This book serves as a complete guide to becoming well-versed in machine learning on IBM Cloud using Python. You will learn how to build complete machine learning solutions, focusing on the role of data representation and feature extraction.
This book starts with supervised and unsupervised machine learning concepts, including an overview of IBM Cloud and the Watson Machine Learning service. You will learn how to run various techniques, such as k-means clustering, KNN, time series prediction, visual recognition, and text-to-speech in IBM Cloud by means of real-world examples. You will learn how to create a Spark pipeline in Watson Studio. The book will also guide you in terms of deep learning and neural network principles on IBM Cloud with TensorFlow. You will learn how to build chatbots using NLP techniques. Later, you will cover three powerful case studies – the facial expression classification platform, the automated classification of lithofacies, and the multibiometric identity authentication platform – with a view to becoming well-versed in the methodologies.
By the end of the book, you will be well-positioned to build efficient machine learning solutions on IBM Cloud. You will also be well-equipped with real-world examples to draw insights from the data at hand.
