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.

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Table of Contents (10 chapters)
About the Book
About the Book
About the Authors
Learning Objectives
Audience
Approach
Minimum Hardware Requirements
Software Requirements
Installation and Setup
Using Kaggle for Faster Experimentation
Conventions
Installing the Code Bundle
Free Chapter
1
Introduction to Data Science and Data Pre-Processing
Introduction to Data Science and Data Pre-Processing
Introduction
Python Libraries
Roadmap for Building Machine Learning Models
Data Representation
Data Cleaning
Data Integration
Data Transformation
Data in Different Scales
Data Discretization
Train and Test Data
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Performance Metrics
Summary
2
Data Visualization
Data Visualization
Introduction
Functional Approach
Object-Oriented Approach Using Subplots
Summary
3
Introduction to Machine Learning via Scikit-Learn
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
4
Dimensionality Reduction and Unsupervised Learning
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
5
Mastering Structured Data
Mastering Structured Data
Introduction
Boosting Algorithms
XGBoost Library
External Memory Usage
Cross-validation
Saving and Loading a Model
Neural Networks
Keras
Categorical Variables
Summary
6
Decoding Images
Decoding Images
Introduction
Images
Convolutional Neural Networks
Pooling Layer
Adam Optimizer
Cross-entropy Loss
Regularization
Image Data Preprocessing
Data Augmentation
Generators
Summary
7
Processing Human Language
Processing Human Language
Introduction
Text Data Processing
Recurrent Neural Networks (RNNs)
Summary
8
Tips and Tricks of the Trade
Tips and Tricks of the Trade
Introduction
Transfer Learning
Useful Tools and Tips
AutoML
Summary
Appendix
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

Hierarchical Cluster Analysis (HCA)

Hierarchical cluster analysis (HCA) is best implemented when the user does not have a priori number of clusters to build. Thus, it is a common approach to use HCA as a precursor to other clustering techniques where a predetermined number of clusters is recommended. HCA works by merging observations that are similar into clusters and continues merging clusters that are closest in proximity until all observations are merged into a single cluster.

HCA determines similarity as the Euclidean distance between and among observations and creates links at the distance in which the two points lie.

With the number of features indicated by n, the Euclidean distance is calculated using the formula:

Figure 4.1: The Euclidean distance
Figure 4.1: The Euclidean distance

After the distance between observations and cluster have been calculated, the relationships between and among all observations are displayed using a dendrogram. Dendrograms are tree-like structures displaying horizontal...

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