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Learn to solve challenging data science problems by building
powerful machine learning models using Python About This Book *
Understand which algorithms to use in a given context with the help
of this exciting recipe-based guide * This practical tutorial
tackles real-world computing problems through a rigorous and
effective approach * Build state-of-the-art models and develop
personalized recommendations to perform machine learning at scale
Who This Book Is For This Learning Path is for Python programmers
who are looking to use machine learning algorithms to create
real-world applications. It is ideal for Python professionals who
want to work with large and complex datasets and Python developers
and analysts or data scientists who are looking to add to their
existing skills by accessing some of the most powerful recent
trends in data science. Experience with Python, Jupyter Notebooks,
and command-line execution together with a good level of
mathematical knowledge to understand the concepts is expected.
Machine learning basic knowledge is also expected. What You Will
Learn * Use predictive modeling and apply it to real-world problems
* Understand how to perform market segmentation using unsupervised
learning * Apply your new-found skills to solve real problems,
through clearly-explained code for every technique and test *
Compete with top data scientists by gaining a practical and
theoretical understanding of cutting-edge deep learning algorithms
* Increase predictive accuracy with deep learning and scalable
data-handling techniques * Work with modern state-of-the-art
large-scale machine learning techniques * Learn to use Python code
to implement a range of machine learning algorithms and techniques
In Detail Machine learning is increasingly spreading in the modern
data-driven world. It is used extensively across many fields such
as search engines, robotics, self-driving cars, and more. Machine
learning is transforming the way we understand and interact with
the world around us. In the first module, Python Machine Learning
Cookbook, you will learn how to perform various machine learning
tasks using a wide variety of machine learning algorithms to solve
real-world problems and use Python to implement these algorithms.
The second module, Advanced Machine Learning with Python, is
designed to take you on a guided tour of the most relevant and
powerful machine learning techniques and you'll acquire a broad set
of powerful skills in the area of feature selection and feature
engineering. The third module in this learning path, Large Scale
Machine Learning with Python, dives into scalable machine learning
and the three forms of scalability. It covers the most effective
machine learning techniques on a map reduce framework in Hadoop and
Spark in Python. This Learning Path will teach you Python machine
learning for the real world. The machine learning techniques
covered in this Learning Path are at the forefront of commercial
practice. This Learning Path combines some of the best that Packt
has to offer in one complete, curated package. It includes content
from the following Packt products: * Python Machine Learning
Cookbook by Prateek Joshi * Advanced Machine Learning with Python
by John Hearty * Large Scale Machine Learning with Python by
Bastiaan Sjardin, Alberto Boschetti, Luca Massaron Style and
approach This course is a smooth learning path that will teach you
how to get started with Python machine learning for the real world,
and develop solutions to real-world problems. Through this
comprehensive course, you'll learn to create the most effective
machine learning techniques from scratch and more!
Leverage benefits of machine learning techniques using Python About
This Book * Improve and optimise machine learning systems using
effective strategies. * Develop a strategy to deal with a large
amount of data. * Use of Python code for implementing a range of
machine learning algorithms and techniques. Who This Book Is For
This title is for data scientist and researchers who are already
into the field of data science and want to see machine learning in
action and explore its real-world application. Prior knowledge of
Python programming and mathematics is must with basic knowledge of
machine learning concepts. What You Will Learn * Learn to write
clean and elegant Python code that will optimize the strength of
your algorithms * Uncover hidden patterns and structures in data
with clustering * Improve accuracy and consistency of results using
powerful feature engineering techniques * Gain practical and
theoretical understanding of cutting-edge deep learning algorithms
* Solve unique tasks by building models * Get grips on the machine
learning design process In Detail Machine learning and predictive
analytics are becoming one of the key strategies for unlocking
growth in a challenging contemporary marketplace. It is one of the
fastest growing trends in modern computing, and everyone wants to
get into the field of machine learning. In order to obtain
sufficient recognition in this field, one must be able to
understand and design a machine learning system that serves the
needs of a project. The idea is to prepare a learning path that
will help you to tackle the real-world complexities of modern
machine learning with innovative and cutting-edge techniques. Also,
it will give you a solid foundation in the machine learning design
process, and enable you to build customized machine learning models
to solve unique problems. The course begins with getting your
Python fundamentals nailed down. It focuses on answering the right
questions that cove a wide range of powerful Python libraries,
including scikit-learn Theano and Keras.After getting familiar with
Python core concepts, it's time to dive into the field of data
science. You will further gain a solid foundation on the machine
learning design and also learn to customize models for solving
problems. At a later stage, you will get a grip on more advanced
techniques and acquire a broad set of powerful skills in the area
of feature selection and feature engineering. Style and approach
This course includes all the resources that will help you jump into
the data science field with Python. The aim is to walk through the
elements of Python covering powerful machine learning libraries.
This course will explain important machine learning models in a
step-by-step manner. Each topic is well explained with real-world
applications with detailed guidance.Through this comprehensive
guide, you will be able to explore machine learning techniques.
Solve challenging data science problems by mastering cutting-edge
machine learning techniques in Python About This Book * Resolve
complex machine learning problems and explore deep learning * Learn
to use Python code for implementing a range of machine learning
algorithms and techniques * A practical tutorial that tackles
real-world computing problems through a rigorous and effective
approach Who This Book Is For This title is for Python developers
and analysts or data scientists who are looking to add to their
existing skills by accessing some of the most powerful recent
trends in data science. If you've ever considered building your own
image or text-tagging solution, or of entering a Kaggle contest for
instance, this book is for you! Prior experience of Python and
grounding in some of the core concepts of machine learning would be
helpful. What You Will Learn * Compete with top data scientists by
gaining a practical and theoretical understanding of cutting-edge
deep learning algorithms * Apply your new found skills to solve
real problems, through clearly-explained code for every technique
and test * Automate large sets of complex data and overcome
time-consuming practical challenges * Improve the accuracy of
models and your existing input data using powerful feature
engineering techniques * Use multiple learning techniques together
to improve the consistency of results * Understand the hidden
structure of datasets using a range of unsupervised techniques *
Gain insight into how the experts solve challenging data problems
with an effective, iterative, and validation-focused approach *
Improve the effectiveness of your deep learning models further by
using powerful ensembling techniques to strap multiple models
together In Detail Designed to take you on a guided tour of the
most relevant and powerful machine learning techniques in use today
by top data scientists, this book is just what you need to push
your Python algorithms to maximum potential. Clear examples and
detailed code samples demonstrate deep learning techniques,
semi-supervised learning, and more - all whilst working with
real-world applications that include image, music, text, and
financial data. The machine learning techniques covered in this
book are at the forefront of commercial practice. They are
applicable now for the first time in contexts such as image
recognition, NLP and web search, computational creativity, and
commercial/financial data modeling. Deep Learning algorithms and
ensembles of models are in use by data scientists at top tech and
digital companies, but the skills needed to apply them
successfully, while in high demand, are still scarce. This book is
designed to take the reader on a guided tour of the most relevant
and powerful machine learning techniques. Clear descriptions of how
techniques work and detailed code examples demonstrate deep
learning techniques, semi-supervised learning and more, in real
world applications. We will also learn about NumPy and Theano. By
this end of this book, you will learn a set of advanced Machine
Learning techniques and acquire a broad set of powerful skills in
the area of feature selection & feature engineering. Style and
approach This book focuses on clarifying the theory and code behind
complex algorithms to make them practical, useable, and
well-understood. Each topic is described with real-world
applications, providing both broad contextual coverage and detailed
guidance.
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