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!

This book will start off assuming that you already have basic knowledge of Python because all the codes provided will be in Python.

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.

By the end of the first section, you will have a good understanding of machine learning basics, and you’ll be able to learn more intermediate to advanced-level concepts in the subsequent sections.

Machine learning is a subset of AI or artificial intelligence. It’s one of the trending topics in the world these days, and it will have many applications in the future.

Here’s an example: imagine that I ask you to write a program to scan an image and tell if it’s an image of a cat or a dog. To build this program using traditional programming techniques, your program will get overly complex. You will have to develop many rules to look for specific curves, edges, and colors in an image to tell if it’s a cat or a dog.

But what if the test image was a black-and-white photo? I bet your rules may not work. They may break. Then you’ll have to rewrite the program. Or if I give you a picture of a cat or a dog from a different angle that you did not predict before? It would be worse if you were asked to extend this program such that it supports three kinds of animals cats, dogs, and horses. You might have to start all over.

Solving this problem using traditional programming techniques is going to get overly complex or sometimes impossible. Machine learning is a technique to solve these kinds of problems.

This is how it works: we build a model or a robot and give it lots and lots of data. For example, we give you thousands or tens of thousands of pictures of cats and dogs from different angles and colors. Our model will then find and learn the patterns in the input data of those pictures. Now, when we give it a new image of a cat that it hasn’t seen before and ask if it is a cat, a dog, or a horse, it will tell us with a certain level of accuracy. The more input data we give it, the more accurate our model is going to be. That was a very basic example of what we do with Machine Learning.

Machine learning has other applications in self-driving cars, robotics, language processing, vision processing, forecasting things like stock market trends and the weather, games, etc.

That’s the basic idea of machine learning. Now, let us start learning. We will look at machine learning in action.

Read The Book…