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It's a technically rigorous course. Recommend? It depends on what your goal is. Definitely, if your goal is to compete on Kaggle. Not really, if you want to improve practical ML. Remember, Kaggle is kind of Formula 1. The fastest car wins. But in the real world, you don't always need a Formula 1 car. Sometimes you need an SUV, sometimes you need a van, ...


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TLDR: No, it is not required for someone with your background to take "Python for everyone" beforehand. I have taken the "Applied Data Science with Python Specialization" by UofM and it provided a very soft start in terms of Python for Data Science. Also, see the description of the first course of the specialization "Introduction to ...


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Dataquest is a great place to start. It will help you bridge the gap from just learning to actually coding as a data analyst, data engineer, or data scientist. Unlike most online courses, there are no videos, and they provide an interactive coding and learning environment that makes it very easy to learn the practical techniques you'll actually need to work ...


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First of all, Congrats that you have started your journey of becoming a Data engineer/analyst. According to me there is no clear path on how to become a Data analyst. Applied data science with Python course is great but i would urge you to start working on problem statement of any domain (NLP,CV) and take part in competition where you will learn how to ...


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There a three dimensions of knowledge you need to be a generally competent data scientist: Statistical / Mathematical knowledge Programming / CS skills Domain knowledge I think studying and courses help in 1) and to a degree in 2) but not at all in 3. If you feel confident enough in 1/2 that you can solve some real life cases it would be best to start ...


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I will describe my personal approach to achieve what is taught in the Course you mentioned, a mind blowing course I think! To generate DT features for your training set: First of all, split your training data in k-folds. It is necessary to avoid overfitting. Then: Hide a fold and build a DT using the (k-1) remaining folds. Control the number of leaf nodes ...


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Let us assume you want to do hyperparameteroptimization with a hyperparameter $h\in[0,1]$. Let us additionally assume that we want to test hundred possible values for the parameter. If you choose a linear scale your parameter values will be uniformly selected form $[0,10^4]$. It will be very unlikely that you will small parameter values smaller than $10^{-4}...


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