T�tulo: Python Machine Learning: A Crash Course for Beginners to Understand Machine learning, Artificial Intelligence, Neural Networks, and Deep Learning with Scikit-Learn, TensorFlow, and Keras.
Editor: Independently Published
Fecha de publicaci�n: 2019
Idioma: Ingl�s
ISBN 10: 1708023062
ISBN 13: 9781708023065
Encuadernaci�n: Paperback / softback
Estado: New
Peso: 245 gramos

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Sinopsis

Are you Interested in Learning Some of the Best Machine Learning Algorithms that Will Help you to See Some Amazing Results and Actually be Able to sort Through your Data?
› › › keep reading › › ›

This guidebook is going to take some time to look at how to handle working with a Python machine learning project. We will explore all of the different parts that need to come with this, including some of the different algorithms that we are able to explore and use to help sort some of our data. This is a crucial step before diving further into machine learning with Python as this will give you a bird’s eye view of what possible algorithm to use and what is best suited for your situation and what you want to do with the findings.

There are so many different algorithms and more that can be used and figuring out which one is going to help with your analysis and ensure that you are able to get the results that you want is going to be tough. With the help of this guidebook, you can finally get started on your data analysis process, and figuring out the best steps to take to handle your data with the right algorithm, in no time.

There is so much that we can explore when it comes to Python machine learning, and we are going to dive right into all of it. Some of the topics that we are going to discuss in this guidebook about.

You will learn:

The basics of machine learning and why it is so important to learn.
The importance of data and the different types of data that show up in Python and how to use these in machine learning.
Some of the supervised learning algorithms that work with regressions, including polynomial regression, gradient descent, linear regression, and cost function.
How to work with regularization and avoid the issue of overfitting.
Some of the best-supervised learning algorithms of classification, including Logistic Regressions.
How to work with non-linear classification models, like SVMs and neural networks, for your needs.
The different validation and optimization techniques that you can use to make sure your algorithms respond the way that you want them to.
Moving on to some unsupervised machine learning that we can use, and the best clustering algorithms along the way.
A look at the Principal Component Analysis and the Linear Discriminant Analysis and how they compare to one another.

A lot can come into play when you work with machine learning, and when we combine the Python language, we would be able to create some of the best data analysis that we need. Combining both machine learning and Python will open doors of opportunity for us when it comes to improving or adding that little extra in our process. When you are ready to learn more about Python machine learning, scroll the top of the page and select �� BUY NOW ��.

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Acerca de

Sinopsis

Are you Interested in Learning Some of the Best Machine Learning Algorithms that Will Help you to See Some Amazing Results and Actually be Able to sort Through your Data? › › › keep reading › › ›

There is so much that we can explore when it comes to Python machine learning, and we are going to dive right into all of it. Some of the topics that we are going to discuss in this guidebook about.

You will learn:

The basics of machine learning and why it is so important to learn.

The importance of data and the different types of data that show up in Python and how to use these in machine learning.

Some of the supervised learning algorithms that work with regressions, including polynomial regression, gradient descent, linear regression, and cost function.

How to work with regularization and avoid the issue of overfitting.

Some of the best-supervised learning algorithms of classification, including Logistic Regressions.

How to work with non-linear classification models, like SVMs and neural networks, for your needs.

The different validation and optimization techniques that you can use to make sure your algorithms respond the way that you want them to.

Moving on to some unsupervised machine learning that we can use, and the best clustering algorithms along the way.

A look at the Principal Component Analysis and the Linear Discriminant Analysis and how they compare to one another.