This is the code repository for Hands-On Machine Learning with IBM Watson, published by Packt.
Leverage IBM Watson to implement machine learning techniques and algorithms using Python
IBM Cloud is a collection of cloud computing services for data analytics using machine learning and artificial intelligence (AI). This book is a complete guide to help you become well versed with machine learning on the IBM Cloud using Python.
Hands-On Machine Learning with IBM Watson starts with supervised and unsupervised machine learning concepts, in addition to providing you with an overview of IBM Cloud and Watson Machine Learning. You'll gain insights into running various techniques, such as K-means clustering, K-nearest neighbor (KNN), and time series prediction in IBM Cloud with real-world examples. The book will then help you delve into creating a Spark pipeline in Watson Studio. You will also be guided through deep learning and neural network principles on the IBM Cloud using TensorFlow. With the help of NLP techniques, you can then brush up on building a chatbot. In later chapters, you will cover three powerful case studies, including the facial expression classification platform, the automated classification of lithofacies, and the multi-biometric identity authentication platform, helping you to become well versed with these methodologies.
By the end of this book, you will be ready to build efficient machine learning solutions on the IBM Cloud and draw insights from the data at hand using real-world examples.
This book covers the following exciting features:
- Implement data science and machine learning techniques to draw insights from real-world data
- Understand what IBM Cloud platform can help you to implement cognitive insights within applications
- Understand the role of data representation and feature extraction in any machine learning system
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
to_drop = ['points']
df_data_1.drop(to_drop, inplace=True, axis=1)
df_data_1.head()
Following is what you need for this book: This beginner-level book is for data scientists and machine learning engineers who want to get started with IBM Cloud and its machine learning services using practical examples. Basic knowledge of Python and some understanding of machine learning will be useful.
With the following software and hardware list you can run all code files present in the book (Chapter 1-10).
| Chapter | Software required | OS required |
|---|---|---|
| All | IBM Cloud | Windows |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
James D. Miller is an innovator and accomplished senior 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, the adoption of disruptive technologies, enablement, and insight identification. He has also written a number of books, including Statistics for Data Science; Mastering Predictive Analytics with R, Second Edition; Big Data Visualization; Learning Watson Analytics; and many more.
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