Learn how to solve real life problems using different methods like logic regression, random forests and SVM's with TensorFlow.

Key Features

• Understand predictive analytics along with its challenges and best practices
• Embedded with assessments that will help you revise the concepts you have learned in this book

Book Description

Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. TensorFlow, Google's brainchild, is immensely popular and extensively used for predictive analysis.

This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow. This book will teach you predictive analytics for high-dimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using K-meansYou will then learn how to predict neighborhoods using K-means, and then, see another example of clustering audio clips based on their audio features.

This book is ideal for developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow.

This book is embedded with useful assessments that will help you revise the concepts you have learned in this book. This book is repurposed for this specific learning experience from material from Packt's Predictive Analytics with TensorFlow by Md. Rezaul Karim.

What you will learn

• Learn TensorFlow features in a real-life problem, followed by detailed TensorFlow installation and configuration
• Explore computation graphs, data, and programming models also get an insight into an example of implementing linear regression model for predictive analytics
• Solve the Titanic survival problem using logistic regression, random forests, and SVMs for predictive analytics
• Dig deeper into predictive analytics and find out how to take advantage of it to cluster records belonging to the certain group or class for a dataset of unsupervised observations
• Learn several examples of how to apply reinforcement learning algorithms for developing predictive models on real-life datasets

Who this book is for

This book is aimed at developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow.

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