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Python Algorithms 2026 Masterclass: From Basics to Advanced
New
1 students

Python Algorithms 2026 Masterclass: From Basics to Advanced

Master Data Structures, Sorting, Graph Algorithms, DP & Backtracking – Ace Coding Interviews
New
Created byShayan Janati
Last updated 8/2026
English

What you'll learn

  • Big O Notation – analyze the time and space complexity of any algorithm
  • Recursion – master the art of functions that call themselves.
  • Sorting Algorithms – implement Bubble, Selection, Insertion, Merge, Quick, Heap, Counting, and Radix sorts.
  • Searching Algorithms – Linear and Binary Search, plus advanced searching techniques.
  • Data Structures – Linked Lists, Stacks, Queues, Hash Tables, Trees, Heaps, Tries, and Graphs.
  • Tree Algorithms – traversals (pre, in, post, level), Binary Search Trees, AVL Trees, and heaps
  • Graph Algorithms – BFS, DFS, Topological Sort, Dijkstra, Bellman‑Ford, Floyd‑Warshall, Prim, and Kruskal
  • Greedy Algorithms – Activity Selection and real‑world optimization problems
  • Backtracking – solve puzzles like N‑Queens and Sudoku systematically.
  • Dynamic Programming – master Fibonacci, 0/1 Knapsack, Longest Common Subsequence, Edit Distance, and Coin Change.
  • Algorithm Design Patterns – choose the right approach for any problem.
  • Problem‑Solving Mindset – think like a software engineer and break down complex challenges.

Course content

1 section • 40 lectures • 5h 6m total length

  • Big O Notation & Algorithm Efficiency6:32
  • Recursion – The Backbone of Algorithms6:59
  • Bubble Sort – The Simplest Sort7:28
  • Selection Sort – Picking the Minimum6:21
  • Insertion Sort – Building Sorted Arrays7:17
  • Merge Sort – Divide & Conquer8:26
  • Quick Sort – In‑Place Partitioning7:37
  • Heap Sort – Using Priority Queues8:40
  • Counting & Radix Sorts – Linear Time9:02
  • Linear & Binary Search.7:19
  • Two‑Pointers Technique10:32
  • Sliding Window – Optimising Subarrays9:53
  • Singly Linked Lists – Fundamentals7:42
  • Doubly & Circular Linked Lists8:29
  • Stacks & Queues – LIFO & FIFO7:51
  • Hash Tables – Dictionaries Under the Hood7:34
  • Binary Trees – Terminology & Properties7:31
  • Tree Traversals – Pre, In, Post, Level9:10
  • Binary Search Trees – Operations9:10
  • Balanced BSTs – AVL Trees Intro9:09
  • Heaps – Priority Queue Implementation8:45
  • Tries – Efficient String Storage10:20
  • Graph Representations – Adjacency Matrix / List8:51
  • Breadth‑First Search (BFS)9:22
  • Depth‑First Search (DFS)9:39
  • Topological Sorting (Kahn's & DFS)8:45
  • Dijkstra's Shortest Path6:17
  • Bellman‑Ford – Handling Negative Weights8:04
  • Floyd‑Warshall – All‑Pairs Shortest Path8:21
  • Prim's Minimum Spanning Tree6:02
  • Kruskal's MST – Union‑Find5:33
  • Greedy Algorithms – Activity Selection6:07
  • Backtracking – N‑Queens Problem6:02
  • Backtracking – Sudoku Solver6:28
  • DP – Fibonacci & Memoisation5:17
  • DP – 0/1 Knapsack6:05
  • DP – Longest Common Subsequence6:49
  • DP – Edit Distance5:31
  • DP – Coin Change Problem5:29
  • Algorithm Design Patterns – Final Wrap‑Up5:46

Requirements

  • Basic Python Knowledge – you should be comfortable with variables, loops, functions, conditionals, and lists.
  • A Computer with Python 3.7+ – and a code editor or IDE (VS Code, PyCharm, or any text editor).

Description

Unlock the secrets of efficient coding with the Python Algorithms 2026 Masterclass – the ultimate guide to mastering algorithms and data structures in Python. In 40 carefully designed lectures, you'll go from complete beginner to algorithm expert, learning everything from Big O Notation to advanced Dynamic Programming and Graph Algorithms.

This course is built for hands‑on learning. Each lecture is delivered in Jupyter Notebook with a structured 5‑cell format: concept introduction, live coding, in‑depth explanation, a challenging exercise, and a detailed solution. You won't just watch – you'll code alongside me and build real problem‑solving skills that stick.

We start with the absolute fundamentals: Recursion, Big O Notation, and the classic sorting algorithms (Bubble, Merge, Quick, Heap, and more). Then we dive into essential data structures like Linked Lists, Stacks, Queues, Hash Tables, Trees, and Heaps – understanding not just how to use them, but how they work under the hood.

Next, we explore graphs and their algorithms: BFS, DFS, Topological Sort, Dijkstra's Shortest Path, Bellman‑Ford, Floyd‑Warshall, and Minimum Spanning Trees (Prim and Kruskal). You'll learn to solve real‑world problems like pathfinding and network optimization.

Finally, we tackle the most challenging topics: Backtracking (N‑Queens, Sudoku) and Dynamic Programming (Fibonacci, Knapsack, LCS, Edit Distance, Coin Change). You'll develop the intuition to recognize when and how to apply these powerful techniques.

By the end of this course, you'll have a deep understanding of algorithms, ace coding interviews, and write efficient, optimized Python code. Whether you're a student, a self‑taught developer, or a professional preparing for interviews, this course gives you the skills and confidence you need.

No prior algorithm experience is required – just basic Python knowledge and a passion for learning. Join thousands of students and become an algorithmic problem‑solving master today!


Who this course is for:

  • Aspiring Software Engineers – who want to ace coding interviews at top tech companies.
  • Computer Science Students – looking to deepen their understanding of algorithms and data structures.
  • Self‑Taught Programmers – who want to build a strong theoretical foundation to complement their practical skills
  • Career Changers – transitioning into tech roles that require algorithmic thinking
  • Competitive Programmers – preparing for coding competitions and challenges.
  • Anyone Who Loves Problem‑Solving – who enjoys tackling puzzles and writing elegant, efficient code.

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