Python Machine Learning for Beginners: Learning from scratch NumPy, Pandas, Matplotlib, Seaborn, Scikitlearn, and TensorFlow for Machine Learning and ... Learning & Data Science for Beginners)
Python Machine
Learning for Beginners
Machine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes,
that’s right. Based on a significant amount of data and evidence, it’s obvious
that ML and AI are here to stay.Consider any industry today. The practical
applications of ML are really driving business results. Whether it’s
healthcare, e-commerce, government, transportation, social media sites,
financial services, manufacturing, oil and gas, marketing and salesYou name it.
The list goes on. There’s no doubt that ML is going to play a decisive role in
every domain in the future.But what does a Machine Learning professional do?A
Machine Learning specialist develops intelligent algorithms that learn from
data and also adapt to the data quickly. Then, these high-end algorithms make
accurate predictions.
Python Machine Learning for Beginners presents
you with a hands-on approach to learn ML fast.
How Is This Book
Different?
AI Publishing strongly believes in learning by doing methodology.
With this in mind, we have crafted this book with care. You will find that the
emphasis on the theoretical aspects of machine learning is equal to the
emphasis on the practical aspects of the subject matter.You’ll learn about data
analysis and visualization in great detail in the first half of the book. Then,
in the second half, you’ll learn about machine learning and statistical models
for data science.Each chapter presents you with the theoretical framework
behind the different data science and machine learning techniques, and
practical examples illustrate the working of these techniques.When you buy this
book, your learning journey becomes so much easier. The reason is you get
instant access to all the related learning material presented with this
book—references, PDFs, Python codes, and exercises—on the publisher’s website.
All this material is available to you at no extra cost. You can download the ML
datasets used in this book at runtime, or you can access them via the Resources/Datasets folder.You’ll also find the short course on
Python programming in the second chapter immensely useful, especially if you
are new to Python. Since this book gives you access to all the Python codes and
datasets, you only need access to a computer with the internet to get started.
The topics covered include:
·
Introduction and
Environment Setup
·
Python Crash Course
·
Python NumPy
Library for Data Analysis
·
Introduction to
Pandas Library for Data Analysis
·
Data Visualization
via Matplotlib, Seaborn, and Pandas Libraries
·
Solving Regression
Problems in ML Using Sklearn Library
·
Solving
Classification Problems in ML Using Sklearn Library
·
Data Clustering
with ML Using Sklearn Library
·
Deep Learning with
Python TensorFlow 2.0
·
Dimensionality
Reduction with PCA and LDA Using Sklearn
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