Machine learning libraries with python

Machine learning libraries with python

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  MACHINE LEARNING WITH PYTHON VISHAL BISHT CSE 4thyear
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  About Python • Itwas created by Guido van Roussum. • Python is an interpreted high level , general purpose programming language. • Python was conceived in the late 1980s as a successor to the ABC language. It was first released in 1991.
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  Why Python?
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  1.EASY TO LEARN Beginnerfriendly language .You don’t need to be a hardcore programmer. This independent language can also be called as one of the most flexible languages across different platforms and technologies. 2.VAST COMMUNITY The constant upgrade by the developer community support makes Python one of the most suitable languages for machine learning applications
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  3.DOCUMENTATION This language hasextensive tutorials and documentation. Readability is a primary focus for Python developers, in both project and code documentation. 4.VERSATILITY Versatile language supports object-oriented programming, structured programming, and functional programming patterns, etc. and can be applied not only in projects like machine learning. 5.FRAMEWORK AND LIBRARIES The language has a great number of machine learning libraries and some of the prominent libraries are such as TensorFlow, Pytorch, Matplotlib, SciKit Learn, etc.
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  • Machine learningis an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
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  1.NumPy NumPy is avery popular python library for large multi-dimensional array and matrix processing, with the help of a large collection of high-level mathematical functions. It is particularly useful for linear algebra, Fourier transform, and random number capabilities. High-end libraries like TensorFlow uses NumPy internally for manipulation of Tensors.
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  2.SciPy SciPy is avery popular library among Machine Learning enthusiasts as it contains different modules for optimization, linear algebra, integration and statistics. SciPy is also very useful for image manipulation. It is built on top of two basic Python libraries, viz., NumPy and SciPy Original Image Tinted image Resized tinted image
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  3.Matplotlib • It isa 2D plotting library used for creating 2D graphs and plots. A module named pyplot makes it easy for programmers for plotting as it provides features to control line styles, font properties, formatting axes, etc. It provides various kinds of graphs and plots for data visualization, viz., histogram, error charts, bar chats, etc,.
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  4.Pandas Pandas is apopular Python library for data analysis. As we know that the dataset must be prepared before training. It provides high-level data structures and wide variety tools for data analysis. It provides many inbuilt methods for groping, combining and filtering data. for eg.
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  5.OpenCV: Operations thatwe can perform with openCV library a. Reading an image : By using imread() fuction. b. Extracting the RGB values of a pixel. c. Extracting the Region of Interest (ROI):By slicing the pixel of image. d. Rotating the image : By generating a rotation matrix. e. Resizing the image :By using resize function. f. Drawing a Rectangle : By using rectangle function. g.Displaying text
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  Region of interestRGB Drawing a rectangle Rotating a image
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  Reference links https://www.geeksforgeeks.org/ https://mlait.in/2019/08/26/machine-learning- libraries-in-python/ https://analyticsindiamag.com/5-reasons-why- python-is-the-dominant-language-for- machine-learning/ https://en.wikipedia.org/wiki/Reference_counti ng#reference_cycle