BEGINNERS COURSE ON MACHINE LEARNING

If you are looking for a machine learning tutorial with Python and Jupyter notebook, it’s your lucky day. You're going to learn how to solve a real-world problem using machine learning and python.

In the US, a machine learning programmer makes an average salary of $166,000! When you have finished this book and implemented the skills you have learned, you will have had enough skills to tackle Machine Learning projects that will either help you obtain your dream job or provide you with the tools you need to use machine learning algorithms to address problems in your business, career, or personal life.

This book uses a project-based learning method to deliver this engaging tutorial with Python.

Create Effective Machine Learning Models to Address Any Issue

By keeping all the boring stuff like math and statistics at the end like a prologue, I ensure that this book will not be boring to those with basic knowledge of Python and statistics or data science. These are very crucial to machine learning on all levels.

The course will teach you how to:

• Obtain complete toolkits for machine learning to address the majority of real-world issues.
• Recognize the many performance indicators for Regression, classification, and other ML algorithms, including accuracy, and know when to apply them.
• Utilize unsupervised Machine Learning (ML) algorithms to comprehend your data, such as hierarchical clustering and k-means clustering.
• Develop models using Jupyter (IPython) notebook, and other IDE
• Visually and successfully communicate using Matplotlib
• Create fresh features to enhance algorithmic forecasts
• Decision trees can be used to forecast staff attrition.
• And a whole lot more!

Who is this book for?

Anyone who is eager to understand Python's machine learning algorithms and has a keen interest in how machine learning may be used to solve problems in the real world.

Anyone who wants to learn more than the fundamentals and gain a comprehensive understanding of machine learning algorithms should do so.

Any intermediate to experienced EXCEL users incapable of handling big files

Anyone wishing to begin or advance in a career as a data scientist Anyone wishing to apply machine learning to their field Anyone wishing to communicate their findings in a professional and compelling manner

We're going to start off with a brief introduction to machine learning, then we're going to talk about the tools you need, and after that, we're going to jump straight into the problem we're going to solve you'll learn how to build a model that can learn and predict the kind of music people like right in the second chapter.