Kaggle is the market leader when it comes to data science hackathons. I started my own data science journey by combing my learning on both Analytics Vidhya as well as Kaggle – a combination that helped me augm
train= np.array([x[1:]forxindataset])#In this case we'll use a random forest, but this could be any classifiercfr = RandomForestClassifier(n_estimators=100)#Simple K-Fold cross validation. 5 folds.cv = cross_validation.KFold(len(train), k=5, indices=False)#iterate through the traini...
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Just like when I started in 2016, data science is defined differently depending on who you talk to. However, the field has definitely gotten more complicated as it has matured, with additional roles like machine learning and MLOps engineers becoming established in the last few years. Despite al...
I coveredgetting started with Kaggle in Google Colabin a separate article, so if that’s what interests you, please check that out! Importing libraries Imports are pretty standard, with a few exceptions. For the most part, you can import your libraries by runningimportlike you do in any oth...
Online Resources for Getting Started with Data Science and Machine LearningFor someone trying to get started with ML, here is a resource where the complexity is just right. It introduces you to a lot of the essential Mathematics but doesn’t go too deep into it. It is an equivalent of ...
In this blog post, I’ll explain everything you need to know about the new Polars GPU engine and provide a step-by-step guide to help you get started! Polars: A High-Performance DataFrame Library At the core of most data science workflows is the DataFrame, a tabular data structure that...
In addition to my technical expertise, I am also a skilled communicator with a talent for distilling complex concepts into clear and concise language. As a result, I have become a sought-after blogger on data science, sharing my insights and experiences with a growing community of fellow data...
He had a number of tips for beginners. His was exactly the talk that I was looking for, though I didn’t know it. I am sharing some of his tips here, in case it helps others as well. Jeremy Howard’s Tips for Getting Started on Data Mining competitions at Kaggle ...
Getting Started with Mixtral 8x22B In this section, we will learn how to start using the Mixtral 8X22B model using the Mistral API. Since the model is about 80 gigabytes in size and requires a 300 gigabyte GPU, it will be a bit hard and expensive to run it on any cloud provider, ...