This skillset 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.
DurationThis skillset 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.
We provide computers with the software for all our paid courses, so you can travel light. You can bring a pen and paper/notebook to take notes if you'd like. Some classes are available on Mac, PC or either. See the class page for details. You can specify your operating system at checkout.
[AI INTRO] is a math heavy course offered at KTBYTE, and require students to have mastered self-guided learning.
Mathematica is a modern computing system for data analytics. Mathematica offers built in machine learning capabilities for data analysis.
NYU Tandon's Summer Program for Machine Learning is a two-week course that introduces high school students to the computer science, data analyses, mathematical techniques, and logic that drive the fields of machine learning and artificial intelligence.
This 35-hour Machine Learning with R course introduces both the theoretical foundation of machine learning algorithms as well as their practical applications in R.
The Machine Learning Pipeline on AWS course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment.
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