Learn and build complete machine learning systems with offerings of IBM Cloud and Watson Machine learning services
Key Features
- Understand key characteristics of the IBM machine learning services
- Data representation and feature extraction in any machine learning system
- Run supervised and unsupervised techniques in the cloud
- Learn to create a Spark pipeline in Watson Studio
- Implement deep learning and neural networks on the IBM cloud with Tensorflow
- Create a complete cloud-based facial expression classification solution
- Use biometrics traits to build cloud-based human identification system
Book Description
This book serves as a complete guide to get well-versed with machine learning on the IBM Cloud Using Python. You will learn to build complete machine learning solutions with a focus on the role of data representation and feature extraction.
This book will start with supervised and unsupervised machine learning concepts with an overview of IBM Cloud and 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 with the real-world examples. You will learn to create a Spark pipeline in Watson Studio. The book will also guide you to deep learning and neural networks principles on the IBM cloud with Tensorflow. You will learn to build chatbot using NLP techniques. Later you will cover three powerful case studies such as the facial expression classification platform, Automated Classification of Lithofacies, and Multi-biometric identity authentication platform to get well-versed with the methodologies.
By the end of the book, the reader will be well-positioned to build efficient machine learning solutions on the IBM cloud. You will be well-equipped with real-world examples to draw insights from the data at hand.
What you will learn
- Understand key characteristics of the IBM machine learning services
- Data representation and feature extraction in any machine learning system
- Run supervised and unsupervised techniques in the cloud
- Learn to create a Spark pipeline in Watson Studio
- Implement deep learning and neural networks on the IBM cloud with Tensorflow
- Create a complete cloud-based facial expression classification solution
- Use biometrics traits to build cloud-based human identification system
Who This Book Is For
This book is for Data Scientists and Machine Learning Engineers who would like to get introduced to the IBM Cloud and its Machine learning services using practical examples. Basic Python knowledge and limited understanding of Machine Learning will be beneficial.
James Miller is an innovator and accomplished Sr. Project Lead and Solution Architect with 37 years experience. of extensive design and development across multiple platforms and technologies. Roles include leveraging his consulting experience to provide hands-on leadership in all phases of advanced analytics and related technology projects, providing recommendations for process improvement, report accuracy, adoption of disruptive technologies, enablement, and insight identification. Author: Statistics for Data Science, Mastering Predictive Analytics w/R, Big Data Visualization, Learning Watson Analytics, Implementing Splunk, Mastering Splunk, 5 Guiding Principles of a Successful Center of Excellence, and TM1 Developer's Certification Guide.
