Algorithms in Python : Design Techniques And Approach
Algorithms in Python : Design Techniques And Approach, available at $59.99, has an average rating of 4.65, with 158 lectures, based on 19 reviews, and has 356 subscribers.
You will learn about Algorithms In Python Algorithms Design Techniques Data Structures Tress Graphs Dynamic Programming , Recursion, Backtracking This course is ideal for individuals who are Want to Learn to Design Algorithms It is particularly useful for Want to Learn to Design Algorithms.
Enroll now: Algorithms in Python : Design Techniques And Approach
Summary
Title: Algorithms in Python : Design Techniques And Approach
Price: $59.99
Average Rating: 4.65
Number of Lectures: 158
Number of Published Lectures: 158
Number of Curriculum Items: 158
Number of Published Curriculum Objects: 158
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
- Algorithms In Python
- Algorithms Design Techniques
- Data Structures
- Tress
- Graphs
- Dynamic Programming , Recursion, Backtracking
Who Should Attend
- Want to Learn to Design Algorithms
Target Audiences
- Want to Learn to Design Algorithms
Algorithm Design Techniques: Live problem-solving in Python
Algorithms are everywhere. One great algorithm applied sensibly can result in a System like GOOGLE!
Completer scientists have worked for 100s of years and derived some of the techniques that can be applied to write and design algorithms.
So Why to reinvent the wheel ??
Let’s go through some of the most famous algorithm design techniques in this course.
Once you will come to know these design techniques It will become very easy for you to approach a problem by identifying which technique to apply to solve that correctly and efficiently.
0. Complexity analysis
1. Recursion is the base of any algorithm design
2. Backtracking
3. Divide and Conquer
4. Greedy algorithms
5. Dynamic programming
6. Trees
7. Graphs
And WE WILL WRITE THE CODE LINE BY LINE IN PYTHON !!
By the end of this course –
1. You will understand how to design algorithms
2. A lot of coding practice and design live problems in Java
3. Algorithm Complexity analysis
AND
If you are preparing for your coding Interview or doing competitive programming This course will be a big help for you.
THRILLED? I welcome you to the course and I am sure this will be fun!!
If it does not – It comes with a 30 Days money-back guarantee so don’t think twice to give it a shot.
Happy Learning
Basics>Strong;
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: Course Resources
Chapter 2: Introduction to algorithms
Lecture 1: Introduction to algorithms
Chapter 3: Complexity Analysis
Lecture 1: Section Introduction
Lecture 2: Complexity Analysis – part 1
Lecture 3: Complexity Analysis – part 2
Lecture 4: Summary
Chapter 4: Recurrence Relation
Lecture 1: Section Introduction
Lecture 2: Recurrence Relation
Lecture 3: Solving Recurrence Relation
Lecture 4: Master's Theorem
Lecture 5: Summary
Chapter 5: Thinking Recursively
Lecture 1: Section Introduction
Lecture 2: Recursion
Lecture 3: Identification
Lecture 4: Approaching the solution
Lecture 5: Problem 01 : FindingSubstrings – Logic
Lecture 6: Problem 01 : FindingSubstrings – Live Code Python
Lecture 7: Problem 01 : FindingSubstrings – Complexity Analysis
Lecture 8: Problem 02 : Tower Of Hanoi
Lecture 9: Problem 02 : Tower of Hanoi – Lets code in Python
Lecture 10: Problem 02 : Tower of Hanoi – Complexity Analysis
Lecture 11: Problem 03 : Array Product Sum – Logic
Lecture 12: Problem 03 : Array Product Sum – Live Code Python
Lecture 13: Problem 03 : Array Product Sum – Complexity Analysis
Lecture 14: Problem 04 : Binary Subtree – Logic
Lecture 15: Problem 04 : Binary Subtree : Live code
Lecture 16: Problem 04 : Binary Subtree – Complexity Analysis
Lecture 17: Why and Why not Recursion
Lecture 18: Types Of Recursion
Lecture 19: Tail Recursion
Lecture 20: Summary
Chapter 6: Backtracking
Lecture 1: Section Introduction
Lecture 2: Introduction to Backtracking
Lecture 3: Identification
Lecture 4: Approaching The Solution
Lecture 5: Problem 01 : Rat In Maze – Logic
Lecture 6: Problem 01 : Rat In Maze – Code
Lecture 7: Problem 01 : Rat In Maze – Complexity Analysis
Lecture 8: Problem 02 : NQueen – Logic
Lecture 9: Problem 02 : N-Queens – Live Code in Python
Lecture 10: Problem 02 : N-Queens – Complexity Analysis
Lecture 11: Problem 03 : Knight's Tour Problem – Logic
Lecture 12: Problem 03 : Knight's Tour Problem – Live Code in Python
Lecture 13: Problem 03 : Knight Tour Problem – Complexity Analysis
Lecture 14: Problem 04 : Boggle | Word Search – Logic
Lecture 15: Problem 04 : Boggle | Word Search – Live Code in Python
Lecture 16: Problem 04 : Boggle | Word Search – Complexity Analysis
Lecture 17: Section Summary
Chapter 7: Divide and Conquer
Lecture 1: Section Introduction
Lecture 2: Introduction To Divide And Conquer
Lecture 3: Identification and Approaching
Lecture 4: Problem 01 : MergeSort – Logic
Lecture 5: Problem 01 : MergeSort – Live Python Code
Lecture 6: Problem 01 : MergeSort – Complexity Analysis
Lecture 7: Problem 02 : QuickSort – Logic
Lecture 8: Problem 02 : QuickSort – Live Python Code
Lecture 9: Problem 02 : QuickSort – Complexity Analysis
Lecture 10: Problem 03 : Median Of Medians – Logic
Lecture 11: Problem 03 : Median Of Medians – Live Python Code
Lecture 12: Section Summary
Chapter 8: Greedy Technique
Lecture 1: Section Introduction
Lecture 2: Introduction to Greedy
Lecture 3: Identification & Approaching the Solution
Lecture 4: Problem 01 : Fractional Knapsack – Logic
Lecture 5: Problem 01 : Fractional Knapsack – Live Code Python
Lecture 6: Problem 01 : Fractional Knapsack – Complexity Analysis
Lecture 7: Problem 02 : IntervalScheduling – Logic
Lecture 8: Problem 02 : IntervalScheduling – Live Code Python
Lecture 9: Problem 02 : IntervalScheduling – Complexity Analysis
Lecture 10: Problem 03 : Huffman Code – Logic
Lecture 11: Problem 03 : Huffman Code – Live Code
Lecture 12: Problem 03 : Huffman Code – Complexity Analysis
Lecture 13: Problem 04 : Dijkstra – Logic
Lecture 14: Problem 04 : Dijkstra Logic – Live Code Python
Lecture 15: Problem 04 : Dijkstra – Complexity Analysis
Lecture 16: Summary
Chapter 9: Dynamic Programming
Lecture 1: Section Introduction
Lecture 2: Introduction to Dynamic Programming
Lecture 3: Identification
Lecture 4: Compare DP, D&C and Greedy
Lecture 5: Approaching the Solution
Lecture 6: Example 01 : Staircase Problem Explanation & Live Code
Lecture 7: Example 01 : Staircase Problem Complexity Analysis
Lecture 8: Example 02 – 0/1 Knapsack Explanation & Live code
Lecture 9: Example 02 – 0/1 Knapsack Complexity Analysis
Lecture 10: Example 03 – Coin Change Problem Theory and Code
Lecture 11: Example 03 – Coin Change Problem Complexity Analysis
Lecture 12: Example 04 : Longest Decreasing Subsequence Explanation And Code
Lecture 13: Example 04 : Longest Decreasing Subsequence | Complexity Analysis
Lecture 14: Example 05 : Levenshtein problem
Instructors
-
Basics Strong
Team of technocrats and Programming lovers
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 0 votes
- 3 stars: 2 votes
- 4 stars: 7 votes
- 5 stars: 10 votes
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