Book description
Leverage the power of the Python data science libraries and advanced machine learning techniques to analyse large unstructured datasets and predict the occurrence of a particular future event.
Key Features
 Explore the depths of data science, from data collection through to visualization
 Learn pandas, scikitlearn, and Matplotlib in detail
 Study various data science algorithms using realworld datasets
Book Description
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 chapters, you will study 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, study 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.
What you will learn
 Preprocess data to make it ready to use for machine learning
 Create data visualizations with Matplotlib
 Use scikitlearn to perform dimension reduction using principal component analysis (PCA)
 Solve classification and regression problems
 Get predictions using the XGBoost library
 Process images and create machine learning models to decode them
 Process human language for prediction and classification
 Use TensorBoard to monitor training metrics in real time
 Find the best hyperparameters for your model with AutoML
Who this book is for
Data Science with Python is designed for data analysts, data scientists, database engineers, and business analysts who want to move towards using Python and machine learning techniques to analyze data and predict outcomes. Basic knowledge of Python and data analytics will prove beneficial to understand the various concepts explained through this book.
Downloading the example code for this ebook: You can download the example code files for this ebook on GitHub at the following link: https://github.com/TrainingByPackt/DataSciencewithPython. If you require support please email: customercare@packt.com
Table of contents
 Preface
 Chapter 1

Introduction to Data Science and Data PreProcessing
 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
 Chapter 2

Data Visualization
 Introduction

Functional Approach
 Exercise 13: Functional Approach – Line Plot
 Exercise 14: Functional Approach – Add a Second Line to the Line Plot
 Activity 2: Line Plot
 Exercise 15: Creating a Bar Plot
 Activity 3: Bar Plot
 Exercise 16: Functional Approach – Histogram
 Exercise 17: Functional Approach – BoxandWhisker plot
 Exercise 18: Scatterplot
 ObjectOriented Approach Using Subplots
 Summary
 Chapter 3

Introduction to Machine Learning via ScikitLearn
 Introduction
 Introduction to Linear and Logistic Regression
 Multiple Linear Regression

Logistic Regression
 Exercise 25: Fitting a Logistic Regression Model and Determining the Intercept and Coefficients
 Exercise 26: Generating Predictions and Evaluating the Performance of a Logistic Regression Model
 Exercise 27: Tuning the Hyperparameters of a Multiple Logistic Regression Model
 Activity 6: Generating Predictions and Evaluating Performance of a Tuned Logistic Regression Model
 Max Margin Classification Using SVMs

Decision Trees
 Activity 8: Preparing Data for a Decision Tree Classifier
 Exercise 30: Tuning a Decision Tree Classifier Using Grid Search
 Exercise 31: Programmatically Extracting Tuned Hyperparameters from a Decision Tree Classifier Grid Search Model
 Activity 9: Generating Predictions and Evaluating the Performance of a Decision Tree Classifier Model

Random Forests
 Exercise 32: Preparing Data for a Random Forest Regressor
 Activity 10: Tuning a Random Forest Regressor
 Exercise 33: Programmatically Extracting Tuned Hyperparameters and Determining Feature Importance from a Random Forest Regressor Grid Search Model
 Activity 11: Generating Predictions and Evaluating the Performance of a Tuned Random Forest Regressor Model
 Summary
 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

Appendix
 Chapter 1: Introduction to Data Science and Data Preprocessing
 Chapter 2: Data Visualization

Chapter 3: Introduction to Machine Learning via ScikitLearn
 Activity 5: Generating Predictions and Evaluating the Performance of a Multiple Linear Regression Model
 Activity 6: Generating Predictions and Evaluating Performance of a Tuned Logistic Regression Model
 Activity 7: Generating Predictions and Evaluating the Performance of the SVC Grid Search Model
 Activity 8: Preparing Data for a Decision Tree Classifier
 Activity 9: Generating Predictions and Evaluating the Performance of a Decision Tree Classifier Model
 Activity 10: Tuning a Random Forest Regressor
 Activity 11: Generating Predictions and Evaluating the Performance of a Tuned Random Forest Regressor Model
 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
Product information
 Title: Data Science with Python
 Author(s):
 Release date: July 2019
 Publisher(s): Packt Publishing
 ISBN: 9781838552862
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