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)

Installation and Setup

Open Anaconda Prompt and follow these steps to get your system ready for data science. We will create a new environment on Anaconda in which we will install all the required libraries and run our code:

  1. To create a new environment and install all the libraries, download the environment file from and run the following command:

    conda env create -f environment.yml

  2. To activate the environment, run this command:

    conda activate DataScience

    For this book, whenever you are asked to open a terminal, you need to open Anaconda Prompt, activate the environment, and then proceed.

  3. Jupyter Notebook allows us to run code and experiment in code blocks. To start Jupyter Notebook run the following inside the DataScience environment:

    jupyter notebook

    A new browser window will open with the Jupyter interface. You can then navigate to the project location and run the Jupyter Notebooks.