Following up on the mock interview we did earlier this week, let me summarize all the topics discussed in the answers

Before starting, thanks to everyone for participating, it was great!

[1.5 min]

🧵
1⃣- Many good answers that, even being a working solution, weren't the fastest ones.
That's ok, but of course an interviewer might follow up asking you: Can you make it faster?

Tip: would a better data structure help you?
2⃣- A working solution is better than no solution!

Sometimes we want to optimize the code but usually you only have 1 hour to finish! Be smart, have a solution and explain how you'd make it perfect

With practice, your good solutions will become the perfect solution by default!
3⃣- Using libraries to help is fine, but the more magic you bring, the more likely you'll need to explain what's behind the magic.

Did you use the sort method?
Fine, what's the complexity of it?
How does it impact the overall complexity of your solution?
4⃣- create some sample inputs and make sure to add corner cases to it.

This shows you understand the problem!
5⃣ - Have questions? Ask them!

The interviewer is sometimes vague on purpose!
If you make assumptions, validate them with the interviewer, that's expected!
6⃣ - Communicate!

Explain what you are thinking and what you are trying to do.
Part of the process it to understand how well you can communicate your ideas
This was a great exercise! I'm very glad with the conversations and discussions that you posted!

I'll do it more often (as some requested)!

Don't forget to follow me (@gusthema ) so you don't miss this content and also Python and Machine Learning threads!

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More from @gusthema

24 Oct
This week I posted about the Code interview!!!😱😭🤓

Here's a summary if you missed it

[30 sec]

1/🧵
To start, You'll need some tips on how to succeed!👍🏾👍🏾



2/🧵
To follow the tips and succeed you'll need good resources to study and practice!📚👓

I've got you covered:

3/🧵
Read 6 tweets
19 Oct
When I was studying for my technical interviews I used a couple of different resources

Here is a list of the 4 most important ones..

[And some bonus ones! 🎁🎁]

[1 minute of investment]

1/8🧵
1⃣ The Algorithm Design Manual by Steven S. Skiena

Is a great book to study basic and advanced algorithms! The text is very clear and good to learn or review.

2/8🧵
2⃣ Introduction to Algorithms by Thomas H. Cormen, Charles E. Leiserson, Ronald L Rivest, Clifford Stein

It's also known as CLRS

This is one of the main books on Computer Algorithms.
It's very deep!



3/8🧵
Read 8 tweets
18 Oct
Are you afraid of the code interviews?😱😱😱
You're not alone!🫂

I've done many on both sides (candidate and interviewer)!

Here are some tips that helped me succeed:
👍🏾👍🏾👍🏾

[1.5 minutes]

1/10🧵
During the interview you are being evaluated in many aspects and not only your coding skills

So while solving the technical questions, talk to the interviewer and explain what you are trying to achieve.

Communication is an important skill!

2/10🧵
Some people get anxious during the interview and might get blocked or forget even the basics. This is a very common problem

To overcome this what I did was:
• Practice
• Practice
• Practice!

I solved many many problems on the white board before going for an interview

3/10🧵
Read 10 tweets
6 Oct
A common question I get from developers is:

Which programming language do I need to know to start with Machine Learning?
👅🤖🧠

[1 quick⚡️ min]

1/5🧵
The easy answer is: If you know how to code well, that's all you need to start learning ML!

TensorFlow for example enables you to use ML in many languages like C++, Java, Kotlin, Swift, Objective C, JavaScript, Go, Julia, Scala, Ruby, C# and many others

But…👀

2/5🧵
The more realistic answer: Python🐍

Most of the ML samples, tutorials and content in general you'll see is written in Python

Understanding the basics of the language will definitely make your life MUCH easier

Here is a good place to start: docs.python.org/3/tutorial/

3/5🧵
Read 5 tweets
5 Oct
Do you want to start working with Machine Learning?

Let me give you 3 tips that I used to start my journey and that helped me a lot!

[2 min]

1/7🧵
1 - Start by understanding the basic concepts

This Machine Learning Crash Course cover all the basics and terms you'll need to start

Separate a couple of hours and have fun!

developers.google.com/machine-learni…

2/7🧵
This course has been updated many times to keep it fresh and to add more than the basic with:
• Problem Framing
• Data Prep
• Clustering
• Recommendation
• Test and Debug
• GAN

3/7🧵
Read 7 tweets
20 Sep
If you use the internet, there's a big chance that you already experienced the results of a Machine Learning Recommendation System

They, given a list of possibilities and your past choices, suggest items of the list that might interest you

Lets understand more about them

1/6🧵
Recommender Systems have a huge Impact:

• 40% of app install on Google Play 🤖☎️
• 60% of watch time on YouTube 🐶🐱
• 35% on purchases on Amazon 💸
• 75% of movie watches on Netflix 🍿

2/6🧵
Recommender Systems are very hard to train/evaluate/deploy:

• Lots of features! 😰😱
• Optimize to multiple objectives ↔️
• Metrics can be misleading 🤔
• Require lots of resources to be served 💰💸

3/6🧵
Read 6 tweets

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