Now we know that neural networks are a special type of machine learning model. Although, usually these models need huge amounts of data to start outperforming other machine learning approaches, one big advantage is that the process of training neural networks can make use of parallelization in hardware such as graphical processing units (GPUs), which do the operations needed for training neural networks faster than traditional CPUs. This is the reason that in the past few years, new specialized software frameworks have been developed with the capacity to make use of GPUs; examples of these frameworks are Theano, Caffe, and TensorFlow. These frameworks have allowed the deep learning models to be used for professionals outside specialized academic circles, thus democratizing the use of these powerful models. In this section, we introduce the two...
Hands-On Predictive Analytics with Python
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Hands-On Predictive Analytics with Python
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Overview of this book
Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages.
The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model.
Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics.
By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming.
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Hands-On Predictive Analytics with Python
Table of Contents (11 chapters)
Preface
Free Chapter
The Predictive Analytics Process
Problem Understanding and Data Preparation
Dataset Understanding – Exploratory Data Analysis
Predicting Numerical Values with Machine Learning
Predicting Categories with Machine Learning
Introducing Neural Nets for Predictive Analytics
Model Evaluation
Model Tuning and Improving Performance
Implementing a Model with Dash
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