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)

Regularization

Regularization is a technique that helps machine learning models generalize better by making modifications in the learning algorithm. This helps prevent overfitting and helps our model work better on data that it hasn't seen during training. In this section, we will learn about the different regularizers available to us.

Dropout Layer

Dropout is a regularization technique that we use to prevent overfitting in our neural network models. We ignore randomly selected neurons from the network while training. This prevents the activations of those neurons continuing down the line, and the weight updates are not applied to them during back propagation. The weights of neurons are tuned to identify specific features; neurons that neighbor them become dependent on this, which can lead to overfitting because these neurons can get specialized to the training data. When neurons are randomly dropped, the neighboring neurons step in and learn the representation, leading to multiple different...