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Leverage the power of Python and statistical modeling techniques
for building accurate predictive models Key Features Get introduced
to Python's rich suite of libraries for statistical modeling
Implement regression, clustering and train neural networks from
scratch Includes real-world examples on training end-to-end machine
learning systems in Python Book DescriptionPython's ease of use and
multi-purpose nature has led it to become the choice of tool for
many data scientists and machine learning developers today. Its
rich libraries are widely used for data analysis, and more
importantly, for building state-of-the-art predictive models. This
book takes you through an exciting journey, of using these
libraries to implement effective statistical models for predictive
analytics. You'll start by diving into classical statistical
analysis, where you will learn to compute descriptive statistics
using pandas. You will look at supervised learning, where you will
explore the principles of machine learning and train different
machine learning models from scratch. You will also work with
binary prediction models, such as data classification using
k-nearest neighbors, decision trees, and random forests. This book
also covers algorithms for regression analysis, such as ridge and
lasso regression, and their implementation in Python. You will also
learn how neural networks can be trained and deployed for more
accurate predictions, and which Python libraries can be used to
implement them. By the end of this book, you will have all the
knowledge you need to design, build, and deploy enterprise-grade
statistical models for machine learning using Python and its rich
ecosystem of libraries for predictive analytics. What you will
learn Understand the importance of statistical modeling Learn about
the various Python packages for statistical analysis Implement
algorithms such as Naive Bayes, random forests, and more Build
predictive models from scratch using Python's scikit-learn library
Implement regression analysis and clustering Learn how to train a
neural network in Python Who this book is forIf you are a data
scientist, a statistician or a machine learning developer looking
to train and deploy effective machine learning models using popular
statistical techniques, then this book is for you. Knowledge of
Python programming is required to get the most out of this book.
Get to grips with the most popular Python packages that make data
analysis possible Key Features Explore the tools you need to become
a data analyst Discover practical examples to help you grasp data
processing concepts Walk through hierarchical indexing and grouping
for data analysis Book DescriptionPython, a multi-paradigm
programming language, has become the language of choice for data
scientists for visualization, data analysis, and machine learning.
Hands-On Data Analysis with NumPy and Pandas starts by guiding you
in setting up the right environment for data analysis with Python,
along with helping you install the correct Python distribution. In
addition to this, you will work with the Jupyter notebook and set
up a database. Once you have covered Jupyter, you will dig deep
into Python's NumPy package, a powerful extension with advanced
mathematical functions. You will then move on to creating NumPy
arrays and employing different array methods and functions. You
will explore Python's pandas extension which will help you get to
grips with data mining and learn to subset your data. Last but not
the least you will grasp how to manage your datasets by sorting and
ranking them. By the end of this book, you will have learned to
index and group your data for sophisticated data analysis and
manipulation. What you will learn Understand how to install and
manage Anaconda Read, sort, and map data using NumPy and pandas
Find out how to create and slice data arrays using NumPy Discover
how to subset your DataFrames using pandas Handle missing data in a
pandas DataFrame Explore hierarchical indexing and plotting with
pandas Who this book is forHands-On Data Analysis with NumPy and
Pandas is for you if you are a Python developer and want to take
your first steps into the world of data analysis. No previous
experience of data analysis is required to enjoy this book.
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