🔗 Master 8 Essential Machine Learning Algorithms
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🏟 Here is a complete roadmap to learn Data Structures and Algorithms (DSA) 🏟
1. Basics of Programming: Start by learning the basics of a programming language like Python, Java, or C++. Understand concepts like variables, loops, functions, and arrays.
2. Data Structures: Study fundamental data structures like arrays, linked lists, stacks, queues, trees, graphs, and hash tables. Understand the operations that can be performed on these data structures and their time complexities.
3. Algorithms: Learn common algorithms like searching, sorting, recursion, dynamic programming, greedy algorithms, and divide and conquer. Understand how these algorithms work and their time complexities.
4. Problem Solving: Practice solving coding problems on platforms like LeetCode, HackerRank, or Codeforces. Start with easy problems and gradually move to medium and hard problems.
5. Complexity Analysis: Learn how to analyze the time and space complexity of algorithms. Understand Big O notation and how to calculate the complexity of different algorithms.
6. Advanced Data Structures: Study advanced data structures like AVL trees, B-trees, tries, segment trees, and fenwick trees. Understand when and how to use these data structures in problem-solving.
7. Graph Algorithms: Learn graph traversal algorithms like BFS and DFS. Study algorithms like Dijkstra's algorithm, Bellman-Ford algorithm, and Floyd-Warshall algorithm for shortest path problems.
8. Dynamic Programming: Master dynamic programming techniques for solving complex problems efficiently. Practice solving dynamic programming problems to build your skills.
9. Practice and Review: Regularly practice coding problems and review your solutions. Analyze your mistakes and learn from them to improve your problem-solving skills.
10. Mock Interviews: Prepare for technical interviews by participating in mock interviews and solving interview-style coding problems. Practice explaining your thought process and reasoning behind your solutions.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best 👍👍
1. Basics of Programming: Start by learning the basics of a programming language like Python, Java, or C++. Understand concepts like variables, loops, functions, and arrays.
2. Data Structures: Study fundamental data structures like arrays, linked lists, stacks, queues, trees, graphs, and hash tables. Understand the operations that can be performed on these data structures and their time complexities.
3. Algorithms: Learn common algorithms like searching, sorting, recursion, dynamic programming, greedy algorithms, and divide and conquer. Understand how these algorithms work and their time complexities.
4. Problem Solving: Practice solving coding problems on platforms like LeetCode, HackerRank, or Codeforces. Start with easy problems and gradually move to medium and hard problems.
5. Complexity Analysis: Learn how to analyze the time and space complexity of algorithms. Understand Big O notation and how to calculate the complexity of different algorithms.
6. Advanced Data Structures: Study advanced data structures like AVL trees, B-trees, tries, segment trees, and fenwick trees. Understand when and how to use these data structures in problem-solving.
7. Graph Algorithms: Learn graph traversal algorithms like BFS and DFS. Study algorithms like Dijkstra's algorithm, Bellman-Ford algorithm, and Floyd-Warshall algorithm for shortest path problems.
8. Dynamic Programming: Master dynamic programming techniques for solving complex problems efficiently. Practice solving dynamic programming problems to build your skills.
9. Practice and Review: Regularly practice coding problems and review your solutions. Analyze your mistakes and learn from them to improve your problem-solving skills.
10. Mock Interviews: Prepare for technical interviews by participating in mock interviews and solving interview-style coding problems. Practice explaining your thought process and reasoning behind your solutions.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best 👍👍
❤4
DSA INTERVIEW QUESTIONS AND ANSWERS
1. What is the difference between file structure and storage structure?
The difference lies in the memory area accessed. Storage structure refers to the data structure in the memory of the computer system,
whereas file structure represents the storage structure in the auxiliary memory.
2. Are linked lists considered linear or non-linear Data Structures?
Linked lists are considered both linear and non-linear data structures depending upon the application they are used for. When used for
access strategies, it is considered as a linear data-structure. When used for data storage, it is considered a non-linear data structure.
3. How do you reference all of the elements in a one-dimension array?
All of the elements in a one-dimension array can be referenced using an indexed loop as the array subscript so that the counter runs
from 0 to the array size minus one.
4. What are dynamic Data Structures? Name a few.
They are collections of data in memory that expand and contract to grow or shrink in size as a program runs. This enables the programmer
to control exactly how much memory is to be utilized.Examples are the dynamic array, linked list, stack, queue, and heap.
5. What is a Dequeue?
It is a double-ended queue, or a data structure, where the elements can be inserted or deleted at both ends (FRONT and REAR).
6. What operations can be performed on queues?
enqueue() adds an element to the end of the queue
dequeue() removes an element from the front of the queue
init() is used for initializing the queue
isEmpty tests for whether or not the queue is empty
The front is used to get the value of the first data item but does not remove it
The rear is used to get the last item from a queue.
7. What is the merge sort? How does it work?
Merge sort is a divide-and-conquer algorithm for sorting the data. It works by merging and sorting adjacent data to create bigger sorted
lists, which are then merged recursively to form even bigger sorted lists until you have one single sorted list.
8.How does the Selection sort work?
Selection sort works by repeatedly picking the smallest number in ascending order from the list and placing it at the beginning. This process is repeated moving toward the end of the list or sorted subarray.
Scan all items and find the smallest. Switch over the position as the first item. Repeat the selection sort on the remaining N-1 items. We always iterate forward (i from 0 to N-1) and swap with the smallest element (always i).
Time complexity: best case O(n2); worst O(n2)
Space complexity: worst O(1)
9. What are the applications of graph Data Structure?
Transport grids where stations are represented as vertices and routes as the edges of the graph
Utility graphs of power or water, where vertices are connection points and edge the wires or pipes connecting them
Social network graphs to determine the flow of information and hotspots (edges and vertices)
Neural networks where vertices represent neurons and edge the synapses between them
10. What is an AVL tree?
An AVL (Adelson, Velskii, and Landi) tree is a height balancing binary search tree in which the difference of heights of the left
and right subtrees of any node is less than or equal to one. This controls the height of the binary search tree by not letting
it get skewed. This is used when working with a large data set, with continual pruning through insertion and deletion of data.
11. Differentiate NULL and VOID ?
Null is a value, whereas Void is a data type identifier
Null indicates an empty value for a variable, whereas void indicates pointers that have no initial size
Null means it never existed; Void means it existed but is not in effect
You can check these resources for Coding interview Preparation
Credits: https://t.iss.one/free4unow_backup
All the best 👍👍
1. What is the difference between file structure and storage structure?
The difference lies in the memory area accessed. Storage structure refers to the data structure in the memory of the computer system,
whereas file structure represents the storage structure in the auxiliary memory.
2. Are linked lists considered linear or non-linear Data Structures?
Linked lists are considered both linear and non-linear data structures depending upon the application they are used for. When used for
access strategies, it is considered as a linear data-structure. When used for data storage, it is considered a non-linear data structure.
3. How do you reference all of the elements in a one-dimension array?
All of the elements in a one-dimension array can be referenced using an indexed loop as the array subscript so that the counter runs
from 0 to the array size minus one.
4. What are dynamic Data Structures? Name a few.
They are collections of data in memory that expand and contract to grow or shrink in size as a program runs. This enables the programmer
to control exactly how much memory is to be utilized.Examples are the dynamic array, linked list, stack, queue, and heap.
5. What is a Dequeue?
It is a double-ended queue, or a data structure, where the elements can be inserted or deleted at both ends (FRONT and REAR).
6. What operations can be performed on queues?
enqueue() adds an element to the end of the queue
dequeue() removes an element from the front of the queue
init() is used for initializing the queue
isEmpty tests for whether or not the queue is empty
The front is used to get the value of the first data item but does not remove it
The rear is used to get the last item from a queue.
7. What is the merge sort? How does it work?
Merge sort is a divide-and-conquer algorithm for sorting the data. It works by merging and sorting adjacent data to create bigger sorted
lists, which are then merged recursively to form even bigger sorted lists until you have one single sorted list.
8.How does the Selection sort work?
Selection sort works by repeatedly picking the smallest number in ascending order from the list and placing it at the beginning. This process is repeated moving toward the end of the list or sorted subarray.
Scan all items and find the smallest. Switch over the position as the first item. Repeat the selection sort on the remaining N-1 items. We always iterate forward (i from 0 to N-1) and swap with the smallest element (always i).
Time complexity: best case O(n2); worst O(n2)
Space complexity: worst O(1)
9. What are the applications of graph Data Structure?
Transport grids where stations are represented as vertices and routes as the edges of the graph
Utility graphs of power or water, where vertices are connection points and edge the wires or pipes connecting them
Social network graphs to determine the flow of information and hotspots (edges and vertices)
Neural networks where vertices represent neurons and edge the synapses between them
10. What is an AVL tree?
An AVL (Adelson, Velskii, and Landi) tree is a height balancing binary search tree in which the difference of heights of the left
and right subtrees of any node is less than or equal to one. This controls the height of the binary search tree by not letting
it get skewed. This is used when working with a large data set, with continual pruning through insertion and deletion of data.
11. Differentiate NULL and VOID ?
Null is a value, whereas Void is a data type identifier
Null indicates an empty value for a variable, whereas void indicates pointers that have no initial size
Null means it never existed; Void means it existed but is not in effect
You can check these resources for Coding interview Preparation
Credits: https://t.iss.one/free4unow_backup
All the best 👍👍
👍2
Let's explore some of the best open source projects by language.
1⃣ Best Python Open Source Projects
🚣♂ TensorFlow
🚣♂ Matplotlib
🚣♂ Flask
🚣♂ Django
🚣♂ PyTorch
2⃣ Best JavaScript Open Source Projects
🚣♂ React
🚣♂ Node.JS
🚣♂ jQuery
3⃣ Best C++ Open Source Projects
🚣♂ Serenity
🚣♂ MongoDB
🚣♂ SonarSource
🚣♂ OBS Studio
🚣♂ Electron
4⃣ Best Java Open Source Projects
🚣♂ Mockito
🚣♂ Realm
🚣♂ Jenkins
🚣♂ Guava
🚣♂ Moshi
It's time to start developing your own open source projects. Explore the projects
1⃣ Best Python Open Source Projects
🚣♂ TensorFlow
🚣♂ Matplotlib
🚣♂ Flask
🚣♂ Django
🚣♂ PyTorch
2⃣ Best JavaScript Open Source Projects
🚣♂ React
🚣♂ Node.JS
🚣♂ jQuery
3⃣ Best C++ Open Source Projects
🚣♂ Serenity
🚣♂ MongoDB
🚣♂ SonarSource
🚣♂ OBS Studio
🚣♂ Electron
4⃣ Best Java Open Source Projects
🚣♂ Mockito
🚣♂ Realm
🚣♂ Jenkins
🚣♂ Guava
🚣♂ Moshi
It's time to start developing your own open source projects. Explore the projects
❤1
Basics of Programming 👆✅
🥰4❤3
⭕ MAHINDRA Interview Experience ⭕
Technical Round:
1) Explain the working of your projects.
2) What are your favourite subjects?
3) Discuss about improving engine
efficiency and fuel economy.
4) What are the CNG driven cars' future in
India?
5) What is an in-car technology?
HR Round:
1) Tell me about yourself?
2) Why do you want to join our company?
3) What are your weakness and strong
points?
4) Can you tell us any instance of your
life when you worked as a leader?
5) Why should we hire you? Etc.
Technical Round:
1) Explain the working of your projects.
2) What are your favourite subjects?
3) Discuss about improving engine
efficiency and fuel economy.
4) What are the CNG driven cars' future in
India?
5) What is an in-car technology?
HR Round:
1) Tell me about yourself?
2) Why do you want to join our company?
3) What are your weakness and strong
points?
4) Can you tell us any instance of your
life when you worked as a leader?
5) Why should we hire you? Etc.
👍2❤1👌1
Learning Python in 2025 is like discovering a treasure chest 🎁 full of magical powers! Here's why it's valuable:
1. Versatility 🌟: Python is used in web development, data analysis, artificial intelligence, machine learning, automation, and more. Whatever your interest, Python has an option for it.
2. Ease of Learning 📚: Python's syntax is as clear as a sunny day!☀️ Its simple and readable syntax makes it beginner-friendly, perfect for aspiring programmers of all levels.
3. Community Support 🤝: Python has a vast community of programmers ready to help! Whether you're stuck on a problem or looking for guidance, there are countless forums, tutorials, and resources to tap into.
4. Job Opportunities 💼: Companies are constantly seeking Python wizards to join their ranks! From tech giants to startups, the demand for Python skills is abundant.🔥
5. Future-proofing 🔮: With its widespread adoption and continuous growth, learning Python now sets you up for success in the ever-evolving world of tech.
6. Fun Projects 🎉: Python makes coding feel like brewing potions! From creating games 🎮 to building robots 🤖, the possibilities are endless.
So grab your keyboard and embark on a Python adventure! It's not just learning a language, it's unlocking a world of endless possibilities.
1. Versatility 🌟: Python is used in web development, data analysis, artificial intelligence, machine learning, automation, and more. Whatever your interest, Python has an option for it.
2. Ease of Learning 📚: Python's syntax is as clear as a sunny day!☀️ Its simple and readable syntax makes it beginner-friendly, perfect for aspiring programmers of all levels.
3. Community Support 🤝: Python has a vast community of programmers ready to help! Whether you're stuck on a problem or looking for guidance, there are countless forums, tutorials, and resources to tap into.
4. Job Opportunities 💼: Companies are constantly seeking Python wizards to join their ranks! From tech giants to startups, the demand for Python skills is abundant.🔥
5. Future-proofing 🔮: With its widespread adoption and continuous growth, learning Python now sets you up for success in the ever-evolving world of tech.
6. Fun Projects 🎉: Python makes coding feel like brewing potions! From creating games 🎮 to building robots 🤖, the possibilities are endless.
So grab your keyboard and embark on a Python adventure! It's not just learning a language, it's unlocking a world of endless possibilities.
👍4
⌨️ Benefits of learning Python Programming
1. Web Development: Python frameworks like Django and Flask are popular for building dynamic websites and web applications.
2. Data Analysis: Python has powerful libraries like Pandas and NumPy for data manipulation and analysis, making it widely used in data science and analytic.
3. Machine Learning: Python's libraries such as TensorFlow, Keras, and Scikit-learn are extensively used for implementing machine learning algorithms and building predictive models.
4. Artificial Intelligence: Python is commonly used in AI development due to its simplicity and extensive libraries for tasks like natural language processing, image recognition, and neural network implementation.
5. Cybersecurity: Python is utilized for tasks such as penetration testing, network scanning, and creating security tools due to its versatility and ease of use.
6. Game Development: Python, along with libraries like Pygame, is used for developing games, prototyping game mechanics, and creating game scripts.
7. Automation: Python's simplicity and versatility make it ideal for automating repetitive tasks, such as scripting, data scraping, and process automation.
1. Web Development: Python frameworks like Django and Flask are popular for building dynamic websites and web applications.
2. Data Analysis: Python has powerful libraries like Pandas and NumPy for data manipulation and analysis, making it widely used in data science and analytic.
3. Machine Learning: Python's libraries such as TensorFlow, Keras, and Scikit-learn are extensively used for implementing machine learning algorithms and building predictive models.
4. Artificial Intelligence: Python is commonly used in AI development due to its simplicity and extensive libraries for tasks like natural language processing, image recognition, and neural network implementation.
5. Cybersecurity: Python is utilized for tasks such as penetration testing, network scanning, and creating security tools due to its versatility and ease of use.
6. Game Development: Python, along with libraries like Pygame, is used for developing games, prototyping game mechanics, and creating game scripts.
7. Automation: Python's simplicity and versatility make it ideal for automating repetitive tasks, such as scripting, data scraping, and process automation.
👍4❤2
🔰 Web Development Roadmap for Beginners 2025
├── 🌐 Introduction to Web Development
├── 🧱 Frontend vs Backend vs Full Stack
├── 🖼 HTML Basics (Elements, Attributes, Forms)
├── 🎨 CSS Basics (Selectors, Box Model, Flexbox, Grid)
├── 🎯 Responsive Design & Media Queries
├── 🧠 JavaScript Fundamentals
├── ⚙️ DOM Manipulation
├── ⚡ Basic Git & GitHub
├── ⚛️ Modern JS Concepts (ES6+, Arrow Functions, Destructuring)
├── 🧩 Frontend Frameworks (React Basics)
├── 🔧 Package Managers (npm, yarn)
├── 🗃 Backend Introduction (Node.js + Express.js)
├── 🗄 Databases (SQL vs NoSQL, MongoDB Basics)
├── 🔐 Authentication & Authorization (JWT, OAuth)
├── 📡 APIs (RESTful APIs, Fetch, Axios)
├── 📦 Hosting & Deployment (Netlify, Vercel, Render)
├── 🧪 Final Projects (Portfolio, Blog, To-Do App, E-commerce)
Web Development Resources ⬇️
https://whatsapp.com/channel/0029Vax4TBY9Bb62pAS3mX32
ENJOY LEARNING 👍👍
#webdevelopment
├── 🌐 Introduction to Web Development
├── 🧱 Frontend vs Backend vs Full Stack
├── 🖼 HTML Basics (Elements, Attributes, Forms)
├── 🎨 CSS Basics (Selectors, Box Model, Flexbox, Grid)
├── 🎯 Responsive Design & Media Queries
├── 🧠 JavaScript Fundamentals
├── ⚙️ DOM Manipulation
├── ⚡ Basic Git & GitHub
├── ⚛️ Modern JS Concepts (ES6+, Arrow Functions, Destructuring)
├── 🧩 Frontend Frameworks (React Basics)
├── 🔧 Package Managers (npm, yarn)
├── 🗃 Backend Introduction (Node.js + Express.js)
├── 🗄 Databases (SQL vs NoSQL, MongoDB Basics)
├── 🔐 Authentication & Authorization (JWT, OAuth)
├── 📡 APIs (RESTful APIs, Fetch, Axios)
├── 📦 Hosting & Deployment (Netlify, Vercel, Render)
├── 🧪 Final Projects (Portfolio, Blog, To-Do App, E-commerce)
Web Development Resources ⬇️
https://whatsapp.com/channel/0029Vax4TBY9Bb62pAS3mX32
ENJOY LEARNING 👍👍
#webdevelopment
👍4👌1
Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months
### Week 1: Introduction to Python
Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions
Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules
Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode
### Week 2: Advanced Python Concepts
Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions
Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files
Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation
Day 14: Practice Day
- Solve intermediate problems on coding platforms
### Week 3: Introduction to Data Structures
Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists
Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues
Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions
Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues
### Week 4: Fundamental Algorithms
Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort
Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis
Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques
Day 28: Practice Day
- Solve problems on sorting, searching, and hashing
### Week 5: Advanced Data Structures
Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)
Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps
Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)
Day 35: Practice Day
- Solve problems on trees, heaps, and graphs
### Week 6: Advanced Algorithms
Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)
Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms
Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree
Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms
### Week 7: Problem Solving and Optimization
Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems
Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef
Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization
Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them
### Week 8: Final Stretch and Project
Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts
Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project
Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems
Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report
Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)
Best DSA RESOURCES: https://topmate.io/coding/886874
Credits: https://t.iss.one/free4unow_backup
ENJOY LEARNING 👍👍
### Week 1: Introduction to Python
Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions
Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules
Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode
### Week 2: Advanced Python Concepts
Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions
Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files
Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation
Day 14: Practice Day
- Solve intermediate problems on coding platforms
### Week 3: Introduction to Data Structures
Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists
Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues
Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions
Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues
### Week 4: Fundamental Algorithms
Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort
Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis
Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques
Day 28: Practice Day
- Solve problems on sorting, searching, and hashing
### Week 5: Advanced Data Structures
Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)
Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps
Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)
Day 35: Practice Day
- Solve problems on trees, heaps, and graphs
### Week 6: Advanced Algorithms
Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)
Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms
Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree
Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms
### Week 7: Problem Solving and Optimization
Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems
Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef
Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization
Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them
### Week 8: Final Stretch and Project
Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts
Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project
Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems
Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report
Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)
Best DSA RESOURCES: https://topmate.io/coding/886874
Credits: https://t.iss.one/free4unow_backup
ENJOY LEARNING 👍👍
❤5👍1
DSA (Data Structures and Algorithms) Essential Topics for Interviews
1️⃣ Arrays and Strings
Basic operations (insert, delete, update)
Two-pointer technique
Sliding window
Prefix sum
Kadane’s algorithm
Subarray problems
2️⃣ Linked List
Singly & Doubly Linked List
Reverse a linked list
Detect loop (Floyd’s Cycle)
Merge two sorted lists
Intersection of linked lists
3️⃣ Stack & Queue
Stack using array or linked list
Queue and Circular Queue
Monotonic Stack/Queue
LRU Cache (LinkedHashMap/Deque)
Infix to Postfix conversion
4️⃣ Hashing
HashMap, HashSet
Frequency counting
Two Sum problem
Group Anagrams
Longest Consecutive Sequence
5️⃣ Recursion & Backtracking
Base cases and recursive calls
Subsets, permutations
N-Queens problem
Sudoku solver
Word search
6️⃣ Trees & Binary Trees
Traversals (Inorder, Preorder, Postorder)
Height and Diameter
Balanced Binary Tree
Lowest Common Ancestor (LCA)
Serialize & Deserialize Tree
7️⃣ Binary Search Trees (BST)
Search, Insert, Delete
Validate BST
Kth smallest/largest element
Convert BST to DLL
8️⃣ Heaps & Priority Queues
Min Heap / Max Heap
Heapify
Top K elements
Merge K sorted lists
Median in a stream
9️⃣ Graphs
Representations (adjacency list/matrix)
DFS, BFS
Cycle detection (directed & undirected)
Topological Sort
Dijkstra’s & Bellman-Ford algorithm
Union-Find (Disjoint Set)
10️⃣ Dynamic Programming (DP)
0/1 Knapsack
Longest Common Subsequence
Matrix Chain Multiplication
DP on subsequences
Memoization vs Tabulation
11️⃣ Greedy Algorithms
Activity selection
Huffman coding
Fractional knapsack
Job scheduling
12️⃣ Tries
Insert and search a word
Word search
Auto-complete feature
13️⃣ Bit Manipulation
XOR, AND, OR basics
Check if power of 2
Single Number problem
Count set bits
Coding Interview Resources: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
ENJOY LEARNING 👍👍
1️⃣ Arrays and Strings
Basic operations (insert, delete, update)
Two-pointer technique
Sliding window
Prefix sum
Kadane’s algorithm
Subarray problems
2️⃣ Linked List
Singly & Doubly Linked List
Reverse a linked list
Detect loop (Floyd’s Cycle)
Merge two sorted lists
Intersection of linked lists
3️⃣ Stack & Queue
Stack using array or linked list
Queue and Circular Queue
Monotonic Stack/Queue
LRU Cache (LinkedHashMap/Deque)
Infix to Postfix conversion
4️⃣ Hashing
HashMap, HashSet
Frequency counting
Two Sum problem
Group Anagrams
Longest Consecutive Sequence
5️⃣ Recursion & Backtracking
Base cases and recursive calls
Subsets, permutations
N-Queens problem
Sudoku solver
Word search
6️⃣ Trees & Binary Trees
Traversals (Inorder, Preorder, Postorder)
Height and Diameter
Balanced Binary Tree
Lowest Common Ancestor (LCA)
Serialize & Deserialize Tree
7️⃣ Binary Search Trees (BST)
Search, Insert, Delete
Validate BST
Kth smallest/largest element
Convert BST to DLL
8️⃣ Heaps & Priority Queues
Min Heap / Max Heap
Heapify
Top K elements
Merge K sorted lists
Median in a stream
9️⃣ Graphs
Representations (adjacency list/matrix)
DFS, BFS
Cycle detection (directed & undirected)
Topological Sort
Dijkstra’s & Bellman-Ford algorithm
Union-Find (Disjoint Set)
10️⃣ Dynamic Programming (DP)
0/1 Knapsack
Longest Common Subsequence
Matrix Chain Multiplication
DP on subsequences
Memoization vs Tabulation
11️⃣ Greedy Algorithms
Activity selection
Huffman coding
Fractional knapsack
Job scheduling
12️⃣ Tries
Insert and search a word
Word search
Auto-complete feature
13️⃣ Bit Manipulation
XOR, AND, OR basics
Check if power of 2
Single Number problem
Count set bits
Coding Interview Resources: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
ENJOY LEARNING 👍👍
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