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Books > Computing & IT > General theory of computing
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scikit-learn : Machine Learning Simplified (Paperback)
Loot Price: R2,444
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scikit-learn : Machine Learning Simplified (Paperback)
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Implement scikit-learn into every step of the data science pipeline
About This Book * Use Python and scikit-learn to create intelligent
applications * Discover how to apply algorithms in a variety of
situations to tackle common and not-so common challenges in the
machine learning domain * A practical, example-based guide to help
you gain expertise in implementing and evaluating machine learning
systems using scikit-learn Who This Book Is For If you are a
programmer and want to explore machine learning and data-based
methods to build intelligent applications and enhance your
programming skills, this is the course for you. No previous
experience with machine-learning algorithms is required. What You
Will Learn * Review fundamental concepts including supervised and
unsupervised experiences, common tasks, and performance metrics *
Classify objects (from documents to human faces and flower species)
based on some of their features, using a variety of methods from
Support Vector Machines to Naive Bayes * Use Decision Trees to
explain the main causes of certain phenomena such as passenger
survival on the Titanic * Evaluate the performance of machine
learning systems in common tasks * Master algorithms of various
levels of complexity and learn how to analyze data at the same time
* Learn just enough math to think about the connections between
various algorithms * Customize machine learning algorithms to fit
your problem, and learn how to modify them when the situation calls
for it * Incorporate other packages from the Python ecosystem to
munge and visualize your dataset * Improve the way you build your
models using parallelization techniques In Detail Machine learning,
the art of creating applications that learn from experience and
data, has been around for many years. Python is quickly becoming
the go-to language for analysts and data scientists due to its
simplicity and flexibility; moreover, within the Python data space,
scikit-learn is the unequivocal choice for machine learning. The
course combines an introduction to some of the main concepts and
methods in machine learning with practical, hands-on examples of
real-world problems. The course starts by walking through different
methods to prepare your data-be it a dataset with missing values or
text columns that require the categories to be turned into
indicator variables. After the data is ready, you'll learn
different techniques aligned with different objectives-be it a
dataset with known outcomes such as sales by state, or more
complicated problems such as clustering similar customers. Finally,
you'll learn how to polish your algorithm to ensure that it's both
accurate and resilient to new datasets. You will learn to
incorporate machine learning in your applications. Ranging from
handwritten digit recognition to document classification, examples
are solved step-by-step using scikit-learn and Python. By the end
of this course you will have learned how to build applications that
learn from experience, by applying the main concepts and techniques
of machine learning. Style and Approach Implement scikit-learn
using engaging examples and fun exercises, and with a gentle and
friendly but comprehensive "learn-by-doing" approach. This is a
practical course, which analyzes compelling data about life,
health, and death with the help of tutorials. It offers you a
useful way of interpreting the data that's specific to this course,
but that can also be applied to any other data. This course is
designed to be both a guide and a reference for moving beyond the
basics of scikit-learn.
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