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A million observations of 20 features should be very manageable on a laptop, if a little slow. Cloud computing for very large datasets is staggeringly expensive and offers little or no benefit unless and until you have good parallelization in place. I would recommend keeping that option as your last resort. For the initial data exploration and ...


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There are 2 things you can do here: 1.) Use libraries like Dask to speed up your data preprocessing. Here is the link 2.) Use cloud computing services like Azure, AWS or GCP. I am not aware of other two but I have worked on Azure and it provides a lot of options for implementing a data science solution. You get options like Auto-ML, Azure Designer, Python ML ...


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Deep Learning Specialization on Coursera: You can follow the lectures for free or apply for a scholarship if you can't afford it. Even though some content is a bit old, still a very extensive source from fundamental concepts of machine learning to advanced optimization concepts and additional courses on CNN's and Sequence models. Also, there are several ...


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https://youtu.be/rfscVS0vtbw - Python basics https://youtube.com/playlist?list=PLZoTAELRMXVPBTrWtJkn3wWQxZkmTXGwe - Full Machine learning tutorials https://youtu.be/xxpc-HPKN28 - Statistics needed for Data Science TBH this is all you need to get to an intermediate level. PS: This only includes traditional machine learning. The op did not mention NLP/DL ...


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Parallelize your analyses on a single (multi-cpu) machine with e.g. pandarallel or the like or go for broke with scala/spark/hadoop if the problem wont fit on a single machine.


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