Book Image

Python Data Science Essentials [Video]

By : Alberto Boschetti, Luca Massaron
Book Image

Python Data Science Essentials [Video]

By: Alberto Boschetti, Luca Massaron

Overview of this book

<p>The Python Data Science Essentials video series takes you through all you need to know to succeed in data science using Python. Get insights into the core of Python data, including the latest versions of Jupyter Notebook, NumPy, Pandas and scikit-learn. In this course, you will delve into building your essential Python 3.6 data science toolbox, using a single-source approach that will allow to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and prepare for machine learning and visualization techniques.</p> <p>The code bundle for the video course is available at -&nbsp;<a href="https://github.com/PacktPublishing/Python-Data-Science-Essentials" target="_blank">https://github.com/PacktPublishing/Python-Data-Science-Essentials</a></p> <h1>Style and Approach</h1> <p>The course is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.</p>

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Python Data Science Essentials [Video]
Alberto Boschetti | Luca Massaron
Jun, 2018 | 3 hours 15 minutes
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Table of Contents (4 chapters)
Free Chapter
1
First Step
The Course Overview
Introducing Data Science and Python
Getting Ready
A Glance at the Essential Packages
Introducing the Jupyter Notebook
Scikit-learn Toy Datasets
2
Data Munging
Data Loading and Preprocessing
Working with Categorical and Text Data
Creating NumPy Arrays
NumPy's Fast Operations and Computations
3
The Data Pipeline
Introducing EDA
Building New Features
Dimensionality Reduction
The Detection and Treatment of Outliers
Validation Metrics
Testing and Validating
Cross-Validation
Hyperparameter Optimization
Feature Selection
Wrapping Everything in a Pipeline
4
Machine Learning
Preparing Tools and Datasets
Linear and Logistic Regression
Naive Bayes
K-Nearest Neighbors
An Overview of Unsupervised Learning
Chapter 3
The Data Pipeline
Content Locked
Section 7
Cross-Validation
Unfortunately, relying on the validation and testing phases of samples brings uncertainty along with a reduction of the learning examples dedicated to training. A solution would be to use cross-validation, and Scikit-learn offers a complete module for cross-validation and performance evaluation. Let’s take a step further and see how to use these in our code. - Use the three possible hypotheses for the digits dataset - Use cross-validation iterators - Perform sampling and bootstrapping
Previous Section
End of Section 7
Next Section