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

About the Authors 

Rohan Chopra graduated from Vellore Institute of Technology with a bachelor's degree in Computer Science. Rohan has experience of more than 2 years in designing, implementing, and optimizing end-to-end deep neural network systems. His research is centered around using deep learning to solve computer vision-related problems and has hands-on experience of working on self-driving cars. He is a data scientist at Absolutdata.

Acknowledgements: 

"This book was written by me, Rohan Chopra, and co-authored by Aaron England and Mohamed Noordeen Alaudeen. Big thanks to my mentor, Sanjiban Sekhar Roy, for his support; also, thanks to all the team at Packt."

Aaron England earned a PhD from the University of Utah in Exercise and Sports Science with a cognate in Biostatistics. Currently, he resides in Scottsdale, Arizona, where he works as a data scientist at Natural Partners Fullscript.

Mohamed Noordeen Alaudeen is a lead data scientist at Logitech. Noordeen has more than 7 years of experience in building and developing end-toend big data and deep neural network systems. It all started when he decided to engage in data science for the rest of his life.

He is seasoned data science and big data trainer with both Imarticus Learning and Great Learning, which are two of the renowned data science institutes in India. Apart from his teaching, he does contribute his work to open-source. He has over 90+ repositories on GitHub, which have open-sourced his technical work and data science material. He is an active influencer( with over 22,000+ connections) on Linkedin, helping the data science community.

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