort implementation
49 Shell sort introduction
50 Shell sort implementation
51 Quicksort introduction I
52 Quicksort introduction II
53 Quicksort implementation
54 Merge sort introduction – divide
55 Merge sort introduction – conquer
56 Merge sort implementation
57 Hybrid algorithms introduction
58 Non-comparison based algorithms
59 Counting sort introduction
60 Counting sort implementation
61 Radix sort introduction
38 Sorting introduction
39 Adaptive sorting algorithms
40 Bogo sort introduction
41 Bogo sort implementation
42 Sleep sort
43 Bubble sort introduction
44 Bubble sort implementation
45 Selection sort introduction
46 Selection sort implementation
47 Insertion sort introduction
48 Insertion sort implementation
49 Shell sort introduction
50 Shell sort implementation
51 Quicksort introduction I
52 Quicksort introduction II
53 Quicksort implementation
54 Merge sort introduction – divide
55 Merge sort introduction – conquer
56 Merge sort implementation
57 Hybrid algorithms introduction
58 Non-comparison based algorithms
59 Counting sort introduction
60 Counting sort implementation
61 Radix sort introduction
Data Compression
62 Run length encoding introduction
63 Run length encoding implementation – encode
64 Run length encoding implementation – decode
65 Huffman encoding introduction
66 Huffman decoding
67 Huffman encoding implementation I – helper classes
68 Huffman encoding implementation II – encoding
69 Huffman encoding implementation III – testing
70 LZW compression introduction – compression
71 LZW compression introduction – decompression
72 LZW implementation
62 Run length encoding introduction
63 Run length encoding implementation – encode
64 Run length encoding implementation – decode
65 Huffman encoding introduction
66 Huffman decoding
67 Huffman encoding implementation I – helper classes
68 Huffman encoding implementation II – encoding
69 Huffman encoding implementation III – testing
70 LZW compression introduction – compression
71 LZW compression introduction – decompression
72 LZW implementation
Source Code & Slides
73 Slides
74 Source code
75 DISCOUNT FOR OTHER COURSES! Stud welder studpro 3750xi.
73 Slides
74 Source code
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Topic information
29-12-2018, 15:23Data Structures And Algorithms - High Level Made Easy
Category: Video Tutorials
Data Structures And Algorithms - High Level Made Easy
.MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | 3.58 GB
Duration: 8.5 hour | Genre: eLearning | Language: English
Abstract reasoning makes algorithms easy. Learn about Sort, Search, Trees, Hash Tables, and Heaps.
What you'll learn
they will be able to use algorithms with ease to solve their programming problems.
build a search engine from scratch
Requirements
Some programming experience is helpful but not required
Description
Have you ever used data structures in Python or Java? Well, do you really understand how they work? If you struggle with algorithms, it means you are approaching this topic the wrong way!
There are lots of places to copy code from, but without having high level understanding, it can be difficult to handle data structures well.
This course teaches the fundamentals of algorithms, so you can apply knowledge easily. This course contains NO PROGRAMMING, JUST PSEUDOCODE to encourage understanding of Algorithms and how to use them
Whatever the reason, if you are looking for a course that focus on the implementations to give you a complete understanding of how things work, then this is the course for you. This course is a combination of two courses I took as an undergraduate and taught to graduate students at Cornell and Stanford.
This course goes over the theory of how things work, but only to give you what you need to know to understand the implementation covered.
This is a hands-on course! If you want to try understand things at a deep level, and work on implementations, rather than theory, then again, this is the course for you.
Topics covered:-
Arrays Linked Lists Trees Hashtables Stacks Queues Heaps Sort algorithms Search algorithms
The course also spends more time than most other courses of its kind looking at what's available in the JDK. Students wanting to understand how things work 'under the hood' will benefit enormously from this course.
Why learn about data structures and algorithms?
The reality is, the more you learn about data structures and algorithms, the better a programmer you become.
Why?
Because, data structures and algorithms are effectively patterns for solving problems. You want to add as many of them as you can to your skill-set. By doing so, you will find you solve more problems, and use the right tools for the job, in a more elegant way. And you will learn HEAPS of them in this course.
Why enrolling in this course is the best decision you can make.
Most courses focus on giving you the theory of how things work, so that you can take an interview. Whilst the theory is important, the knowledge of how to implement these data structures and algorithms are of vital importance. This course goes over the theory of how things work, but only to give you what you need to know to understand the implementation covered. The main focus of the course is to give you a real understanding of how things work under the hood, so that you can apply this to future programming projects.
If you want to actually understand how things work, and be able to take that understanding and apply it to your own programs, then this course is for you.
After completing this course, you will have a solid understanding of data structures and algorithms. The sooner you sign up for this course, the sooner you will have the skills and knowledge you need to increase your job or consulting opportunities. Both Python and Java developers with key skills and understanding of data structures and algorithms are in high demand and get paid extremely well. You need to MASTER ALGORITHMS to be a respected programmers.
If you are ready for that new job promotion or consulting opportunity, it's time to get started.
Why not get started today?
Click the Signup button to sign up for the course
Who is the target audience?
Developers who have some knowledge of Python or Java looking to understand data structures and algorithms at a deep level
Who is the target audience?
Anyone interested in programming in any language.
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Data Structures And Algorithms - High Level Made Easy
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