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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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
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