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With Nathan Grossman (Data Scientist, Wells Fargo).
Monday, July 30, 2018 at 06:30 PM    Cost: $1750
Metis, Online
 
     
 
 
              

            
 
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LOCATION
DETAILS
From robotics, speech recognition, & analytics to finance & social network analysis, machine learning comprises one of the most useful scientific toolsets of our age. This course provides an overview of the core principles of machine learning & artificial intelligence using a hands-on, project-based curriculum. There is an intense focus on implementing popular machine learning algorithms to solve real problems using real data. So get ready to learn work hard & complete 5 portfolio-building projects in 5 weeks.

Who the course is designed for:
Individuals working in any number of data-intensive fields including consulting, finance, information technology, healthcare, & logistics, as well as recent college graduates & entrepreneurs interested or specializing in these fields & others like them.

Outcomes
An understanding of the basic principles of machine learning & artificial intelligence from both an intuitive & practical level.
An understanding of common feature design principles for image & text data.
An understanding of how to use popular machine learning & deep learning software packages in Python, as well as how to implement several popular machine learning algorithms (Linear/Logistic Regression; KMeans Clustering) from scratch.
Extensive experience applying machine learning algorithms to real data sets.

What you'll receive upon completion:
Certificate of completion
Certificate link & instructions on how to add to your LinkedIn profile
2.7 Continuing Education Units

Our 1-hour Live Online sample class serves as an excellent way to preview the Live Online format & experience.

Nathan Grossman, instructor of the Live Online Machine Learning & AI Principles course, will cover a few sample topics in the class followed by Q&A.

Introduction to plotting in Python
Using a linear regressor on a linear dataset
Using a linear regressor on a nonlinear dataset
 
 
 
 
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