PYTHON MACHINE LEARNING: The Ultimate Basic Guide For Beginners To Learn How To Design Types Of Automatic Production by Oliver R. Simpson
Requirements: .ePUB, .PDF, .MOBI/.AZW reader, 22 MB
Overview: The concepts of machine learning and machine learning algorithms can appear daunting and complex to most computer programming beginners.
Most of the information out there on various machine learning constitutes a description of a few pages, and yes, it is difficult to find time and energy to deal with such widespread detail. However, it is important to master the concepts of machine learning technology and learn how researchers are breaking the boundaries of data science to mimic human intelligence in machines using various machine learning algorithms.
Genre: Non-Fiction > Tech & Devices
– Some of the Highlights of the Book include:
• Master the concepts of artificial intelligence technology
• Learn how artificial intelligence technology is being applied in some of the most important industrial domains
• Learn the basic concepts and the definition of machine learning
• Overview of different types of machine learning algorithms along with the relationship between machine learning and Artificial Intelligence technology
• A thorough understanding of the concept of the "Statistical Learning" framework of machine learning has been provided
• Deep dive into a variety of supervised and unsupervised learning algorithms explained in exquisite detail
• Select algorithms have been explained with required mathematical equations for application in real life
• Learn how the “Artificial Neural Networks” or (ANN) have been developed with inspiration from the structure of the human brain
• Learn the 9 stages to create a data pipeline to build your own machine learning model
• Learn the basics of Python programming language and some of the key features that render it as the language of choice for coding beginners and advanced software programmers alike
• An overview of various renowned machine learning libraries such as "Scikit-Learn", "NumPy", "SciPy", "IPython", and "Pandas" among others.
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