Data Science with Python

Data Science with Python

By : Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen

Buy this Book

Data Science with Python

By: Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen

Buy this Book

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.

About the Book

Minimum Hardware Requirements

Software Requirements

Installation and Setup

Using Kaggle for Faster Experimentation

Conventions

Installing the Code Bundle

Free Chapter

Introduction to Data Science and Data Pre-Processing

Introduction

Python Libraries

Roadmap for Building Machine Learning Models

Data in Different Scales

Data Discretization

Train and Test Data

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Performance Metrics

Summary

Data Visualization

Introduction

Functional Approach

Object-Oriented Approach Using Subplots

Summary

Introduction to Machine Learning via Scikit-Learn

Introduction

Introduction to Linear and Logistic Regression

Multiple Linear Regression

Logistic Regression

Max Margin Classification Using SVMs

Decision Trees

Random Forests

Summary

Dimensionality Reduction and Unsupervised Learning

Introduction

Hierarchical Cluster Analysis (HCA)

K-means Clustering

Principal Component Analysis (PCA)

Supervised Data Compression using Linear Discriminant Analysis (LDA)

Summary

Mastering Structured Data

Introduction

Boosting Algorithms

XGBoost Library

External Memory Usage

Cross-validation

Saving and Loading a Model

Neural Networks

Keras

Categorical Variables

Summary

Decoding Images

Introduction

Images

Convolutional Neural Networks

Image Data Preprocessing

Data Augmentation

Generators

Summary

Processing Human Language

Introduction

Text Data Processing

Recurrent Neural Networks (RNNs)

Summary

Tips and Tricks of the Trade

Introduction

Transfer Learning

Useful Tools and Tips

AutoML

Summary

Appendix

Chapter 1: Introduction to Data Science and Data Preprocessing

Chapter 2: Data Visualization

Chapter 3: Introduction to Machine Learning via Scikit-Learn

Chapter 4: Dimensionality Reduction and Unsupervised Learning

Chapter 5: Mastering Structured Data

Chapter 6: Decoding Images

Chapter 7: Processing Human Language

Chapter 8: Tips and Tricks of the Trade

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Supervised Learning

Supervised learning is a learning system that trains using labeled data (data in which the target variables are already known). The model learns how patterns in the feature matrix map to the target variables. When the trained machine is fed with a new dataset, it can use what it has learned to predict the target variables. This can also be called predictive modeling.

Supervised learning is broadly split into two categories. These categories are as follows:

Classification mainly deals with categorical target variables. A classification algorithm helps to predict which group or class a data point belongs to.

When the prediction is between two classes, it is known as binary classification. An example is predicting whether or not a customer will buy a product (in this case, the classes are yes and no).

If the prediction involves more than two target classes, it is known as multi-classification; for example, predicting all the items that a customer will buy.

Regression deals with numerical target variables. A regression algorithm predicts the numerical value of the target variable based on the training dataset.

Linear regression measures the link between one or more predictor variables and one outcome variable. For example, linear regression could help to enumerate the relative impacts of age, gender, and diet (the predictor variables) on height (the outcome variable).

Time series analysis, as the name suggests, deals with data that is distributed with respect to time, that is, data that is in a chronological order. Stock market prediction and customer churn prediction are two examples of time series data. Depending on the requirement or the necessities, time series analysis can be either a regression or classification task.

Data Science with Python

By : Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen

Data Science with Python

By: Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen

Overview of this book

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