Python Machine Learning Bootcamp
Advanced Python Classes in NYC
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.
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Video Training and Workbook Included
Students get a workbook and lifetime access to self-paced training videos, a $95 value.
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Computer Provided
Modern computer classroom with your choice of Mac or PC to work on.
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Small In-Person Classes
Get personalized attention in small groups with an expert instructor in the classroom.
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Free Retake
Refresh the course materials within six months for free as long as there's space in the class.
What You'll Learn
- How to clean and balance your data using the Pandas library
- Applying machine learning algorithms such as logistic regression and random forest using the scikit-learn library
- Choosing good features to use as input for your algorithms
- Properly splitting data into training, test and cross-validation sets
- Important theoretical concepts like overfitting, variance and bias
- Evaluating the performance of your machine learning models