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Python Libraries for ML and its different frameworks

Python application development enables developers to play with both structured and unstructured data in the most seamless way.
But Python is not enough - there needs to be a machine learning framework where engineers can work with data. Frameworks include tools, libraries, and interfaces that help develop Python apps for ML-based projects and Deep Learning solutions.

But what are those machine learning frameworks and libraries? This article will highlight some of the top Python machine learning libraries and deep learning frameworks that developers use to create advanced AI-based solutions which helps in image detection, language processing, pattern detection, and much more.

  1. TensorFlow: The most popular library for machine learning, TensorFlow is the best Python application development tool for advanced solutions. This makes it easy to create machine learning models for beginners and professionals. It has built-in modules for visualization, inspection and model serialization. The Google Brain Team updates TensorFlow regularly. It is useful for Natural Language Processing, Deep Neural Network, Image and speech recognition and other functions of Deep learning.

  2. Keras: Keras is one of the fastest growing deep learning framework packages. Keras supports high-level network AP with clean user interface. Keras are used in building neural layers, solutions with activation and cost functions, batch normalization and much more. It works on top of TensorFlow, which extends its effectiveness for ML-based projects.

3.PyTorch: PyTorch's primary goal is to accelerate the entire process of Python app development for machine learning solutions. It has a C ++ frontend with Python interface. PyTorch enables fast manufacturing installations, providing companies with quick solutions. PyTorch offers easy training, building and small prototype.

4.Scikit-Learn: One of the leading Python libraries for Machine Learning, Scikit-learn quickly integrates with NumPy and Pandas. It is capable of creating machine learning models for classification, regression, clustering, dimensionality reduction and other types of algorithms. The main purpose of SkitLearn is to focus only on data modeling. This is the basic library that engineers use to create end-to-end machine learning applications. The library also has some excellent data pre-processing tools.

5.Pandas: Python is another well known Python Machine Learning Library. Engineers use the pandas library for data manipulation and analysis. It works amazingly well with structured data for machine learning algorithms. This is great for deploying ML and DL-based applications. Pandas assists in data reshaping, dataset joining, data filtration, alignment and manages missing data easily. It also provides a 2-D presentation of data to make things easier for Python developers.

6.NumPy: One of the most useful frameworks for machine learning engineers, NumPy developers are able to process multidimensional arrays. It is useful for Fourier transforms, linear algebra, and other mathematical functions. NumPy gives developers the ability to quickly add calculations to the solution. Complex functions can be easily performed all thanks to the power of NumPy for scientific and numerical computing.

In this blog I have tried to give a basic idea about some of the most popular libraries and frameworks of Python used in machine learning. We will try to find out more about this in the future. Many thanks to everyone for reading this blog.

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