 Название: Python for Data Science: Deep Machine Learning Algorithms in Python and Artificial Intelligence. Crash Course to Measure Value of Big Data and Analyzes what Matters to Live by Computer Programming
Автор: Mik Arduino
Издательство: Amazon.com Services LLC
Год: 2019
Страниц: 155
Язык: английский
Формат: pdf, azw3, epub
Размер: 19.2 MB
If you are tired of reading without understanding and want to learn the value of big data and artificial intelligence simply and quickly, then keep reading... Today 91% of Python programmers are not quite prepared. This is what the IT companies say, according to a recent survey.
Do you know why? The reason is that they have no notions of artificial intelligence, so future programmers will no longer find work without having knowledge of this particular field that is constantly evolving.
Artificial Intelligence (AI) has made great strides over the years, and by 2024 it is estimated that 77% of programmers will have to be experts in this field to implement it in the various programming languages.
So if you are such a programmer or aspirant and ignore the importance that Python data analysis and artificial intelligence have in our future, then you will be cut off from the business world.
The solution? You need to learn these things and, above all, do it in a clear, simple, and practical way. The goal of Python for Data Science is to give you an advanced level training on Python, artificial intelligence, and deep machine learning as quickly as possible.
The end of this guidebook is going to spend some time taking a look at a lot of the different machine learning and Python algorithms that you can use in order to help complete the data analysis and tell you all of the great insights and predictions that are inside of your data. Some of the different algorithms we are going to take a look at including the KNN, clustering, Naïve Bayes, Support Vector Machines, and Markov Algorithms. And then we will finish off with some of the basics of working with a data visualization and why this is so important to some of the work that we want to do in order to understand those complex relationships in our data.
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