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Designed with beginners in mind, this workshop helps you make the
most of Python libraries and the Jupyter Notebook's functionality
to understand how data science can be applied to solve real-world
data problems. Key Features Gain useful insights into data science
and machine learning Explore the different functionalities and
features of a Jupyter Notebook Discover how Python libraries are
used with Jupyter for data analysis Book DescriptionFrom banking
and manufacturing through to education and entertainment, using
data science for business has revolutionized almost every sector in
the modern world. It has an important role to play in everything
from app development to network security. Taking an interactive
approach to learning the fundamentals, this book is ideal for
beginners. You'll learn all the best practices and techniques for
applying data science in the context of real-world scenarios and
examples. Starting with an introduction to data science and machine
learning, you'll start by getting to grips with Jupyter
functionality and features. You'll use Python libraries like
sci-kit learn, pandas, Matplotlib, and Seaborn to perform data
analysis and data preprocessing on real-world datasets from within
your own Jupyter environment. Progressing through the chapters,
you'll train classification models using sci-kit learn, and assess
model performance using advanced validation techniques. Towards the
end, you'll use Jupyter Notebooks to document your research, build
stakeholder reports, and even analyze web performance data. By the
end of The Applied Data Science Workshop, you'll be prepared to
progress from being a beginner to taking your skills to the next
level by confidently applying data science techniques and tools to
real-world projects. What you will learn Understand the key
opportunities and challenges in data science Use Jupyter for data
science tasks such as data analysis and modeling Run exploratory
data analysis within a Jupyter Notebook Visualize data with
pairwise scatter plots and segmented distribution Assess model
performance with advanced validation techniques Parse HTML
responses and analyze HTTP requests Who this book is forIf you are
an aspiring data scientist who wants to build a career in data
science or a developer who wants to explore the applications of
data science from scratch and analyze data in Jupyter using Python
libraries, then this book is for you. Although a brief
understanding of Python programming and machine learning is
recommended to help you grasp the topics covered in the book more
quickly, it is not mandatory.
A hands-on guide to deep learning that's filled with intuitive
explanations and engaging practical examples Key Features Designed
to iteratively develop the skills of Python users who don't have a
data science background Covers the key foundational concepts you'll
need to know when building deep learning systems Full of
step-by-step exercises and activities to help build the skills that
you need for the real-world Book DescriptionTaking an approach that
uses the latest developments in the Python ecosystem, you'll first
be guided through the Jupyter ecosystem, key visualization
libraries and powerful data sanitization techniques before we train
our first predictive model. We'll explore a variety of approaches
to classification like support vector networks, random decision
forests and k-nearest neighbours to build out your understanding
before we move into more complex territory. It's okay if these
terms seem overwhelming; we'll show you how to put them to work.
We'll build upon our classification coverage by taking a quick look
at ethical web scraping and interactive visualizations to help you
professionally gather and present your analysis. It's after this
that we start building out our keystone deep learning application,
one that aims to predict the future price of Bitcoin based on
historical public data. By guiding you through a trained neural
network, we'll explore common deep learning network architectures
(convolutional, recurrent, generative adversarial) and branch out
into deep reinforcement learning before we dive into model
optimization and evaluation. We'll do all of this whilst working on
a production-ready web application that combines Tensorflow and
Keras to produce a meaningful user-friendly result, leaving you
with all the skills you need to tackle and develop your own
real-world deep learning projects confidently and effectively. What
you will learn Discover how you can assemble and clean your very
own datasets Develop a tailored machine learning classification
strategy Build, train and enhance your own models to solve unique
problems Work with production-ready frameworks like Tensorflow and
Keras Explain how neural networks operate in clear and simple terms
Understand how to deploy your predictions to the web Who this book
is forIf you're a Python programmer stepping into the world of data
science, this is the ideal way to get started.
Getting started with data science doesn't have to be an uphill
battle. This step-by-step guide is ideal for beginners who know a
little Python and are looking for a quick, fast-paced introduction.
Key Features Get up and running with the Jupyter ecosystem and some
example datasets Learn about key machine learning concepts like
SVM, KNN classifiers and Random Forests Discover how you can use
web scraping to gather and parse your own bespoke datasets Book
DescriptionGet 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 learn Get up and running with the Jupyter ecosystem and
some example datasets Learn about key machine learning concepts
like SVM, KNN classifiers, and Random Forests Plan a machine
learning classification strategy and train classification, models
Use validation curves and dimensionality reduction to tune and
enhance your models Discover how you can use web scraping to gather
and parse your own bespoke datasets Scrape tabular data from web
pages and transform them into Pandas DataFrames Create interactive,
web-friendly visualizations to clearly communicate your findings
Who this book is forThis 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, Matplotlib and Pandas providing you a useful
head start.
Become the master player of data exploration by creating
reproducible data processing pipelines, visualizations, and
prediction models for your applications. Key Features Get up and
running with the Jupyter ecosystem and some example datasets Learn
about key machine learning concepts such as SVM, KNN classifiers,
and Random Forests Discover how you can use web scraping to gather
and parse your own bespoke datasets Book DescriptionGetting started
with data science doesn't have to be an uphill battle. Applied Data
Science with Python and Jupyter is a step-by-step guide ideal for
beginners who know a little Python and are looking for a quick,
fast-paced introduction to these concepts. In this book, you'll
learn every aspect of the standard data workflow process, including
collecting, cleaning, investigating, visualizing, and modeling
data. You'll start with the basics of Jupyter, which will be the
backbone of the book. After familiarizing ourselves with its
standard features, you'll look at an example of it in practice with
our first analysis. In the next lesson, you dive right into
predictive analytics, where multiple classification algorithms are
implemented. Finally, the book ends by looking at data collection
techniques. You'll see how web data can be acquired with scraping
techniques and via APIs, and then briefly explore interactive
visualizations. What you will learn Get up and running with the
Jupyter ecosystem Identify potential areas of investigation and
perform exploratory data analysis Plan a machine learning
classification strategy and train classification models Use
validation curves and dimensionality reduction to tune and enhance
your models Scrape tabular data from web pages and transform it
into Pandas DataFrames Create interactive, web-friendly
visualizations to clearly communicate your findings Who this book
is forApplied Data Science with Python and Jupyter is ideal for
professionals with a variety of job descriptions across a 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 such as Pandas, Matplotlib, and
Pandas providing you a useful head start.
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