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Python Algorithms Masterclass

Algorithms with Python for efficient coding and problem-solving skills

Created byEMCAST Inc.

Last updated 9/2024

English

What you'll learn

You can choose the right algorithm for various problem situations and solve them efficiently.
You can solve problems systematically by learning algorithm design techniques such as divide-and-conquer, dynamic programming, and more.
You can analyze the time and space complexity of your algorithms to improve their performance.
You can implement various algorithms and data structures using Python, and apply them to real-world problems.
You can understand the basic concepts of key data structures, such as stacks, queues, trees, and hash tables, and apply them to solve problems.
You can learn efficient search and sort algorithms, including binary search, bubble sort, selective sort, quick sort, and merge sort.
You can solve graph-based problems by learning Depth-First Search (DFS), Breadth-First Search (BFS), Minimum Height Tree, Shortest Path Algorithm, and more.
You can solve mathematical algorithmic problems such as prime number checking, divisor calculation, prime factorization, greatest common factor, least common m
You can use dynamic programming techniques to solve optimization problems and write code efficiently.
You can solve algorithmic problems of different difficulty levels, gaining hands-on problem-solving experience.

Course content

87 sections • 393 lectures • 33h 50m total length

Introduction to Problem-Solving2:00
Step 1: Check background knowledge for problem solving4:05
Check background knowledge for problem solving by understanding data structures and algorithms, and learn to write efficient Python code for matrix addition of A and B to obtain C.
Step 2: Create solution8:06
Learning Summary0:21
Master problem solving concepts and apply coding to solve a given problem using Python standard input and output for data handling during the process.

Introduction to Problem-Solving1:38
Compare two algorithms for efficiently computing the sum of natural numbers up to n, and implement a solution through a two-step problem-solving approach.
Step 1: Check background knowledge for problem solving3:36
Step 2: Create solution4:00
Create a Python algorithm to calculate the sum of natural numbers up to n using a for loop, then compare with the efficient n(n+1)/2 formula in the solve function.
Learning Summary0:19

Introduction to Problem-Solving2:12
Explore asymptotic analysis and problem-solving methods for algorithms by locating the maximum value and its index in a list, then writing and analyzing code across two steps.
Step 1: Check background knowledge for problem solving16:44
Learn to evaluate algorithm performance using big O notation, time and space complexity, and asymptotic analysis to select efficient solutions.
Step 2: Create solution3:00
Learning Summary0:16
Explore time analysis tools and the big O notation used in the asymptotic analysis of algorithms.

Introduction to Problem-Solving2:05
Step 1: Check background knowledge for problem solving2:02
Check your background knowledge by understanding divisors as numbers that divide a positive integer with zero remainder, then count them and implement a Python algorithm to compute the divisor count.
Quiz
Quiz solution0:39
Step 2: Create solution11:51
Implement a Python algorithm to count divisors of a positive integer by checking remainders when dividing by each number from 1 to n, following a flowchart and a solve function.
Step 3: Improve solution14:51
Improve the divisor-counting method by checking up to the square root of n, using divisor pairs and adjusting for perfect squares to achieve O(sqrt(n)) time.
Learning Summary0:38
Identify more efficient strategies for finding the number of divisors and how algorithms approach the same answer, and prepare for learning prime numbers in the next lecture.

Introduction to Problem-Solving2:03
Step 1: Check background knowledge for problem solving3:10
Master the basics of problem solving by confirming the prime number definition and divisors, then apply a divisor-based approach to primality in Python algorithms.
Quiz
Quiz solution3:24
Determine if numbers are prime by counting divisors from 2 to n-1 and noting two divisors. Use examples 6, 5, 27, 54, 69, 95, and 97.
Step 2: Create solution14:55
Step 3: Improve solution8:53
Learning Summary0:37
Explore prime number determination and compare algorithms to reveal more efficient strategies for the same answer. Study permutations and factorials as essential tools for solving problems in the next lecture.

Introduction to Problem-Solving2:43
Step 1: Check background knowledge for problem solving4:09
Check your background knowledge for problem solving by understanding factorial as a recursive definition, and identify base and recursive conditions to ensure termination.
Step 2: Create solution12:20
Design and implement a recursive factorial solution in Python, using base and recursive cases, pseudocode planning, and recursion limit adjustments to handle large inputs.
Step 3: Improve solution9:06
Explore recursion and iteration, convert recursive solutions to loops, and implement an iterative factorial in Python while planning with pseudocode and flowcharts to optimize speed and memory.
Learning Summary0:59
Explore recursion, detailing how recursive functions use base and recursive conditions, how iteration can solve recursive structures, and prepare for the next lesson on the greatest common divisor.

Introduction to Problem-Solving2:14
Learn the Euclidean algorithm to compute the greatest common divisor of two non-negative integers. Apply a two-step process: understand the background and implement the recursive gcd solution.
Step 1: Check background knowledge for problem solving5:22
Step 2: Create solution6:23
Implement the Euclidean algorithm in Python to compute the greatest common divisor using a recursive gcd function, starting from inputs n and m, with pseudocode and flowcharts for planning.
Step 3: Improve solution5:39
Improve the Euclidean algorithm for gcd by converting recursion to iteration, and implement it in Python using an explicit while loop after designing pseudocode or a flowchart.
Learning Summary0:30

Introduction to Problem-Solving1:46
Step 1: Check background knowledge for problem solving5:00
Check background knowledge for problem solving by exploring the Collatz sequence, its even/odd rule, and the recursive definition, noting the exit condition and the hailstone visualization.
Step 2: Create solution6:36
Learning Summary0:22
Invest in understanding the Collatz conjecture and implement a recursive Python function to compute the Collatz sequence from its recursive definition of the Collett sequence.

Introduction to Problem-Solving3:50
Explore the Tower of Hanoi problem, its three pegs and n disks, and learn a recursive Python solution that outputs the minimal move sequence from A to C.
Step 1: Check background knowledge for problem solving7:48
Learn how to solve the Tower of Hanoi with recursion, using the base case of one disk and expanding to n disks with source, intermediate, and destination pegs.
Step 2: Create solution9:55
Learning Summary0:22

Requirements

You need computer and internet skills sufficient to install the programs required for the course.
It is recommended that you have a basic understanding of Python syntax, such as variables, conditional statements, loops, and functions.
You should have an understanding of basic math concepts and be able to challenge yourself to solve mathematical problems.
You should have a good understanding of basic math concepts (prime numbers, divisors, greatest common divisor, least common multiple, and more) and be able to challenge yourself to solve mathematical problems.

Description

"Python Algorithms Masterclass" is a fundamental course for any developer who wants to solve complex problems effectively.

In this course, you will systematically learn various algorithmic problems and how to implement them efficiently using Python so that you can apply them immediately in your work.

Develop your ability to combine mathematical and computational thinking to design better performing algorithms, and even master how to analyze performance to choose the optimal solution!

The "Python Algorithms Masterclass" course is the best choice for anyone who wants to learn how to solve problems quickly and accurately in the real world.

In the “Python Algorithms Masterclass” course,

(POINT 1) You will learn a variety of topics from basic concepts of problem-solving to advanced algorithms.

(POINT 2) You will learn theory and practice simultaneously through various practical problems, and gain experience in implementing them yourself using Python.

(POINT 3) You will learn how to understand and analyze the time complexity and space complexity of algorithms to improve their performance.

(POINT 4) You will challenge yourself with difficult problems by gradually increasing the difficulty level from basic to advanced.

Introduction to Problem-Solving: Learn the basic concept of problem-solving, how to input and output data, and write problem-solving strategies in code.
Recursion: Learn the basic concept of recursion and efficiently solve problems such as Euclidean algorithm to find the greatest common divisor, Collatz conjecture, Tower of Hanoi, permutations, Fibonacci sequence, and more.
Sort: Learn the basic concept of sorting algorithms and efficiently solve problems such as word sorting, coordinate sorting, median elimination, determining anagrams, bubble sorting, insertion sorting, and more.
Sequential Search: Learn the basic concept of sequential search and efficiently solve problems such as locating elements, Palindrome numbers, Eratosthenes' sieve, building triangles, and more.
Binary Search: Learn the basic concept of binary seacrch and efficiently solve problems such as locating elements, playing twenty questions game, the batonic sequence, cutting trees, and more.
Stack: Learn the basic concept of a stack and efficiently solve problems such as visible sticks, postfix operations and transformations, stacked sequences, and more.
Queue and Heap: Learn the concept of a queue and a deque and efficiently solve problems such as changing median, Josephus, and more.
Hash Tables: Learn the basic concepts of a hash table and efficiently solve problems such as sums of two elements, Roman numeral conversion, and more.
Divide-and-Conquer 1: Learn the basic concept of divide and conquer and efficiently solve problems involving power of integer and matrice, quadtree, and more.
Divide-and-Conquer 2: Efficiently solve problems such as dividing and sorting a given list by a pivot.
Dynamic Programming 1: Learn the basic concept of dynamic programming and efficiently solve problems such as Fibonacci numbers, 2 x N Tiling, stair climbing, making it 1, finding the optimal value and the optimal solution simultaneously, and more.
Dynamic Programming 2: Efficiently solve problems such as how to compute binomial coefficients, paths on triangles, sequence of final common parts, and more.
Greedy approach: Learn the basic concept of the greedy approach and efficiently solve problems such as coin exchange, room assignment, and more.
DFS and BFS: Learn the basic concept of Depth-First Search and Breadth-First Search and efficiently solve problems such as a number of islands, maze navigation, and more.
Backtracking: Learn the basic concept of backtracking and use backtracking to efficiently solve problems such as N-Queens, and more.
Tree: Learn the basic concept of trees and binary trees and efficiently solve problems such as traversal of binary trees, reconstruction of binary trees, and more.
Graph: Learn the basic concept of graphs and efficiently solve problems such as Union-Find and finding the minimum height tree using the Kruskal algorithm, and more.
Shortest Paths: Learn the Daixtra and Floyd algorithms and efficiently solve problems such as finding the cost of the shortest path and finding the shortest path of all pairs.
Knapsack Problem: Efficiently solve problems such as the divisible knapsack problem using Griddy strategies and dynamic programming.
Computational Complexity: Learn computational complexity through the selection problem, and understand the basic concept of NP-Theory, NP-Complete, and NP-Hard problems.

Who this course is for:

A learner who wants to learn Python syntax, algorithms, and data structures in a structured way.
A computer science and software major who wants to gain diverse problem-solving experience.
A beginner who has a basic knowledge of algorithms and data structures or who is interested in learning them.
A developer who wants to develop efficient problem-solving skills in their projects and solve complex algorithmic problems in Python.
A learner who wants to learn strategies and efficient coding methods for solving algorithmic problems in IT companies, programming competitions, and more.
A learner who wants to develop computational thinking and problem-solving skills.

Python Algorithms Masterclass

What you'll learn

Explore related topics

Course content

What is Problem Solving?4 lectures • 15min

Efficiency of Problem Solving4 lectures • 10min

Asymptotic Analysis4 lectures • 22min

Number of divisors7 lectures • 32min

Prime number Determination7 lectures • 33min

Factorial5 lectures • 29min

Greatest Common Divisor5 lectures • 20min

Collatz Sequence4 lectures • 14min

Tower of Hanoi4 lectures • 22min

Permutation4 lectures • 17min

Requirements

Description

Who this course is for: