When it comes to libraries in Python, there are more than plenty. But which ones are the most useful for machine learning and great for beginners? To identify the best python libraries for machine learning, we have to specify the best use cases. The best machine learning libraries will have the functionality to handle the most common data types as well as the most common machine learning algorithms.
The most common data types are images, text and tabular data. For images, the best libraries are opencv and fastai. OpenCV is well known in the computer vision industry and is used by many top companies, but it's also great for beginners as well. It allows us to preprocess images and videos, as well as carry out analysis on them. For text, nltk and spaCY are libraries that perform exceptionally well at handling and preparing text data for machine learning algorithms, with important functions like tokenisation.
Lastly, tabular data and time series data go well with libraries like pandas, statsmodel and numpy. Best all rounded libraries for machine learning algorithms include tensorflow and pytorch and Keras is the best library for deep learning. With knowledge of how to make use of these particular libraries, it possible to build extremely useful and innovative machine learning solutions.
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