Название: Machine Learning Algorithms Using Scikit and TensorFlow Environments
Автор: Puvvadi Baby Maruthi, Smrity Prasad, Amit Kumar Tyagi
Издательство: IGI Global
Год: 2024
Страниц: 473
Язык: английский
Формат: pdf (true)
Размер: 14.2 MB

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.

Classification Models in Machine Learning Techniques: Classification is the process of identifying, understanding, and grouping objects and ideas into specified categories. These pre-categorized training datasets are used by machine learning techniques to classify datasets into relevant and acceptable categories. Using the incoming training data, Machine Learning classifiers assess the chance or probability that the incoming data will fall into one of the established categories. One of categorization’s most prominent applications is used by the largest email service providers of today: classifying emails as “spam” or “non-spam.” In essence, classification is a form of “pattern recognition.” Following the application of classification algorithms to the training data, the same pattern (similar number sequences, words, or attitudes, etc.) is found in future data sets. Classification falls within the category of supervised learning in the context of Machine Learning.


Machine Learning plays a vital role in all major sectors like healthcare, banking, finance, and marketing. There is a need to understand the role and working of ML algorithms in a better way. Google also uses a learning algorithm to rank the web pages whenever we try to browse the internet to get the desired information. Understanding the platform and working of these algorithms is crucial for researchers. In the Chapter 2, the authors have presented an overview of Machine Learning fundamentals and the working of these algorithms with suitable examples. They have also highlighted the importance of major Machine Learning libraries like TensorFlow and SciKit in developing and deploying vast applications. Finally, a case study of ML application is presented to better understand the concept. Future prospects of ML applications are also depicted in detail.

In academia and business, deep-learning-based models have exhibited extraordinary performance over the last decade. The learning potential of Convolutional Neural Networks (CNNs) derives from a combination of several feature extraction levels that completely use a vast quantity of input. CNN is an important technique for tackling computer vision issues, although the theories behind its processing efficacy are not yet completely understood. CNN has achieved cutting-edge performance on a variety of datasets in computer vision applications like remote sensing, medical image categorization, facial detection, and object identification. This is due to the efficiency with which they process visual features. The Chapter 3 presents the most significant advancements in CNN for efficient processing in computer vision, including convolutional layer configurations, pooling layer approaches, network activation functions, loss functions, normalization approaches, and CNN optimization techniques.





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