Coding Interview Resources

Important Machine Learning Algorithms 👆

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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.me/free4unow_backup ENJOY LEARNING 👍👍

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Algorithms for Coding Interviews 👆

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69 Functions in Python ✅

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Python Iterators ☝️

Control Flow in Python 👆

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List of most asked Programming Interview Questions. Are you preparing for a coding interview? This tweet is for you. It contains a list of the most asked interview questions from each topic. Arrays - How is an array sorted using quicksort? - How do you reverse an array? - How do you remove duplicates from an array? - How do you find the 2nd largest number in an unsorted integer array? Linked Lists - How do you find the length of a linked list? - How do you reverse a linked list? - How do you find the third node from the end? - How are duplicate nodes removed in an unsorted linked list? Strings - How do you check if a string contains only digits? - How can a given string be reversed? - How do you find the first non-repeated character? - How do you find duplicate characters in strings? Binary Trees - How are all leaves of a binary tree printed? - How do you check if a tree is a binary search tree? - How is a binary search tree implemented? - Find the lowest common ancestor in a binary tree? Graph - How to detect a cycle in a directed graph? - How to detect a cycle in an undirected graph? - Find the total number of strongly connected components? - Find whether a path exists between two nodes of a graph? - Find the minimum number of swaps required to sort an array. Dynamic Programming 1. Find the longest common subsequence? 2. Find the longest common substring? 3. Coin change problem? 4. Box stacking problem? 5. Count the number of ways to cover a distance?

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Why Algorithm is Important for a Program An efficient algorithm determines how fast and effectively a program can solve a problem. While modern hardware provides abundant memory, execution time remains a critical factor. Faster algorithms save time, enhance user experience, and enable scalability, especially for large datasets or real-time applications. A poor algorithm can lead to inefficiencies that no amount of hardware can fix. Why Space is Less Important With advancements in technology, storage has become cheaper and more abundant. For most applications, the cost of additional memory is negligible compared to the time lost due to an inefficient algorithm. However, in constrained environments (like embedded systems), space considerations may still matter.

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Important DSA concepts: • Arrays • Strings • Sorting & Searching • Hashing • Linked List • Stack & Queue • Recursion & Backtracking • Binary Tree & BST • Heap & Priority Queue • Graph Theory • Dynamic Programming (DP) • Greedy Algorithms • Bit Manipulation • Math & Number Theory • Trie & Advanced Data Structures

Object Oriented Programming ✅