Different types of algorithms :

1. Brute Force Algorithm:
- Tries all possible solutions.
- Simple but can be inefficient.

2. Recursive Algorithm:
- Breaks problems into smaller subproblems.
- Often used in divide-and-conquer approaches.

3. Dynamic Programming Algorithm:
- Optimizes solutions by reusing results.
- Common for optimization problems.

4. Divide And Conquer Algorithm:
- Divides problems, solves, and combines results.
- Efficient for sorting and searching.

5. Greedy Algorithm:
- Makes locally optimal choices.
- Used in minimum spanning trees, Huffman coding.

6. Backtracking Algorithm:
- Explores all options systematically.
- Useful for problems with constraints.

7. Randomized Algorithm:
- Introduces randomness for efficiency.
- Examples: Monte Carlo simulations, randomized quicksort.

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Learn The high level overview of algorithm. 👇

https://www.youtube.com/watch?v=4ae6gujJqjI

Data Structure course.. .

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High level overview of Data Structure

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https://youtu.be/VLLAFULBj78?si=yUUxgYBomfIWU1Gw

Data Structures in short :

- Primitive:
- byte
- short
- int
- float
- char
- boolean
- long
- double

- Non-Primitive:
- Linear:
- Array
- Stack
- Queue
- Linked-list

- Non-linear:
- Trees
- Graph
- Heap
- Hash

#datastructure
#algorithm

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Learn all this list in this video.

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DSA:

1. Data Structures:
- Linked List: A linear data structure where elements are connected via pointers.
- Matrix/Grid: A 2D array with rows and columns.
- Queue: Follows the First-In-First-Out (FIFO) principle.
- Stack: Follows the Last-In-First-Out (LIFO) principle.
- Array: A collection of elements with a fixed size.
- Hash: Uses a hash function to map keys to values.
- Graph: Consists of nodes connected by edges.
- Tree: A hierarchical structure with a root and child nodes.

2. Algorithms:
- Pattern Searching: Finding occurrences of a pattern in a text.
- Divide & Conquer: Breaking a problem into smaller subproblems.
- Searching: Locating an element in a data structure.
- Sorting: Arranging elements in a specific order.
- Bitwise Operations: Manipulating individual bits.
- Greedy Algorithms: Making locally optimal choices.
- Recursion: Solving problems by dividing them into smaller instances.
- Backtracking: Exploring all possible solutions.
- Dynamic Programming: Solving problems by breaking them down into overlapping subproblems.

Remember, mastering these concepts is essential for becoming a proficient programmer! 🚀

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with videos :
Algorithm: https://www.youtube.com/watch?v=4ae6gujJqjI

Data Structure: https://youtu.be/VLLAFULBj78?si=yUUxgYBomfIWU1Gw

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i recommend to watch this videos before learning any development skill.

this are the foundation.

Posted respectively. 👇

Intro to Computer Science and related fields.

https://www.youtube.com/watch?v=QMnwj_i8_AA

Crash Course for Algorithm.

https://www.youtube.com/watch?v=4ae6gujJqjI

Crash Course for Data Structure

https://youtu.be/VLLAFULBj78?si=yUUxgYBomfIWU1Gw

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New to Programming ?

Here's a concise breakdown of the different types of computer languages:

1. Low-Level:
- Machine Language: Binary instructions executed directly by hardware.
- Assembly Language (e.g., NASM): Uses mnemonics for machine instructions.

2. High-Level:
- Procedural (e.g., Fortran): Step-by-step problem-solving.
- Functional (e.g., Haskell, F#): Focuses on functions.
- OOP (e.g., Python, C++): Objects and classes.
- Scripting (e.g., JavaScript, Perl): Automation and web development.

3. Specialized:
- Markup (e.g., HTML, XML): Content structuring.
- Query (e.g., SQL, SPARQL): Database queries.
- Domain-Specific (e.g., MATLAB, R): Tailored applications.

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New Video about Programming.

watch this before learning any programming language.

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