Dimensional reduction is usually used to reduce the number of variables that are to be considered in an machine learning project. It is often used where columns of data in a file have more than an acceptable number of missing values, have low variance, or are extremely variable in nature. Before attempting to reduce your data source by removing those unwanted columns, you need to be comfortable that this is the right thing to be doing. In other words, you want to make sure that the data you reduce does not create a bias in the remaining data. Profiling the data is an excellent way to determine whether the dimensional reduction of a particular column or columns is appropriate. Data profiling is a technique that is used to examine data to determine its accuracy and completeness. This is the process of examining a data source to uncover the erroneous sections...
Hands-On Machine Learning with IBM Watson
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Hands-On Machine Learning with IBM Watson
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Overview of this book
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.
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Table of Contents (15 chapters)
Preface
Introduction to IBM Cloud
Feature Extraction - A Bag of Tricks
Supervised Machine Learning Models for Your Data
Implementing Unsupervised Algorithms
Section 2: Tools and Ingredients for Machine Learning in IBM Cloud
Machine Learning Workouts on IBM Cloud
Using Spark with IBM Watson Studio
Deep Learning Using TensorFlow on the IBM Cloud
Section 3: Real-Life Complete Case Studies
Creating a Facial Expression Platform on IBM Cloud
The Automated Classification of Lithofacies Formation Using ML
Building a Cloud-Based Multibiometric Identity Authentication Platform
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