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 4
Machine Learning
Content Locked
Section 5
An Overview of Unsupervised Learning
In all the methods we've seen so far, every sample or observation has its own target label or value. In some other cases, the dataset is unlabeled and, in order to extract the structure of the data, you need an unsupervised approach. In this video, we're going to introduce two methods to perform clustering, as they are among the most used methods for unsupervised learning. - Create the artificial datasets and represent them by a plot - Apply Kernel PCA - Apply DBSCAN
Previous Section
End of Chapter 4
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