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)


The digital cameras that we have today store images as a big matrix of numbers. These are what we call digital images. A single number on this matrix refers to a single pixel in the image. Individual numbers refer to the intensity of the color at that pixel. For a grayscale image, these values vary from 0 to 255, where 0 is black and 255 is white. For a colored image, this matrix is three-dimensional, where each dimension has values for red, green, and blue. The values in the matrices refer to the intensities of the respective colors. We use these values as input to our computer vision programs or data science models to perform predictions and recognitions.

Now, there are two ways for us to create machine learning models using these pixels:

  • Input individual pixels as different input variables to the neural network
  • Use a convolutional neural network

Creating a fully connected neural network that takes individual pixel values as input variables is the easiest and the most...