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)


Cross-validation is a technique that helps data scientists evaluate their models on unseen data. It is helpful when your dataset isn't large enough to create three splits (training, testing, and validation). Cross-validation helps the model avoid overfitting by presenting it with different partitions of the same data. It works by feeding different training and validation sets of the dataset for every pass of cross-validation. 10-fold cross-validation is the most used, where the dataset is divided into 10 completely different subsets and is trained on each one of them, and finally, the metrics are averaged out to obtain the accurate prediction performance of the model. In every round of cross-validation, we do the following:

  1. Shuffle the dataset and split it into k different groups (k=10 for 10-fold cross-validation).
  2. Train the model on k-1 groups and test it on 1 group.
  3. Evaluate the model and store the results.
  4. Repeat steps 2 and 3 with different groups...