Beginning Data Analysis with Python And Jupyter Use powerful industry–standard tools to unlock new

Key FeaturesGet up and running with the Jupyter ecosystem and some example datasetsLearn about key machine learning concepts like SVM, KNN classifiers and Random ForestsDiscover how you can use web scraping to gather and parse your own bespoke datasetsBook Description
Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context.
What you will learnIdentify potential areas of investigation and perform exploratory data analysisPlan a machine learning classification strategy and train classification modelsUse validation curves and dimensionality reduction to tune and enhance your modelsScrape tabular data from web pages and transform it into Pandas DataFramesCreate interactive, web-friendly visualizations to clearly communicate your findingsWho This Book Is For
This book is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries like Pandas, MatDescriptionlib and Pandas providing you a useful head start.
Table of ContentsJupyter FundamentalsData Cleaning and Advanced Machine LearningWeb Scraping and Interactive Visualizations