Dynamic Programming, Backtracking, Knowledge Constructions, BigO,Query Patterns,In depth Explanations. Get the job you need !
What you’ll study
Dynamic Programming, Backtracking Strategies
Widespread Knowledge Constructions corresponding to Arrays, Hash Desk,Linked Record,Binary timber,Graphs and so on.
Time and Area Complexity of Algorithms, Detailed Dialogue of Logic to resolve questions
Actual Coding Interview Questions from Google, Meta,Amazon,Netflix ,Microsoft and so on.
Enhance your Downside fixing expertise
Description
In regards to the Course:
Welcome to the Algorithms and Knowledge Constructions Coding Interview Bootcamp with Python!
The first objective of this course is to organize you for coding interviews at high tech firms. By tackling one drawback at a time and understanding its answer, you’ll accumulate a wide range of instruments and strategies for conquering any coding interview.
Day by day Coding Challenges:
The course is structured round every day coding challenges. Constant follow will equip you with the abilities required to ace coding interviews. For the following 40 days decide to your self to follow atleast 2 coding interview questions on a regular basis. You don’t want any setup for this because the every day coding drawback challenges may be solved within the coding atmosphere supplied by Udemy. The course will mechanically observe your progress and also you simply must spend your time making precise progress on a regular basis.
Subjects Coated:
We begin from the fundamentals with Large O evaluation, then transfer on to crucial algorithmic strategies corresponding to Recursion, Backtracking and Dynamic Programming Patters. After this we transfer to cowl frequent knowledge buildings, and talk about actual issues requested in interviews at tech giants corresponding to Google, Meta, Amazon, Netflix, Apple, and Microsoft.
For every query, we’ll:
- Focus on the optimum method
- Clarify time and house complexity
- Code the answer in Python (you may comply with alongside in your most popular language)
Further Sources:
The course consists of downloadable assets, motivational trackers, and cheat sheets.
Course Define:
- Day 1: Arrays, Large O, Sorted Squared Array, Monotonic Array
- Day 2:Recursion,k-th image in Grammar,Josephus drawback
- Day 3:Recursion, Tower of Hanoi, Energy Sum
- Day 4:Backtracking, Permutations, Permutations 2
- Day 5:Backtracking, Subsets, Subsets 2
- Day 6:Backtracking, Mixtures, Mixtures Sum 1
- Day 7:Backtracking,Mixtures Sum 2,Mixtures Sum 3
- Day 8:Backtracking,Sudoku Solver, N Queens
- Day 9:Dynamic Programming, Fibonacci, Climbing Stairs
- Day 10:Dynamic Programming, Min Price Climbing Stairs, Tribonacci
- Day 11:Dynamic Programming, 01 Knapsack, Unbounded Knapsack
- Day 12:Dynamic Programming, Goal Sum, Partition Equal Subset Sum
- Day 13:Dynamic Programming, LCS, Edit Distance
- Day 14:Dynamic Programming, LIS, Max Size of Pair Chain, Russian Doll Envelopes
- Day 15:Dynamic Programming, Palindromic Substrings, Longest Palindromic Substring, Longest Palindromic Subsequence
- Day 16:Dynamic Programming, Palindrome Partitioning, Palindrome Partitioning 2
- Day 17:Dynamic Programming, Phrase Break, Matrix Chain Multiplication
- Day 18:Dynamic Programming, Kadane’s algorithm – Max Subarray, Most Product Subarray
- Day 19: Arrays, Rotate Array, Container with Most Water
- Day 20: Hash Tables, Two Sum, Isomorphic Strings
- Day 21: Strings, Non-Repeating Character, Palindrome
- Day 22: Strings, Longest Distinctive Substring, Group Anagrams
- Day 23: Looking, Binary Search, Search in Rotated Sorted Array
- Day 24: Looking, Discover First and Final Place, Search in 2D Array
- Day 25: Sorting, Bubble Type, Insertion Type
- Day 26: Sorting, Choice Type, Merge Type
- Day 27: Sorting, Fast Type, Radix Type
- Day 28: Singly Linked Lists, Assemble SLL, Delete Duplicates
- Day 29: Singly Linked Lists, Reverse SLL, Cycle Detection
- Day 30: Singly Linked Lists, Discover Duplicate, Add 2 Numbers
- Day 31: Doubly Linked Lists, DLL Take away Insert, DLL Take away All
- Day 32: Stacks, Assemble Stack, Reverse Polish Notation
- Day 33: Queues, Assemble Queue, Implement Queue with Stack
- Day 34: Binary Timber, Assemble BST, Traversal Strategies
- Day 35: Binary Timber, Degree Order Traversal, Left/Proper View
- Day 36: Binary Timber, Invert Tree, Diameter of Tree
- Day 37: Binary Timber, Convert Sorted Array to BST, Validate BST
- Day 38: Heaps, Max Heap, Min Precedence Queue
- Day 39: Graphs, BFS, DFS
- Day 40: Graphs, Variety of Linked Parts, Topological Type
My confidence in your satisfaction with this course is so excessive that we provide a whole money-back assure for 30 days! Thus, it’s a completely risk-free alternative. Register right now, dealing with ZERO danger and standing to achieve EVERYTHING.
So what are you ready for? Be a part of one of the best Python Knowledge Constructions & Algorithms Bootcamp on Udemy.
I’m wanting to see you within the course.
Let’s kick issues off! 
Jackson
English
language
Content material
Day 1: Arrays Knowledge Constructions and Algorithms
What you’re going to get from this course
Welcome! Tips on how to make finest use of this course (Please Watch)
Day 1 Targets
Introduction to Knowledge Constructions
Introduction to Large O, Time Complexity
2 Asymptotic Evaluation and Large O
Large O Area Complexity
Large O Logarithm
Arrays: Knowledge Constructions Crash Course
Quiz: Arrays
CODING EXERCISES
CODING INTERVIEW Q1 (Straightforward): Sorted Squared Array
Coding Train: Sorted Squared Array
Methodology 1, Large O Evaluation
Python Code – Methodology 1
Methodology 2
Python Code – Methodology 2
CODING INTERVIEW Q2 (Straightforward): Monotonic Array
Coding Train: Monotonic Array
Methodology and Large O evaluation
Python Code – Monotonic Array
Day 2: Recursion
Day 2 Targets
Recursion Fundamentals
Recursive Leap of Religion
Visualising Recursion
Recursion vs Iteration
Methods to put in writing Base situation
Recurrence relation
Tips on how to Remedy Recursion Questions
Recursion Approaches – 0 to N and N to 0
Recursion is in every single place
Complexity Evaluation of Recursive Options
Quiz: Recursion
CODING INTERVIEW QUESTION (Medium): k-th image in Grammar
Coding Train (k-th image in Grammar)
Strategy(k-th image in Grammar)
Pseudocode (k-th image in Grammar)
Python Code
Complexity Evaluation(k-th image in Grammar)
Python Resolution (k-th image in Grammar)
CODING INTERVIEW QUESTION (Medium): Josephus drawback
Coding Train: Josephus drawback
Strategy 1
Pseudocode
Complexity Evaluation
Python Resolution 1: Josephus drawback Methodology 1
Strategy 2
Pseudocode
Complexity Evaluation
Python Resolution 2 : Josephus drawback Methodology 2
Strategy 3
Complexity Evaluation
Python Resolution 3 : Josephus drawback Methodology 3
Day 3: Recursion Continued
Day 3 Targets
CODING INTERVIEW QUESTION (Medium): Tower of Hanoi
Coding Train(Tower of Hanoi)
Figuring out that wew can use Recursion
Strategy
Recursion Tree
Python Resolution : Tower of Hanoi
Complexity Evaluation : Tower of Hanoi
CODING INTERVIEW QUESTION(Medium): Energy Sum
Coding Train: Energy Sum
Methodology and Large O Evaluation
Python Resolution: Energy Sum
Day 4: Backtracking
Day 4 Targets
What’s Backtracking
How is it completely different from Recursion ?
How does Backtracking work ?
Cross by reference / change inplace
Blueprint to resolve questions utilizing Backtracking
Establish when to make use of Backtracking
Quiz: Backtracking
CODING INTERVIEW QUESTION (Medium): Permutations
Coding Train ( Permutations)
Strategy
Pseudocode
Python Resolution : Permutations
Complexity Evaluation
CODING INTERVIEW QUESTION(Medium): Permutations 2
Permutations 2
Strategy
Pseudocode
Python Code: Permutations 2
Complexity Evaluation : Permutations 2
Day 5: Backtracking
Day 5 Targets
CODING INTERVIEW QUESTION(Medium): Subsets
Subsets
Methodology
Subsets – Comparability with Backtracking Blueprint
Subsets – Complexity Evaluation
Python Code – Subsets
CODING INTERVIEW QUESTION(Medium): Subsets 2
Coding Train: Subsets 2
Strategy
Python Code: Subsets 2
Subsets 2: Complexity Evaluation
Day 6: Backtracking
Day 6 Targets
CODING INTERVIEW QUESTION(Medium): Mixtures
Coding Train: Mixtures
Strategy
Mixtures : Complexity Evaluation
Python Code : Mixtures
Mixtures: Optimisation
Python Code: Mixtures with Optimisation
CODING INTERVIEW QUESTION ( Medium) : Mixtures Sum 1
Mixtures Sum 1
Day 7: Backtracking
Day 7 Targets
CODING INTERVIEW QUESTION (Medium): Mixtures Sum 2
Coding Train: Mixtures Sum 2
CODING INTERVIEW QUESTION ( Medium) : Mixtures Sum 3
Coding Train: Mixtures Sum 3
Day 8: Backtracking
Day 8 Targets
CODING INTERVIEW QUESTION(Onerous) : Sudoku Solver
Sudoku Solver
Strategy
Pseudocode
isValid verify for Sudoku Solver
Python Code : Sudoku Solver
Complexity Evaluation
One other method – Sudoku Solver ( Python Code)
CODING INTERVIEW QUESTION(Onerous): N Queen
Coding Train: N Queen
Strategy
Pseudocode
Python Code: N Queen
Complexity Evaluation
Day 9: Dynamic Programming
Day 9 Targets
Introduction to Dynamic Programming (DP)
Dynamic Programming – Patterns
Strategy to resolve DP(Dynamic Programming) Questions
Why writing the Recursive answer helps to put in writing the Backside up method
Figuring out Dynamic Programming Questions
Quiz: Dynamic Programming
CODING INTERVIEW QUESTION(Straightforward): Fibonacci
Coding Train: Fibonacci
Approaches
Strategy 1: Recursion
Complexity Evaluation: Strategy 1 – Recursion
Python Code – Recursion
Strategy 2: Memoization
Complexity Evaluation : Strategy 2 – Memoization
Python Code: Strategy 2 – Memoization
Strategy 3: Tabulation
Complexity Evaluation: Strategy 3 – Tabulation
Python Code: Strategy 3 – Tabulation
Strategy 4: Area Optimised Tabulation + Complexity Evaluation
Python Code: Strategy 4 -Area Optimised Tabulation + Complexity Evaluation
CODING INTERVIEW QUESTION(Straightforward): Climbing Stairs
Coding Train: Climbing Stairs
Day 10: Dynamic Programming Sort – Fibonacci
Day 10 Targets
CODING INTERVIEW QUESTION(Straightforward): Min Price Climbing Stairs
Coding Train: Minimal Price Climbing Stairs
CODING INTERVIEW QUESTION(Straightforward): Tribonacci
Coding Train: Tribonacci
Day 11: Dynamic Programming Sort – Knapsack
Day 11 Targets
CODING INTERVIEW QUESTION(Medium): 0/1 Knapsack
Coding Train: 01 Knapsack
Strategy 1: Recursion
Recursive Strategy: Pseudocode
Recursive Strategy: Complexity Evaluation
Python Code : Recursive Strategy
Strategy 2: Memoization
Memoization: Pseudocode
Python Code: Memoization
Memoization: Complexity Evaluation
Strategy 3: Tabulation
Python Code: Tabulation
Tabulation: Complexity Evaluation
Strategy 4: Area Optimised Tabulation Strategy
Python Code: Area Optimised Tabulation
Area Optimised Tabulation Strategy: Complexity Evaluation
CODING INTERVIEW QUESTION(Medium): Unbounded Knapsack
Coding Train: Unbounded Knapsack
Day 12: Dynamic Programming Sort – Knapsack
Day 12 Targets
CODING INTERVIEW QUESTION(Medium): Goal Sum
Coding Train: Goal Sum
CODING INTERVIEW QUESTION(Medium): Partition Equal Subset Sum
Coding Train: Partition Equal Subset Sum
Day 13: Dynamic Programming Sort – LCS ( Longest Widespread Subsequence)
Day 13 Targets
CODING INTERVIEW QUESTION(Medium): LCS
Coding Train: LCS ( Longest Widespread Subsequence)
Strategy 1: Recursion
Pseudocode
Recursion Tree and Complexity Evaluation
Python Code: LCS
Strategy 2: Memoization
Python Code: Memoization – LCS
Strategy 3: Tabulation
Tabulation: Complexity Evaluation
Python Code : Tabulation – LCS
Strategy 4: Area Optimised Tabulation – LCS
Python Code : Area Optimised Tabulation – LCS
CODING INTERVIEW QUESTION(Medium): Edit Distance
Coding Train: Edit Distance
Figuring out this as an LCS Sort Query
Strategy 1: Recursion
Pseudocode
Recursion: Complexity Evaluation
Python Code: Recursive Strategy(Edit Distance)
Strategy 2: Memoization
Python Code: Memoization(Edit Distance)
Strategy 3: Tabulation
Tabulation: Complexity Evaluation
Python Code: Tabulation (Edit Distance)
Strategy 4: Area Optimised Tabulation
Python Code: Area Optimised Tabulation ( Edit Distance)
Day 14: Dynamic Programming Sort – LIS ( Longest Rising Subsequence)
Day 14 Targets
CODING INTERVIEW QUESTION(Medium): Longest Rising Subsequence (LIS)
Coding Train: LIS
Strategy 1: Recursion – LIS
Recursion Tree
Complexity Evaluation – Recursion – LIS
Python Code – Recursion – LIS
Strategy 2: Memoization
Complexity Evaluation – Memoization
Python Code – Memoization – LIS
Strategy 3: Tabulation – utilizing a 2D dp array
Dry run
Complexity Evaluation – Tabulation – utilizing a 2D dp array
Python Code – Tabulation utilizing a 2D dp array – LIS
Strategy 4: Tabulation – utilizing a 1D dp array
Dry run
Complexity Evaluation- Tabulation – utilizing a 1D dp array
Python Code-Tabulation – utilizing a 1D dp array
Strategy 5: utilizing Binary Search – LIS
Half 1 – Strategy 5: utilizing Binary Search – LIS
Half 2 – Strategy 5: utilizing Binary Search – LIS
Binary Seek for this query ( refer Binary Search part for extra particulars)
Complexity Evaluation – Strategy 5: utilizing Binary Search – LIS
Python Code – Strategy 5: utilizing Binary Search – LIS
CODING INTERVIEW QUESTION(Medium): Max Size of Pair Chain
Coding Train: Max Size of Pair Chain
CODING INTERVIEW QUESTION(Onerous): Russian Doll Envelopes
Coding Train: Russian Doll Envelopes
Day 15: Dynamic Programming Sort – Hole Technique / Size clever Iteration
Day 15 Targets
Introduction to Hole Technique or Size clever Iteration
CODING INTERVIEW QUESTION(Medium): Palindromic Substrings
Palindromic Substrings
Instinct for Strategy
Indetifying this as a DP query
Strategy: Recursion with memoization
pseudocode
Filling the Memoization desk
iterate size clever
Recursion with memoization: Complexity evaluation
Python Code: Recursion with memoization
Tabulation method
Tabulation method: Complexity Evaluation
Python Code: Tabulation
CODING INTERVIEW QUESTION(Medium): Longest Palindromic Substring
Coding Train: Longest Palindromic Substring
CODING INTERVIEW QUESTION(Medium): Longest Palindromic Subsequence
Coding Train: Longest Palindromic Subsequence
Day 16: Dynamic Programming Sort – Partition Methodology
Day 16 Targets
Introduction to the Partition technique
CODING INTERVIEW QUESTION(Medium): Palindrome Partitioning
Coding Train: Palindrome Partitioning
Strategy
Pseudocode
Facet notice: Computing n C r
Complexity Evaluation
Python Code: Palindrome Partitioning
CODING INTERVIEW QUESTION(Onerous):Palindrome Partitioning 2 ( Minimal Cuts) – Onerous
Coding Train: Palindrom Partitioning 2 ( Min Cuts)
Strategy 1: Recursion
Python Code: Recursion – Palindrome Partitioning 2
Strategy 2: Memoization
Python Code: Memoization – Palindrome Partitioning 2
Tabulation – Strategy A : Palindrome Partitioning 2
Dry Run
Pseudocode
Python Code : Tabulation – Strategy A : Palindrome Partitioning 2
Complexity Evaluation
Tabulation – Strategy B : Palindrome Partitioning 2
Dry run
Pseudocode
Python Code: Tabulation – Strategy A : Palindrome Partitioning 2
Complexity Evaluation
Day 17: Dynamic Programming Sort – Partition Methodology
Day 17 Targets
CODING INTERVIEW QUESTION(Medium): Phrase Break
Coding Train: Phrase Break
CODING INTERVIEW QUESTION(Onerous): Matrix Chain Multiplication
Coding Train: Matrix Chain Multiplication
Day 18: Dynamic Programming Sort – Kadane’s Algorithm
Day 18 Targets
CODING INTERVIEW QUESTION (Medium): Max Subarray
Coding Train: Max Subarray
CODING INTERVIEW QUESTION (Medium): Most Product Subarray
Coding Train: Most Product Subarray
Day 19: Arrays Knowledge Constructions and Algorithms
Day 19 Targets
Coding Interview Q1(Medium): Rotate Array
Coding Train: Rotate Array
Methodology and Large O evaluation
PYTHON Code Resolution
Python Code Methodology 2
Coding Interview Q2(Medium): Container with most water
Coding Train: Container with most water
Methodology 1 and Large O evaluation
PYTHON Code Methodology 1
Methodology 2 and Large O evaluation
PYTHON Code Methodology 2
Day 20: Dictionaries / Hash Tables Knowledge Constructions and Algorithms
Day 20 Targets
Hash Desk: Knowledge Constructions Crash Course
Coding Interview Q1(Straightforward): Two Sum
Coding Train: Two Sum
Methodology 1, Large O evaluation
PYTHON Code
Methodology 2, Large O evaluation
PYTHON Code
Coding Interview Q2(Straightforward): Isomorphic Strings
Coding Train: Isomorphic Strings
Methodology and Large O evaluation
PYTHON Code
Day 21 : Strings Knowledge Constructions and Algorithms
Day 21 Targets
Knowledge Constructions Crash Course: Strings
Coding Interview Q1(Straightforward): First Non Repeating Character
Coding Train: First Non Repeating Character
Methodology 1 and Large O evaluation
PYTHON code
Methodology 2 and Large O evaluation
PYTHON code
Coding Interview Q2(Straightforward): Is Palindrome ?
Coding Train: Is Palindrome ?
Methodology 1 and Large O evaluation
PYTHON code
Methodology 2 and Large O evaluation
PYTHON code
Methodology 3 and Large O evaluation
PYTHON code
Day 22: Strings Knowledge Constructions and Algorithms
Day 22 Targets
Coding Interview Q1(Medium): Longest Sub string with Distinctive characters
Coding Train: Longest Sub string with Distinctive characters
Methodology and Large O evaluation
PYTHON code
Coding Interview Q2(Medium): Group Anagrams
Coding Train: Group Anagrams
technique and Large O evaluation
PYTHON code
Day 23: Looking Algorithms
Day 23 Targets
Coding Interview Q1 (Straightforward): Binary Search Algorithm
Coding Train: Binary Search Algorithm
Methodology and Large O evaluation
PYTHON Code Iterative
PYTHON Code Recursive
Coding Interview Q2(Medium): Search in rotated sorted array
Coding Train: Search in rotated sorted array
Methodology and Large O evaluation
PYTHON Code
Day 24: Looking Algorithms
Day 24 Targets
Coding Interview Q1(Medium): Seek for vary
Coding Train: Seek for vary
Methodology and Large O evaluation
PYTHON Code – Recursive
PYTHON Code – Iterative
Coding Interview Q2(Medium): Search in Matrix
Coding Train: Search in Matrix
technique and Large O evaluation
PYTHON code
Day 25: Sorting Algorithms
Day 25 Targets
Coding Interview Q1: Bubble Type Algorithm
Coding Train: Bubble Type Algorithm
Methodology and Large O evaluation
Python Code
Coding Interview Q2: Insertion Type Algorithm, Large O evaluation
Coding Train : Insertion Type Algorithm
Python code
Insertion type is a steady sorting Algorithm
Day 26: Sorting Algorithms
Day 26 Targets
Coding Interview Q1: Choice Type Algorithm, Large O evaluation
Coding Train: Choice Type Algorithm
Python Code
Coding Interview Q2: Merge Type Algorithm
Coding Train: Merge Type
Methodology and Large O evaluation
Python Code
Day 27: Sorting Algorithms
Day 27 Targets
Coding Interview Q1: Fast Type Algorithm
Coding Train: Fast Type
Optimise Time Complexity
Optimise Area Complexity
Python Code
Coding Interview Q2: Radix Type Algorithm, Large O evaluation
Coding Train: Radix Type Algorithm
Python Code
Day 28 Singly Linked Record Knowledge Constructions and Algorithms
Day 28 Targets
Knowledge Constructions Crash Course: Linked Lists
Coding Interview Q1(Medium): Design a Singly Linked Record
Coding Train: Design a Singly Linked Record
Methodology and Large O evaluation
Python Code
Coding Interview Q2: Take away Duplicates
Coding Train: Take away Duplicates
Methodology and Large O evaluation
Python Code
Day 29 Singly Linked Record Knowledge Constructions and Algorithms
Day 29 Targets
Coding Interview Q1(Straightforward): Reverse
Coding Train: Reverse SLL
Methodology and Large O evaluation
Python Code
Coding Interview Q2(Medium) : Cycle Detection
Coding Train: Cycle Detection
Methodology and Large O evaluation
Python Code
proof
Day 30 : Singly Linked Record Knowledge Constructions and Algorithms
Day 30 Targets
Coding Interview Q1(Medium): Discover duplicate quantity
Coding Train: Discover duplicate quantity
technique and Large O evaluation
Python code
Coding Interview Q2(Medium): Add 2 numbers
Coding Train: Add 2 numbers
technique and Large O evaluation
Python code
Day 31 Doubly Linked Record Knowledge Constructions and Algorithms
Day 31 Targets
Coding Interview Q1: Take away Node, Insert Node
Coding Train: Take away Node
Methodology take away
Python code: Take away
Insert Intro
Methodology Insert
Coding Train: Insert Node
Python code: Insert
Coding Interview Q2: Take away Worth, Insert at Place in Doubly Linked Record
Coding Train :Take away Worth in Doubly Linked Record
Take away Val Methodology
Python Code
Insert at Place
technique
Coding Train: Insert at Place in DLL
Python Code
Day 32: Stacks Knowledge Constructions and Algorithms
Day 32 Targets
Knowledge Constructions Crash Course: Stacks and Queues
Coding Interview Q1: Design a Stack
Coding Train: Design a Stack (with Linked Record)
Python Code
Coding Interview Q2(Medium): Reverse Polish Notation
Coding Train: Reverse Polish Notation
technique and Large O evaluation
Python Code
Day 33: Queue Knowledge Constructions and Algorithms
Day 33 Targets
Coding Interview Q1: Design a Queue
Coding Train: Design a Queue utilizing a Linked Record
Python Code
Coding Interview Q2(Straightforward) : Queue with Stack
Coding Train : Queue with Stack
technique and Large O evaluation
Python Code
Day 34: Binary Tree / Binary Search Tree Knowledge Constructions and Algorithms
Day 34 Targets
Knowledge Constructions Crash Course: Timber Introduction
Idea: Binary Timber 1
Proof : top of Balanced Binary tree is flooring(log N)
Idea: Binary Tree Terminaologies
What’s a BST – Binary Search Tree
Coding Interview Q1: Assemble Binary Search Tree,Large O evaluation
Coding Train: Assemble Binary Search Tree
Python Code
Coding Interview Q2 : Traverse – BFS and DFS,Large O evaluation
Coding Train : Traverse – BFS and DFS
Python Code
Day 35: Binary Tree / Binary Search Tree Knowledge Constructions and Algorithms
Day 35 Targets
Coding Interview Q1(Medium): Degree Order traversal
Coding Train: Occasion technique, Degree Order traversal
Insert technique
Python code
Degree Order Traversal Methodology and Large O evaluation
Python code – Degree order traversal
Coding Interview Q2(Medium): Left / Proper view
Coding Train: Left / Proper view
Methodology and Large O evaluation
Python code
Day 36: Binary Tree Knowledge Constructions and Algorithms
Day 36 Targets
Coding Interview Q1 (Straightforward): Invert Binary Tree
Iterative technique and Large O evaluation
Python Code: Iterative
Recursive technique and Large O evaluation
Python Code: Recursive
Coding Interview Q2 (Straightforward): Diameter of Binary Tree
Methodology and Large O evaluation
Python Code
Day 37: Binary Search Timber Knowledge Constructions and Algorithms
Day 37 Targets
Coding Interview Q1(Straightforward): sorted array to BST
technique and Large O evaluation
Python code
Coding Interview Q2(Medium) : Legitimate BST
Methodology and Large O evaluation
Python Code
Day 38: Heaps and Precedence Queue Knowledge Constructions and Algorithms
Day 38 Targets
Binary Heap: Knowledge Construction Crash Course
Coding Interview Q1: Assemble Max Binary Heap, Large O evaluation
Proof of Construct Binary Heap Time Complexity
Python Code
Introduction to Precedence Queue
Coding Interview Q2: Assemble Precedence Queue,Large O evaluation
Python Code
Day 39: Graphs Knowledge Constructions and Algorithms
Day 39 Targets
Coding Interview Q1: BFS, Adjacency Record,Large O evaluation
Python Code
BFS, Adjacency Matrix
Python Code
Coding Interview Q2: DFS, Recursive, Large O evaluation
Python Code
DFS Iterative
Python Code
Day 40: Graphs Knowledge Constructions and Algorithms
Day 40 Targets
Coding Interview Q1: Variety of Parts, Large O evaluation
Python Code
Coding Interview Q2(Medium): Course Scheduler
Brute Power Methodology and Large O evaluation
Python Code – Brute Power Methodology
Topological Type based mostly technique and Large O evaluation
Python Code
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