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 augment my theoretical knowledge with practical hands-on coding. Now, here’s the thi...
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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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 ...
One of my favorite parts of my job as a developer advocate is being able to help people get started in data science. I still remember when I made the transition from academia to data science almost 8
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...
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, ...
Kaggle also provides notebooks with the algorithms and different types of pre-trained models. AWS Datasets You can search, download and share the datasets that are publicly available in theregistry of open data on AWS. Though they are accessed through AWS, the datasets are maintained and updated...
. But, when doing so, we suggest also uploading a Notebook discussing what and where the files are, how to work with them, and demonstrating how to get started with the dataset. Reproducible code samples can go a long way towards making your data files accessible to the data science ...
Make sure theidfield indataset-metadata.json(ordatapackage.json) points to your dataset Runkaggle datasets version -p /path/to/dataset -m "Your message here" These instructions are the basic commands required to get started with creating and updating Datasets on Kaggle. You can find out more...