It has become quite common these days to hear people refer to modern machine learning systems as “black boxes” - data goes in, decisions come out, but the processes between input and output are disconnected. This series is aimed at teaching the core concepts of machine learning by looking into the “black box” and understanding the math behind an algorithm. Popular Python packages will be used to implement these machine learning models on interesting datasets.
Join us for a 6-part technical series introducing some basic machine learning concepts which encapsulate sueprvised machine learning problems. The webinar will be for 2 hours on Saturdays from 12 - 2pm PST. The focus for the first hour will be the concept, formulae and statistics behind the algorithm . In the second hour, the goal would be to implement what has been learned using Python (source code will be provided).
Sumana Ravikrishnan, Tejal Patted, Chinmayee Hota, Anju Mercian
Machine Learning, Concepts and Terminology, Hypothesis Testing, How to use Google Colab
🎥. Recording
📝 Slides
💻 Code
Conditional Probability, Bayesian Learning, Naive Bayes Theorem
🗣️ Sumana Ravikrishnan & Tejal Patted
🎥. Recording
📝 Slides
💻 Code
Decision Trees, Overfitting & Underfitting, Ensemble Learning
🗣️ Sumana Ravikrishnan & Tejal Patted
🎥. Recording
📝 Slides
💻 Code
Linear Regression, Loss Function, Gradient Descent
🗣️ Sumana Ravikrishnan & Chinmayee Hota
🎥. Recording
📝 Slides
💻 Code
Logistic Regression, Support Vector Machines
🗣️ Sumana Ravikrishnan & Chinmayee Hota
🎥. Recording
📝 Slides
💻 Code
Performance Metrics for Classification & Regression, Cross Validation
🗣️ Sumana Ravikrishnan & Tejal Patted
🎥. Recording
📝 Slides
💻 Code