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Unlock the Power of Algorithms: Master the Art of Problem Solving, Complexity Analysis, and Optimization Strategies in the Revolutionary World of Computing in 2024!
Learn, Understand, Discuss. "GO" for the Best.
star star star star star | 5.0 (1 ratings) |
Instructor: Sachin Mittal(MTech IISc Bangalore, Ex-Amazon Scientist, GATE AIR 33)
Language: English
Description:
Algorithms 2024 is a comprehensive course that introduces students to the fundamental concepts and techniques of algorithms. Through a combination of theoretical lectures and practical exercises, students will gain a solid understanding of algorithms and their applications in problem-solving.
Key Highlights:
What you will learn:
Features of the course:
1. Quality Content: No Rote-learning. No poor understadning. No By-hearting of formulas, tables or theorems. Understand everything with proofs-intuitions-ideas.
2. No Prerequisites: Every concept is taught from basics, without assuming any prior knowledge whatsoever.
3. Daily Homeworks: Practice material, with solutions, for Every Lecture to test your understanding of concepts of respective lecture.
4. Summary Lectures: Short videos which summarises every concept and detail of the course. Helps in quick revision.
5. Quality Practice Sets: Practice Sets from standard resources, with solutions, containing a lot of quality questions for practice.
6. Weekly Quizzes: Every week, there will be a Live Quiz, containing 15-20 questions, to evaluate your understanding of concepts taught in the previous week. The Quiz questions can be seen, solved even after tha live quiz is over.
7. Doubt Resolution: All your doubts will be resolved directly by the faculty. There is a dedicated Telegram group for Enrolled Students of Goclasses where our faculties resolve students' doubts. So, our students don't have to go anywhere else for asking doubts.
Enroll Now.
Python Programming | |||
Lecture 1: Introduction to Python Programming and Variables in Python | |||
Annotated Notes Lecture 1 Introduction to Python | |||
Lecture 2: Operators and Conditional Statements in Python | |||
Annotated Notes Lecture 2 Operators and Conditional Statements in Python | |||
Lecture 3: Strings in Python | |||
Annotated Notes Lecture 3 Strings in Python | |||
Lecture 4: Lists in Python | |||
Annotated Notes Lecture 4 Lists in Python | |||
Lecture 5: Loops and List Comprehension in Python | |||
Annotated Notes Lecture 5 Loops and List Comprehension in Python | |||
Lecture 6: References, Objects, and List aliasing in Python | |||
Annotated Notes Lecture 6 References and Objects in Python | |||
Lecture 7: Functions in Python | |||
Annotated Notes Lecture 7 Functions in Python | |||
Lecture 8: More on Functions in Python | |||
Annotated Notes Lecture 8 More on Functions in Python | |||
Lecture 9: Default, Keyword, and Variable Number of Arguments | Tuples in Python | |||
Annotated Notes Lecture 9 Default, Keyword, and Variable Number of Arguments | Tuples in Python | |||
Lecture 10: Zip Function, and Lambda Functions | |||
Annotated Notes Lecture 10 Zip and Lambda Functions | |||
Lecture 11: Dictionary and Sets in Python | |||
Annotated Notes Lecture 11 Dictionary | |||
Lecture 12: Sets In python | |||
Annotated Notes Lecture 12 - Sets | |||
Lecture 13: OOPS Concepts | |||
Annotated Notes Lecture 13 OOPS Concepts | |||
Lecture 14: Linked List | |||
Annotated Notes Lecture 14 : LinkedList oops contd | |||
Lecture 15: Linked List - Insertion Deletion | |||
Annotated Notes Lecture 15 : Linked List Insertion Deletion | |||
Lecture 16 : Introduction to Recursion | |||
Annotated Notes Lecture 16 : Introduction to Recursion | |||
Linked List | |||
1a. Introduction to Data Structures | |||
1b. Introduction to Linked list | |||
1c. Why we need Linked list | Array vs Linked list | |||
1d. Printing and Finding Length of Linked List | |||
Lecture 1 Annotated notes | |||
2a. Insertions in linked list | |||
2b.Deletion in Linked list | |||
OPTIONAL : Linked list C code | |||
linkedList | |||
Lecture 2 Annotated Notes | |||
Lecture 3 : Recursion in Linked List (Insert, Delete, questions) | |||
Lecture 3 : Annotated Notes - Lecture 3 : Recursion in Linked List (Insert, Delete, questions) | |||
Lecture 4a: GATE 2022 Reverse a Linked List ( 2 variations ) - PYTHON | |||
Lecture 4b: Reverse a Linked List ( Variations 3 and 4) - PYTHON | |||
Reverse_linked_List.py | |||
Lecture 4: Annotated notes - Reversing Linked List and 4 variations | |||
Lecture 5a : Circular Linked List - PYTHON | |||
Lecture5b : Circular Linked List - PYTHON | |||
Circular_linked_list.py | |||
lecture 5 : Annotated Notes - Circular Linked List - PYTHON | |||
Lecture 6 : Doubly Linked List - PYTHON | |||
Doubly_linked_list.py | |||
Lecture 6 : Annotated Notes - Doubly Linked List - PYTHON | |||
Practice Set Linked List | |||
LIVE: Linked List Practice Set Discussion | |||
LIVE Session Annotated Notes | |||
Weekly Quiz 20 - Linked List | |||
Asymptotic Analysis and Loop Complexities | |||
5a. Introduction to Asymptotic Analysis | |||
5b. Big oh and Big omega Asymptotic Notations | |||
5c. Theta Asymptotic notation | |||
5d. What is Asymptotic comparison | how LOG works | rice grain story | |||
5e. Comparing Different Functions Asymptotically | |||
5f. Little Oh Little Omega | Properties of asymptotic notation | Stirling approximation | |||
6a.Formal set notation of Asymptote symbols | |||
6b. Asymptotic Notations GATE CSE PYQs 1994, 96, 2000,1,3,4,8,11,17 | |||
6c. Analysing the loops time complexity | |||
6d. Time Complexity of loops -2 | |||
Annotated Notes: Lecture 5, 6 - Asymptotic Analysis | |||
Practice Set Loop Complexities | |||
LIVE: Loop Time Complexity Practice Set Discussion | |||
LIVE Session Loop Time Complexity Annotated Notes | |||
7a. Brief about Best Case, Worst Case | |||
7b. More about Best Case, Worst Case | |||
7c. Questions on Algorithmic notations | |||
Annotated Notes Lecture 7 | |||
Practice Set Asymptotic Notations | |||
LIVE: Asymptotic Notations Practice Set Discussion -1 | |||
LIVE: Asymptotic Notations Practice Set Discussion-2 | |||
LIVE Session Asymptotic Notations Annotated Notes | |||
Stack and Queue | |||
8a. Introduction to Stack and Stack Permutations | |||
8b. Implementation of Stack using Array and Linked list | |||
9a. Introduction to Queue | |||
9b. Implementation of Queue using Array | |||
9c. Queue another Implementation using Array with F=R=0 | GATE 2012 | |||
9d. Implementing Queue using Linked List | |||
9e. Queue using two stacks | |||
10a. Stack using two queues | |||
10b. Balancing parentheses and Two Stacks in one array | |||
10c. Infix, Prefix & Postfix and Conversion to each other | |||
10d. Infix to Postfix using Stack and Postfix evaluation using Stack | |||
Stack and Queue Anotated Notes | |||
Practice Set Stack and Queue | |||
LIVE: Stack and Queue Practice Set Discussion-1 | |||
LIVE Stack and Queue Practice Set Discussion 2 | |||
LIVE Session Stack and Queue Annotated Notes | |||
Stacks and GATE PYQs - PYTHON | |||
Annotated Notes : Stacks and GATE PYQs Python | |||
Queues and GATE PYQs - PYTHON | |||
Annotated Notes : Queues and GATE PYQs Python | |||
Binary Trees | |||
11a. Introduction to Binary trees | |||
11b. Some questions and representation of tree | |||
11c. Questions on Binary trees | |||
11d. Questions on Binary trees 2 | |||
12a. Binary Tree Traversals | |||
12b. Binary tree construction using preorder postorder and inorder | |||
12c. Binary tree construction using preorder postorder and inorder 2 | |||
13a. Introduction to Binary Search Tree | |||
13b. Range Search in Binary Search Tree | GATE 2014 | GATE 2020 | |||
PDF: Range Search Binary Search Tree Animations (Stanford Slides) | |||
13c. BST Deletion | |||
14a. GATE 1996 Possible probe sequences | |||
14b. BST PYQs 1997, 2001, 06, 07, 16 | |||
14c. BST Time Complexities | |||
Annotated Notes Binary Tree and BST | |||
Practice Set Binary Trees and Binary Search Tree | |||
LIVE Binary Tree and Binary Search Tree Practice Set Discussion - 1 | |||
LIVE Binary Tree and Binary Search Tree Practice Set Discussion - 2 | |||
LIVE SessionBinary Tree and Binary Search Tree Annotated Notes | |||
Session - 1 : Binary Trees Questions & PYQs - PYTHON | |||
Annotated Notes : Session - 1 : Binary Trees Questions & PYQs - PYTHON | |||
Session - 2 : Binary Trees Questions & PYQs - PYTHON | |||
Annotated Notes : Session - 2 : Binary Trees and PYQs - PYTHON | |||
Hashing | |||
20a. Motivation to hashing and Direct address table | |||
20b. Introduction to hashing | |||
20c. Collision resolution techniques | |||
20d. Avg case performance chaining | |||
21a. Linear Probing | |||
21 b. Quadratic probing and Double hashing | |||
21c. Possible Number of probes | |||
22a. [Optional but please watch ] UnSucessfull search time open addressing | |||
22b. [Optional but please watch ] Successful search time open addressing | |||
23 a. Probability Background (video from Probability course) Bernoulli and Binomial RVs | |||
23b. Three popular expectation questions in hashing | |||
Hashing Anotated Notes | |||
Why Study Algorithms | |||
1a. Why Study Algorithms | |||
1b.An algorithm that changed history And Child's algorithm | |||
Time Complexity of Recursive Programs | |||
2a. Introduction to recurrence relations | |||
2b. Solving recurrence using Iteration Method | |||
2c. More Examples of Iteration Method | |||
3a. Solving recurrence using Tree Method | |||
3b. More Examples Tree Method | |||
3c. Even More Examples Tree Method | |||
3d. More Examples Tree Method | |||
4a. Masters Theorem Idea and examples | |||
4b. Examples On Master Theorem | |||
4c. Proof of Master Theorem | |||
5a. Generalised Master Theorem | |||
5b. [Optional but watch] Extended Master Theorem | |||
5c. Various Examples of Master Theorem | |||
5d. Problems that master theorem can not solve | |||
6a. Introduction to change to variable method | |||
6b. Examples on Change of Variable | |||
6c. Few more examples on change of variable method | |||
6d. Some More variation | |||
Annotated Notes Module 1 Solving Reccurance relation | |||
Divide and Conquer Algorithms (Part-1) | |||
7a. Introduction to Divide and Conquer Algorithm | |||
7b. Maximum of an array using D and C | |||
7c. Example 2- Sum of an array | |||
7d. Example 3- Search in an array | |||
7e. Example 4 - Dumb Sort | |||
A Note: Viewing Recurrence as Induction | |||
8a. Introduction to Merge Sort algorithm | |||
8b.Heart of Merge Sort- Merge Procedure | |||
8c. Merge Procedure -2 | |||
8d. Merge Sort Recursive tree with example | |||
8e. Merge Sort Analysis (Contd..) | |||
9a. Merge Sort questions -1 | |||
9b. Merge Sort questions -2 | |||
9c. More Questions on Merge sort | |||
10a. Iterative (or Bottom-up) Merge sort | |||
10b. GATE 1999 question on Bottom up Merge Sort | |||
10c. Time Complexity of Iterative Merge sort | |||
10d. [Optional and Skip] Implementation of Iterative Merge sort | |||
11a. Merging k sorted arrays Part 1 | |||
11b. Merging k sorted arrays Part 2 | |||
11d. Definition of Stable and In-place sorting | |||
[DELETED] In Place and Space Complexity | |||
Annotated Notes Divide and Conquer Algorithms (Part-1) | |||
Divide and Conquer - PYTHON | |||
Annotated : Divide and Conquer - PYTHON | |||
Merge Sort & Questions | |||
Annotated : Merge Sort & Questions | |||
Divide and Conquer Algorithms (Part-2) | |||
12a. Introduction to Quick Sort | |||
12b. Working Example of Quick Sort | |||
12c. Quick sort Analysis and Randomised quick sort | |||
12d. GATE 2014 question | |||
12e. Questions on quick sort | |||
12f. [Optional and Skip] Average case analysis of quick sort | |||
13a. The Select algorithm | |||
13b. Binary Search | |||
Annotated notes Divide and Conquer part 2 | |||
Quick Sort - PYTHON | |||
Annotated : Quick Sort - PYTHON | |||
Breadth First and Depth First search (BFS and DFS) | |||
14a. Introduction to Graphs | Adjacency List and Matrix | |||
14b. intuition for graph search methods | |||
14c. Introduction to DFS | |||
14d. DFS Implementation (Recursive and explicit stack) and Time complexity | |||
15a. DFS Parentheses Theorem | GATE 2006 | |||
15b. DFS Edge Classifications | |||
15c. Questions on DFS Edge Classification | |||
15d. [Optional] Back-edge and cycle question | |||
16a. DFS Application 1- Cycles in graph | |||
16b. GATE 2007 question on DAG finish time | |||
16c. DFS Application 2 Topological sort | GATE 2014 | |||
16d. DFS Application 3 Articulation Point | GATE 2021 | |||
17a. Introduction to BFS | |||
17b. BFS few Observations(Properties) and BFS Edge Classification | |||
17c. BFS Applications | |||
Annotated Notes DFS BFS | |||
Shortest Paths Algorithms (Greedy Algorithms) | |||
18a. Introduction to Greedy Algorithms | Introduction and Intuitive proof of Dijkstra | |||
18b. Dijkstra code and Working Example 1 | |||
18c. Dijkstra Working Example 2 | |||
18d. Dijkstra Working Example 3 | |||
19a. Dijkstra on negative weights | |||
19b. Video From DS Course: Priority Queues | |||
19c. Dijkstra Time Complexity | |||
19d. Dijkstra Time Complexity on more variant data structures | Dijkstra Demo | |||
20a. DAG Shortest Path | Shortest Path in Directed Acyclic Graph | |||
20b. Intuition Behind Bellman Ford Algorithm | |||
20c. Examples of Bellman Ford | Time complexity | |||
20d. Bellman ford proof and Early termination | |||
Annotated Notes Dijkstra, DAG Shortest Path Algo, and Bellman Ford | |||
Sorting Algorithms (selection, insertion, bubble, counting sort) | |||
21a .Bubble Sort | |||
21b .Insertion Sort | |||
21c .Bubble Sort and Insertion Sort | |||
21d. Selection Sort and Heap Sort | |||
21e. Decision Tree | |||
21f. [Optional] Counting Sort and Radix Sort | |||
Annotated Notes Sorting Algorithms | |||
Student Notes | |||
Algorithm GATE DA Student Written Notes | |||
Data Structures GATE DA Student Written Notes |
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