3β£ Cracking The Coding Interview by Gayle Laakmann McDowell
This book changed the game completely for me!
It gave me all the information I needed!
I solved every exercise, some more than once!
I highly recommend this one!
4/8π§΅
4β£ Google Code Jam!
I used the exercises to practice!
They are challenging but I tried most of the easier ones and it helped me a lot on thinking about input sizes and corner cases.
The idea is to use them to practice. I don't have a preferred one. Find one you like and practice!
6/8π§΅
Bonus2: π
Another resource to help are repository with many algorithms for you to read like this one: github.com/TheAlgorithms/β¦
Of course you don't need to understand all of them but while studying you might want to check out some specifics ones
7/8π§΅
Summary:
β’ Have good resources to follow and study
β’ You need to dedicate time to practice, reading is no enough
β’ You'll be a better developer by the end!
Do you have more resources to share? Leave them in the comments!
Don't forget to follow me for daily content!
8/8π§΅
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Which programming language do I need to know to start with Machine Learning?
π π€π§
[1 quickβ‘οΈ min]
1/5π§΅
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
Butβ¦π
2/5π§΅
The more realistic answer: Pythonπ
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:
β’ Problem Framing
β’ Data Prep
β’ Clustering
β’ Recommendation
β’ Test and Debug
β’ GAN
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
1/6π§΅
Recommender Systems have a huge Impact:
β’ 40% of app install on Google Play π€βοΈ
β’ 60% of watch time on YouTube πΆπ±
β’ 35% on purchases on Amazon πΈ
β’ 75% of movie watches on Netflix πΏ
2/6π§΅
Recommender Systems are very hard to train/evaluate/deploy:
β’ Lots of features! π°π±
β’ Optimize to multiple objectives βοΈ
β’ Metrics can be misleading π€
β’ Require lots of resources to be served π°πΈ