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Machine learning is driving a new wave of change in the world. It has brought in many changes and development that makes it one of the most lucrative technologies of the world. If you are also planning to become a machine learning expert, you must also know about the various python libraries.
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Python Libraries for Machine Learning That You Should Know Session 1
Python Libraries for Machine Learning That You Should Know Machine learning is driving a new wave of change in the world. It has brought in many changes and development that makes it one of the most lucrative technologies of the world. If you are also planning to become a machine learning expert, you must also know about the various python libraries. In this blog, we cover the top 9 Python libraries.
Top 9 Python Libraries 1. NumPy- It is a general-purpose array-processing package. It comprises various mathematical functions, which makes it a highly useful python library. It can be used for random numbers, linear algebra, Fourier transforms and others. This is a highly functional library, and because of it, libraries like Tensor Flor use it at the backend. 2) SciPy- Python is a popular programming language, and because of this, Python developers have come up with python libraries that can be used for machine learning as well. An open community supports the development of this library. This library offers image optimisation modules, signal and image processing, social function, linear algebra, Fast Fourier transform, or Ordinary Differential Equation (ODE) solving.
3) Scikit-learn- NumPy and SciPy form the base for Scikit-learn, and it has become one of the most popular machine learning libraries. It has both supervised and unsupervised learning, which works on the interface of Python. You can use it for data analysis and data mining. It can handle the model selection, clustering, classification, and regression. 4) Theano- It can be used for evaluation and manipulating mathematical expression and matrix calculations. It is built on NumPy. What makes it one of the popular Python libraries is that it can easily take care of errors and bugs. It also has in-built tools for unit-testing and validation.
5) TensorFlow- It was developed for Google. It is a computational framework and supports different toolkits that are used for constructing models at different levels. It has. Flexible architecture and can run on different platforms like GPUs, CPUs and TPUs. 6) Keras-Neural network and machine learning makes use of this open-source library. It can easily run on CPU and GPU. It works with neural network. It has various free features which are used for wiring deep neural network code. 7) PyTorch- It has different tools which support machine learning, NLP and computer vision. It is easy to use and learn. Moreover, you can easily integrate the python data science stack. Using this, you can easily perform computations on Tensors. It also has multiple GPU support and custom data loaders.
8) Pandas- If you are looking for a tool that can be used for real-world data analysis, then this is the right tool for you. It is highly stable. Its backend code is written in C or Python. It has two types of data structures : 1. Series (1-dimensional) 2. DataFrame (2-dimensional) When combined together, they can handle a variety of data requirements. It finds use in fields of science, statistics, finance and analytics. 9) Matplotlib- This is a data visualization library used to produce a publication of quality image posts and figures. It helps in generating error charts, bar charts, graphs, scatter plots, and more. If you are willing to become a machine learning expert, you must know about these libraries. To know more about Python programming, you must connect with the Global Tech Council today.
