Python for Machine Learning & Data Science Masterclass

Learn about Data Science and Machine Learning with Python! Including Numpy, Pandas, Matplotlib, Scikit-Learn and more!

Created by Jose Portilla | 44 hours on-demand video course

This is the most complete course online for learning about Python, Data Science, and Machine Learning. Join Jose Portilla’s over 2.6 million students to learn about the future today!

Welcome to the most complete course on learning Data Science and Machine Learning on the internet! After teaching over 2 million students I’ve worked for over a year to put together what I believe to be the best way to go from zero to hero for data science and machine learning in Python!

This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Data Science and Machine Learning. The typical starting salary for a data scientists can be over $150,000 dollars, and we’ve created this course to help guide students to learning a set of skills to make them extremely hirable in today’s workplace environment.

What you’ll learn

• Master critical data science skills.
• Understand Machine Learning from top to bottom.
• Replicate real-world situations and data reports.
• Learn NumPy for numerical processing with Python.
• Conduct feature engineering on real world case studies.
• Learn Pandas for data manipulation with Python.
• Create supervised machine learning algorithms to predict classes.
• Learn Matplotlib to create fully customized data visualizations with Python.
• Create regression machine learning algorithms for predicting continuous values.
• Learn Seaborn to create beautiful statistical plots with Python.
• Construct a modern portfolio of data science and machine learning resume projects.
• Learn how to use Scikit-learn to apply powerful machine learning algorithms.
• Get set-up quickly with the Anaconda data science stack environment.
• Learn best practices for real-world data sets.
• Understand the full product workflow for the machine learning lifecycle.
• Explore how to deploy your machine learning models as interactive APIs.

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