๐ Hey everyone! Are you interested in #MachineLearning and #DeepLearning, but not sure where to start?
Don't worry, I've got you covered! In this thread, I'll be sharing some of the most important libraries you need to know for these fields.
#100DaysOfCode
Thread ๐งต
๐งฎ Let's start with the basics: #NumPy and #Pandas.
NumPy is a powerful library for numerical computing in #Python, while Pandas provides easy-to-use data structures for data analysis.
These libraries are essential for data pre-processing and manipulation in #MachineLearning.
๐ Want to create stunning visualizations for your #data?
Look no further than these amazing visualization libraries: #Matplotlib, #Seaborn and #Plotly. From basic charts to interactive dashboards, these libraries have got you covered! #DataVisualization #Python ๐
๐ค Next up is #ScikitLearn. This library provides a wide range of machine learning #algorithms for classification, regression, clustering, and more. It also includes tools for model selection, evaluation, and data preprocessing. #MLAlgorithms
๐งฌ Another important library is #TensorFlow, which is a popular open-source platform for building and deploying #DeepLearning models. It provides a variety of tools for building and training neural networks, including Keras, a high-level API for building and testing models.
๐ If you're a fan of #Python, you'll love #PyTorch, another popular deep learning library. It's known for its simplicity, flexibility, and ease of use, and is widely used in industry and academia for a variety of applications.
(It is more popular compared to #TensorFlow )
๐คฏ Last but not least, we have #OpenCV, a computer vision library that is often used in #MachineLearning and #DeepLearning projects. It includes a wide range of image and video processing tools, as well as machine learning algorithms for object detection and recognition.
I hope you found this list helpful! What other libraries would you add to this list? Let me know in the comments below! ๐
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