Beginner
96 Lessons
36h
Certificate of Completion
Takeaway Skills
An understanding of the foundations of machine learning and scientific modeling
A working knowledge of deep learning models including MLPs, CNNs, and RNNs
Familiarity with traditional machine learning methods including regression, SVMs, and decision trees
An understanding of the proper application of these methods
Hands-on experience with implementing various machine learning models using sklearn and Keras
Course Overview
The machine learning field is rapidly advancing today due to the availability of large datasets and the ability to process big data efficiently. Moreover, several new techniques have produced groundbreaking results for standard machine learning problems. This course provides a detailed description of different machine learning algorithms and techniques, including regression, deep learning, reinforcement learning, Bayes nets, support vector machines (SVMs), and decision trees. The course also offers suffici...
Course Content
Getting started
Introduction
Scientific Programming with Python
Machine Learning with Sklearn
Neural Networks and Keras
Regression and Optimization
11 Lessons
Basic Probability Theory
8 Lessons
Probabilistic Regression and Bayes Nets
7 Lessons
Generative Models
10 Lessons
Cyclic Models and Recurrent Neural Networks
9 Lessons
Reinforcement Learning
8 Lessons
Artificial intelligence, the Brain, and Our Society
4 Lessons
Conclusion
2 Lessons
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