Master the in-demand skill of machine learning and learn how to create algorithms that can independently learn patterns and make decisions in this hands-on bootcamp.
Master machine learning to create algorithms that can independently learn patterns and make decisions in this hands-on bootcamp.
Machine learning goes beyond normal coding, which requires step-by-step instructions, by using algorithms that can independently learn patterns and make decisions. This skill set is in high demand, as machine learning algorithms now run the majority of trading on Wall Street and the product recommendations at big companies like Amazon, Spotify, and Netflix.
This course will begin with linear and logistic regression, the most time-tested and reliable tools for approaching a machine learning problem. The course will then progress to algorithms with a very different theoretical basis, such as k-nearest neighbors, decision trees, and random forest. This will bring important statistical concepts to the forefront, such as bias, variance, and overfitting. You’ll also learn how to measure the accuracy of your models, as well as tips for choosing effective features and algorithms.
The course will be focused on the practical skills needed to solve real-world problems with machine learning. The mathematical foundations for each machine learning algorithm will be explained visually, but there will not be a formal math component. Entering students are expected to be comfortable with writing Python programs, as well as the Numpy and Pandas libraries.
This Python Machine Learning Bootcamp is offered at our coding school, Noble Desktop.
This course has a prerequisite:
Learn more about Python Machine Learning Bootcamp at NYIM Training.
This course is available for "remote" learning and will be available to anyone with access to an internet device with a microphone (this includes most models of computers, tablets). Classes will take place with a "Live" instructor at the date/times listed below.
Upon registration, the instructor will send along additional information about how to log-on and participate in the class.
For cancellations and reschedules, please email us at [email protected]
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