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!
Amusing footnote on the travelling salesman problem: The Japanese used a mushroom to map out an optimised solution for their railway network, by using oat flakes as „stations“, and the mycelial strings that grew the strongest where those linking the „stations“ with the shortest possible route. Bio-computer solving the problem via nutrient-gradient.
Hello I would like to suggest something, the colors in this video were not very "popping" lets call it. In the video you have shown multiple times a binary tree where a few nodes are light purple and a few nodes are dark purple. I would like to suggest that you make the dark nodes more dark so as to make sure the color difference is able to be seen easily. You really have to concentrate to view them. Also Could you make a dedicated video to Binary Trees next? That would be a great help for me
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.
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.
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.
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.
Sounds like you just need some greedy algos and a creative application of the traveling salesman problem. (Routes through all bars, and pickup techniques)
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
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.
Hi, i really like the graphical representation of these topics in simple manner, however i think that @10:40 the explanation of bubble sort is mentioned as insertion sort.
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 :)
no hay un video que te enseñe todo, al final debemos consumir mucho contenido de calidad e intentar quedarnos con lo positivo de dicho contenido para internalizarlo y realmente poder implementarlo de manera adecuada. Este video a mi criterio cumple con el criterio de ser un video de calidad.... No comenté en inglés porque siento que los hispano hablantes que no son angloparlantes merecen también hacerse notar en las cuentas de tecnología donde usualmente solamente se habla inglés
Good explanation, thank you. A small note for the quicksort - keep in mind that the unshift operation at 13:44 is really computational costly as you will have to unshift all elements to the left. It is better to just swap the chosen pivot element with the last one.
I start learning data science because the curiosity. I used to learn math and data structure before but I don’t know where to apply so now is different I can know.
Wow, you have no idea how awesome that is to hear. Thank you so much for that, and I hope I can continue to keep helping you on your journey towards breaking into the tech industry!
Oh and here is some advice that I think might be helpful: I know music in videos is vastly used, but mostly it‘s a bit too much. The music in this video was okay, but I actually liked the music-free videos more. They *seem* to me better structured/clean. I know this video has the same great content as always, but (maybe it‘s just me) I have problems following you. Idk, maybe I‘m wrong and using music is a good idea ^^ Just a friendly tip :)
@@voegel thank you for the feedback! I’ve been experimenting a bit with the music, but I get that it might be a bit hard to follow with it. I’m going to experiment a bit with audio levels/not including it at all, thanks for letting me know :)
This so weird. I leaned all these in 1994 to 1997 but never heard of greedy or maybe I did but I certainly remembered the shortest path algorithm which is greedy
Hey, Thanks for the video. I am a final year CSE student and I have the placement season starting soon. I am left with a lot of DSA. Could you suggest me with some tips?
Only problem with dsa is practical approach... theory part understood well and coding of those sortings are somewhat difficult...do we need to bihot the logic?
Thanks for the video. Note: Merge sort is not properly explained. 1. it requires creation of temporary arrays of size equal to the sorted array. 2. the "merging" process itself is not highlighted in video.
i think i am not getting the insertion sort because you are saying that we swap the value but we swap the value in the bubble sort and in the insertion sort we catch the value of index [0] and then we compare with that value and assign the position here is the code public static int[] Insertionsort(int[] Element) { //[5,10,8,6,2,1] int n =Element.Length; for (int i = 1; i < n; i++) { int temp = Element[i]; //10 int j = i - 1; //0 while ((j >= 0) && Element[j] > temp ) // 10 >8 { Element[j + 1] = Element[j]; j--; } Element[j + 1] = temp; } return Element; }
Yes! That’s what’s coming out next! I’ll be doing kind of 8-10 minute in-depth videos explaining how the code works, and how to solve problems of using the algorithm! Stay tuned :)
Really appreciate your work but you are wrong about the insertion sort algorithm you explained the bubble sort insertion sort will compare each number with the previous numbers and put it in place. Hope you will fix the video
Greedy sort is not used when you need an ACCURATE answer, not efficient as you initially stated. The CC has it correct later on. Important difference though, as greedy sort is way more efficient when you're looking at impossibly large possibilities!
Man, this just helped me so much in ways I can't even explain. Took the entire time taking notes and all. Really appreciate the hard work that was put into this, like and sub from me cheers!
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!
This video is one of the finest and well defined educational video on algorithms.
Amusing footnote on the travelling salesman problem: The Japanese used a mushroom to map out an optimised solution for their railway network, by using oat flakes as „stations“, and the mycelial strings that grew the strongest where those linking the „stations“ with the shortest possible route.
Bio-computer solving the problem via nutrient-gradient.
Hello I would like to suggest something, the colors in this video were not very "popping" lets call it. In the video you have shown multiple times a binary tree where a few nodes are light purple and a few nodes are dark purple. I would like to suggest that you make the dark nodes more dark so as to make sure the color difference is able to be seen easily. You really have to concentrate to view them. Also Could you make a dedicated video to Binary Trees next? That would be a great help for me
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.
Good work, works for adding to quick documentation
My man
Not all hero’s wear capes
God bless you!
Thanks mate you saved the day ! :)
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.
Code examples would help a lot too, great video
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.
Codebagel is officially my favorite DSA channel. Thank you!
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.
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
stay strong brother!
Great choice bro
Sounds like you just need some greedy algos and a creative application of the traveling salesman problem.
(Routes through all bars, and pickup techniques)
Broke up and still leveling up GOD mode acticated 🤖
Looks like someone was greedy 😂😂😂😂
Why does this channel have only 55.6K subscribers? Dude you are a gem! Love your videos, they are very helpful!
You should explain how machine learning works and more of that types of videos too ,your explanation is on another level
Thanks so much! I’ll definitely tackle machine learning at some point in the near future, it’s on the list of future videos!
Commenting for the youtube algorithm! Binge watching your videos to prepare for my technical interview. VERY helpful!!
Thanks so much! More coming soon 👀
just found your channel and the videos are awesome. Amazing balance between simplicity and coverage for the topics
Thank you for simplifying algorithms for me! I learned a lot from you, keep up this amazing tutoring 🙏🏻
I’m currently taking Data Structures. So this is coming very handy.
Thanks for this video! I’m starting to prepare for technical interviews now, and this is a big help!
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
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.
The quality of your videos is insane! Keep going, the growth of your channel is bound to happen!
With content like this you'll be big in no time, excited for what's to come!
Thank you so much! I really appreciate the support!
Bravo, the effort shows in the quality, detail and simple concise delivery.
Bro, these explanations are truly great and easily digestible. Hopefully there is more to come!
Where are you , its been 2 years
Your videos are soo good we need more
This is the best explained algorithm I came across
This is great, Very well explained and straight to the point. Way better than any teacher I have seen explain this..
Hi, i really like the graphical representation of these topics in simple manner, however i think that @10:40 the explanation of bubble sort is mentioned as insertion sort.
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 :)
Thanks so much! I’m glad you enjoyed it. Content will definitely continue to get even better, so I’m happy you’re going to stick around :)
Your representation helps me understand better thanks for you help
Thank you!
Explaining Data Structures with Code Examples would be more beneficial. This video is informative too.
no hay un video que te enseñe todo, al final debemos consumir mucho contenido de calidad e intentar quedarnos con lo positivo de dicho contenido para internalizarlo y realmente poder implementarlo de manera adecuada. Este video a mi criterio cumple con el criterio de ser un video de calidad.... No comenté en inglés porque siento que los hispano hablantes que no son angloparlantes merecen también hacerse notar en las cuentas de tecnología donde usualmente solamente se habla inglés
Good explanation, thank you. A small note for the quicksort -
keep in mind that the unshift operation at 13:44 is really computational costly as you will have to unshift all elements to the left. It is better to just swap the chosen pivot element with the last one.
This vid is doing quite well for explaining,appreacting your hard work.
Loving your channel bro! The examples you give are insightful.
I start learning data science because the curiosity. I used to learn math and data structure before but I don’t know where to apply so now is different I can know.
I love the fact that you took the time for writing the outcome numbers of TSP in the comments haha. Nice work as always, keep it up!
Haha yeah, to me that’s such a strong way to explain why certain algorithms are used. I’m glad you enjoyed!
I know ur working hard my guy but keep pushing you'll breeze to 100 k by the end of the year dw 💪🧠
Keep up the amazing content man
Thanks so much! Been such a grind lately with everything going on, but definitely going to start pumping out more videos so that’s good :)
I really wonder how this video hasn't blown up after a whole year
Dude , you explain so well. Thank you
This video got me liked and subscribed. You explained this really well.
Your content is literally quality over quantity , keep the good work man
Thank you! I really appreciate it :)
A very well done sir. God bless your effort & reward you. 🙏
When I get my coding gig in the future, I'm gonna look back at these videos and think "This is why I landed this job".
Wow, you have no idea how awesome that is to hear. Thank you so much for that, and I hope I can continue to keep helping you on your journey towards breaking into the tech industry!
I wish you would have just added an example code for each. But great video, keep up the good work!
Omg, that’s so cool! Outstanding work!
Amazing content, very clearly explained and the illustrations helped a lot, thank you for sharing.
Very detailed explanation as always! I learnt a lot, thank you so much!!
You’re welcome! Thanks for the kind words :)
Please keep going, your explanations are on point! Subscribed.
Thanks so much!
Well illustrated! Thanks
thank you soo much.
i know this could have taken so much affrords to make .
Thank you for this comprehensive video! 21 minutes is a good length. Btw, did your job interviews went well?
Oh and here is some advice that I think might be helpful: I know music in videos is vastly used, but mostly it‘s a bit too much. The music in this video was okay, but I actually liked the music-free videos more. They *seem* to me better structured/clean. I know this video has the same great content as always, but (maybe it‘s just me) I have problems following you. Idk, maybe I‘m wrong and using music is a good idea ^^ Just a friendly tip :)
@@voegel thank you for the feedback! I’ve been experimenting a bit with the music, but I get that it might be a bit hard to follow with it. I’m going to experiment a bit with audio levels/not including it at all, thanks for letting me know :)
Excellent explanation!
Thank you so much for this detailed video bro👊👊
Love it thanks for such a clear explanation with examples . Liked and subbed
easy to understand bro.. great. Thanks!
I've been trying to understand these concepts for ages. Your videos rock! Even if you spell neighbor wrong. 🙂
I was here before this channel exploded. Keep the contents coming.
Thanks so much Sujeet! I’ll keep working towards making better and better content for you to enjoy!
Great explanation! Thank you
Hey, I‘m wondering if you are still planning to make a video about how to pass the resume screening? That might be quite interesting :)
Yes, it’s actually the next video coming out! I’m hoping to get it finished and uploaded for Monday! :)
Okay so I didn’t finish it in time for Monday haha, had 2 last minute interviews come up. It will be out by tomorrow though, guaranteed!
This so weird. I leaned all these in 1994 to 1997 but never heard of greedy or maybe I did but I certainly remembered the shortest path algorithm which is greedy
I love all of your videos. Amazing they are. Please make videos on System Design as well.
great video... straightforward explanations
Really impressive contents mate keep going . appreciate you'r contents and efforts. :)
Hey, Thanks for the video.
I am a final year CSE student and I have the placement season starting soon. I am left with a lot of DSA. Could you suggest me with some tips?
Dynamic Programming left the chat..🏃🏻♂️🏃🏻♂️
PS: amazing visuals & explanations. Thanks ❤
Amazing!! You helped me so much!!
Like your voice, subcribed to listen it as frequently as possible.
Only problem with dsa is practical approach... theory part understood well and coding of those sortings are somewhat difficult...do we need to bihot the logic?
can you please upload the separate data structure videos? i really liked the hashmap one
Great job bro. It was very interesting & easy to understant
Thanks I'll subscribe for your hard work. Now to implement them in Python.
Underrated channel Subbed!!
Love your videos ! please do more, ive learned so much
Thanks for the video.
Note: Merge sort is not properly explained.
1. it requires creation of temporary arrays of size equal to the sorted array.
2. the "merging" process itself is not highlighted in video.
Thanks, great explanation
Travelling salesman problem is à good support to practise genetic algorithms
Thanks🙏 very clear explanation
Very good explanation
i think i am not getting the insertion sort because you are saying that we swap the value but we swap the value in the bubble sort and in the insertion sort we catch the value of index [0] and then we compare with that value and assign the position here is the code
public static int[] Insertionsort(int[] Element)
{
//[5,10,8,6,2,1]
int n =Element.Length;
for (int i = 1; i < n; i++)
{
int temp = Element[i]; //10
int j = i - 1; //0
while ((j >= 0) && Element[j] > temp ) // 10 >8
{
Element[j + 1] = Element[j];
j--;
}
Element[j + 1] = temp;
}
return Element;
}
Well explained but you mentioned binary search only works for sorted list but it is not true .It also works for unsorted.
can you possible make a video on how to code up these algorithms? that is what i have the most trouble with
Yes! That’s what’s coming out next! I’ll be doing kind of 8-10 minute in-depth videos explaining how the code works, and how to solve problems of using the algorithm! Stay tuned :)
@@Codebagel yessir goated youtuber🐐
Great explanation!👍
Loved this, please make more videos
And here I am a civil engineer loving C++! And hence learning programming algos. 😂
Can u plz do the selection sort
Great video, helped me a lot
Great job, mate!
goated video🐐🐐
Pseudo code would be very helpful.
This video is very helpful ❤
Really appreciate your work but you are wrong about the insertion sort algorithm you explained the bubble sort insertion sort will compare each number with the previous numbers and put it in place. Hope you will fix the video
I came here to say this. I've not heard of insertion sort before, this is definitely a Bubble sort.
DFS and BFS require sorted binary trees to operate, right?
Greedy sort is not used when you need an ACCURATE answer, not efficient as you initially stated. The CC has it correct later on. Important difference though, as greedy sort is way more efficient when you're looking at impossibly large possibilities!
Great explaination. 👍
Man, this just helped me so much in ways I can't even explain. Took the entire time taking notes and all. Really appreciate the hard work that was put into this, like and sub from me cheers!
Thank you so much!
11:48 How does this sorting work in detail?
Best Explanation!
Wait for Dynamic programming video 😁🤘
Thank you so much, amazing content
It was really great! Thanks
0:10 BFS & DFS are not searches, they are traversals
0:22 greedy is not algorithm, its an approach