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  • Python for Data Science

  • The Ultimate Guide for Beginners. Machine Learning Tools, Concepts and Introduction. Python Programming Crash Course.
  • By: Tony F. Charles
  • Narrated by: Russell Newton
  • Length: 3 hrs and 1 min

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Python for Data Science

By: Tony F. Charles
Narrated by: Russell Newton
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Publisher's summary

Are you looking for an ultimate python step-by step guide in an efficient way? Do you want to implement a variety of supervised and unsupervised learning algorithms and techniques quickly and accurately?

If you cannot wait to explore the fundamental concepts and entire process on python data science, listen to this audiobook!

You will start by learning the basics of working with Python and the wide variety of data science packages and extensions. You will be guided on how to setup you work environment before diving into the world of data science. In each section you will learn a great deal of theory backed up by practical examples that contain well-explained Python code. Once you have the fundamentals down, you will get to the core of data science learning algorithms and techniques that are industry-standard in this field.

Studying data science and working with supervised and unsupervised algorithms, as well as neural networks, doesn’t have to be as complicated as it sounds. Explore the world of data science using clear, simple, real-world examples and enjoy the power and versatility of Python and machine learning algorithms!

You will explore:

  • How to install Python and setup a scientific distribution.
  • The most popular Python packages and library used in data science and machine learning, such as Scikit-learn, Numpy, Matplotlib, and Pandas.
  • Data munging with pandas and how to import and prepare your dataset for preprocessing and exploration.
  • How to further prepare your data for the data science pipeline by fully understanding concepts such as data exploration, dimensionality reduction, and outlier detection.
  • How to implement supervised and unsupervised machine learning algorithms such as regression algorithms, the Naïve Bayes classifier, K-nearest neighbors, support vector machines, decision trees, and K-means clustering.
  • Neural networks and how to work with feedforward and recurrent networks, with a focus on the restricted Boltzmann machine.
  • Big Data and why it is the path for the future in data science.

Even if python for data science is a brand new field to you, this audiobook is the key to introduce you into the python world. Python for Data Science can guide you step-by-step through the entire learning process.

Get the audiobook now!

©2020 Tony F. Charles (P)2020 Tony F. Charles
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