Machine learning is able to solve real-time problems. It has several algorithms such as classification, clustering, and more. To learn these essential algorithms, we require tools like Scikit and TensorFlow. Machine Learning Algorithms Using Scikit and TensorFlow Environments assists researchers in learning and implementing these critical algorithms. Covering key topics such as classification, artificial neural networks, prediction, random forest, and regression analysis, this premier reference source is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students.

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Puvvadi Baby Maruthi received Master of Computer Applications degree from JNTU Ananthapur in 2010, and the Ph.D degree from Sri Padmavati Mahila VisvaVidyalayam, Tirupati, in 2019. She is currently working as an Assistant Professor in Sri Venkateswara College of Engineering, Tirupati. She got gold medal in R programming in NPTEL. Her research interests include soft computing, information security, digital image processing and Machine Learning.

Amit Kumar Tyagi is Assistant Professor (Senior Grade), and Senior Researcher at Vellore Institute of Technology (VIT), Chennai Campus, India. He received his Ph.D. Degree in 2018 from Pondicherry Central University, India. He joined the Lord Krishna College of Engineering, Ghaziabad (LKCE) for the periods of 2009-2010, and 2012-2013.

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Machine Learning Algorithms Using Scikit and Tensorflow Environments

Maruthi, Puvvadi Baby (EDT); Prasad, Smrity (EDT); Tyagi, Amit Kumar (EDT)

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