Book Image

Data Science with Python

By : Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen
Book Image

Data Science with Python

By: Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen

Overview of this book

Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression. As you make your way through the book, you will understand the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, discover how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome. By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.
Table of Contents (10 chapters)


Now that you have created multiple neural network models, you understand that there are two main components that go into creating well-performing networks. They are as follows:

  • The architecture of the neural network
  • The hyperparameters of the neural network

Depending on the problem, it could take tens of iterations to get to the best possible network. So far, we have been creating architectures and tuning the hyperparameters manually. AutoML can help us perform these tasks. It searches for the most optimal network and parameters for the dataset at hand. Auto-Keras is an open source library that helps us implement AutoML on Keras. Let's learn about how to use Auto-Keras with the help of an exercise.

Exercise 59: Get a Well-Performing Network Using Auto-Keras

In this exercise, we will make use of the Auto-Keras library to find the most optimal network and parameters for the cats-vs-dogs dataset (