Top 7 Algorithms for Coding Interviews Explained SIMPLY

Today we'll be covering the 7 most important algorithms you need to ace your coding interviews and land a job as a software engineer. We'll be showing visualizations, common uses, and time complexity. Hope you enjoy!

📘 Chapters
0:00 - Intro
0:57 - Binary Search
3:43 - Depth-First Search
6:39 - Breadth-First Search
9:13 - Insertion Sort
10:57 - Merge Sort
12:52 - Quick Sort
15:57 - Greedy

🔗 Resources

🎵 Music
Music Courtsey of Epidemic Sound
Wizzle - Guustavv
Slapperoni Pizza - Jobii
Unfettered - Jobii
At My Own Pace - Sarah, the Illstrumentalist
Deadlines - Dylan Sitts
Sleepy + Hungry - baegel
Tea Tree - Jobii
Taargus - Jobii
Pick Up Your Phone Please - baegel
Brontosaurus - Dylan Sitts

#coding #softwareengineer #algorithm

[Codebagel]

Hey guys! Sorry for lack of uploads lately, hopefully this 20 MINUTE comprehensive guide to algorithms makes up for it a bit! Please like and share with friends, I worked really hard on it and want it to help lots of people!

[shadowrl]

Algorithms

Binary Search:
- Used to find a specific element in a sorted list efficiently.
- Inefficient: O(n) for linear search, incrementally guessing from start to end.
- Efficient: O(log2(n)) for binary search, repeatedly dividing the search interval in half until the correct element is found.

Depth-First Search (DFS):
- Begins at the root node and explores as far as possible along each branch before backtracking.
- Utilizes a visited array to track already visited nodes.
- Continues backtracking until all nodes are visited.
- Real-life example: Solving a maze by systematically exploring paths until the exit is found.

Breadth-First Search (BFS):
- Looks at every node at one level before going down to the next level.
- Utilizes a visited array to track already visited nodes and a queue to keep track of neighbors.
- Begins at the root node and adds it to the visited array and all its connected nodes to the queue, then continues to explore nodes level by level.
- Real-life example: Chess algorithms predict the best move by exploring possible moves at each level of the game tree.
- Runtime: O(V + E), where V is the number of vertices and E is the number of edges.

Insertion Sort:
- Examine’s each element in the list, comparing it with the previous elements and shifting them to the right until the correct position for insertion is found.
- Simple sorting algorithm suitable for small datasets or nearly sorted arrays.
- Runtime:
- Best case: O(n) when the list is already sorted.
- Worst case: O(n^2) when the list is sorted in reverse order.
- Efficient for small or nearly sorted lists, but inefficient for large unsorted lists.

Merge Sort:
- A divide-and-conquer sorting algorithm that breaks the problem into smaller subproblems and solves them recursively.
- Starts by splitting the array into halves recursively until each subarray consists of single elements.
- Merges pairs of subarrays by comparing elements and placing them in sorted order.
- Continues merging subarrays until the full array is sorted.
- Runtime: O(n log(n)) in both best and worst cases, making it efficient for large datasets.

Quick Sort:
- A complex sorting algorithm that follows the divide-and-conquer approach and is recursive.
- Selects a pivot element, ideally close to the median, and partitions the list into two sublists: one with elements greater than the pivot and the other with elements less than the pivot.
- Continues the process recursively on each sublist until the entire list is sorted.
- Utilizes a pivot element that is moved to the end of the list, with pointers positioned at the leftmost and rightmost elements.
- Compares the elements pointed to by the left and right pointers, swapping them if necessary, until the pointers cross.
- Once the pivot is correctly positioned, the process repeats on the sublists.
- Runtime:
- Best case: O(n log(n)), when the pivot consistently divides the list into approximately equal halves.
- Worst case: O(n^2), when the pivot selection consistently results in unbalanced partitions.

Greedy Algorithm:
- A problem-solving approach that makes the locally optimal choice at each stage with the hope of finding a global optimum.
- May not always guarantee an optimal solution but is often simple and efficient.
- Real-life example: Finding the shortest path in a weighted graph using Dijkstra's algorithm, where at each step, the algorithm selects the vertex with the smallest distance from the source.

[jasonbelcher]

I love how you put each algorithm into a use case context. That is literally the only way my interest based nervous system works. My hat is off to you sir. Keep it up.

[NikosS-id]

Why does this channel have only 55.6K subscribers? Dude you are a gem! Love your videos, they are very helpful!

[Redyf]

Code examples would help a lot too, great video

[bb-xjed]

thank you bro, my gf broke up with me so i have nothing but to learn algos, your video made me laugh at the greedy algo part. thank you u earned a sub

[LenSys-AE]

Thank you for simplifying algorithms for me! I learned a lot from you, keep up this amazing tutoring 🙏🏻

[ngocchaunguyen]

This is an incredible video. I didn't understand most algorithms as a student, because I focused on theories and code. This video helps me save time in understanding these popular algorithms. Indeed, I rarely use them in my work, it is simply because we don't have many situations to apply. I usually use linear search in JavaScript. there was a time I had a problem with sort of 10, 000 objects. I have to use the binary-sort library without understanding. Once again, thanks for your video.

[bigyabajracharya]

Commenting for the youtube algorithm! Binge watching your videos to prepare for my technical interview. VERY helpful!!

[originallyme]

This video is amazing! I was so surprised when I went to subscribe that you only had 5k subscribers, from the quality of these videos I expected at least 100k! Thank you for your awesome work, you really helped me a lot

[omgh]

I enjoyed the explanations and presentation about the different important algorithms in this video. I am subscribing to this channel in the hope of similar good content in the future. Wish you the best with growing your channel :)

[redfinance]

The quality of your videos is insane! Keep going, the growth of your channel is bound to happen!

[BbB-vruh]

Thanks for this video! I’m starting to prepare for technical interviews now, and this is a big help!

[wlqpqpqlqmwnhssisjw]

You should explain how machine learning works and more of that types of videos too, your explanation is on another level

[semilife]

Bravo, the effort shows in the quality, detail and simple concise delivery.

[LastShade]

For the traveling salesman, I would use a set of controlled variables that you could programmatically cycle through to optimize results. One example would be a tolerance on overlapping routes. Cycle through 0-10 overlaps and calculate results. Compare results. As long as you keep the number of variables and variable values low, it’s a great way to squeeze out extra optimization.

[tamalpaul]

Brother, You gotta be my best teacher on youtube. You making lesson just the way I want. You start a topic by first being pause. Then continue with the easiest way possible.
Please make more videos like these.

[durantburner]

With content like this you'll be big in no time, excited for what's to come!

[markost.]

Bro, these explanations are truly great and easily digestible. Hopefully there is more to come!

[largato]

just found your channel and the videos are awesome. Amazing balance between simplicity and coverage for the topics