Learning Data Mining With Python 2nd Edition Layton Robert by Layton, Robert 9781997100508, 1997100509 instant download after payment.

Key Features Use a wide variety of Python libraries for practical data mining
purposes. Learn how to find, manipulate, analyze, and visualize data using
Python. Step-by-step instructions on data mining techniques with Python that
have real-world applications. Book Description
This book teaches you to design and develop data mining applications using a
variety of datasets, starting with basic classification and affinity analysis.
This book covers a large number of libraries available in Python, including
the Jupyter Notebook, pandas, scikit-learn, and NLTK.
You will gain hands on experience with complex data types including text,
images, and graphs. You will also discover object detection using Deep Neural
Networks, which is one of the big, difficult areas of machine learning right
now.
With restructured examples and code samples updated for the latest edition of
Python, each chapter of this book introduces you to new algorithms and
techniques. By the end of the book, you will have great insights into using
Python for data mining and understanding of the algorithms as well as
implementations.
What you will learn Apply data mining concepts to real-world problems Predict
the outcome of sports matches based on past results Determine the author of a
document based on their writing style Use APIs to download datasets from
social media and other online services Find and extract good features from
difficult datasets Create models that solve real-world problems Design and
develop data mining applications using a variety of datasets Perform object
detection in images using Deep Neural Networks Find meaningful insights from
your data through intuitive visualizations Compute on big data, including
real-time data from the internet About the Author
**Robert Layton** is a data scientist investigating data-driven applications
to businesses across a number of sectors. He received a PhD investigating
cybercrime analytics from the Internet Commerce Security Laboratory at
Federation University Australia, before moving into industry, starting his own
data analytics company dataPipeline. Next, he created Eureaktive, which works
with tech-based startups on developing their proof-of-concepts and early-stage
prototypes. Robert also runs the LearningTensorFlow website, which is one of
the world's premier tutorial websites for Google's TensorFlow library.
Robert is an active member of the Python community, having used Python for
more than 8 years. He has presented at PyConAU for the last four years and
works with Python Charmers to provide Python-based training for businesses and
professionals from a wide range of organisations.
Robert can be best reached via Twitter @robertlayton
Table of Contents Getting Started with Data Mining Classifying with scikit-
learn Estimators Predicting Sports Winners with Decision Trees Recommending
Movies Using Affinity Analysis Features and scikit-learn Transformers Social
Media Insight using Naive Bayes Follow Recommendations Using Graph Mining
Beating CAPTCHAs with Neural Networks Authorship Attribution Clustering News
Articles Object Detection in Images using Deep Neural Networks Working with
Big Data Next Steps...
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