What about this course?
This course introduces the fundamental concepts of Machine Learning and explores the basic modeling workflow needed to get into different Machine Learning applications. This course also makes an introduction to the scikit-learn library with practical examples of its use.
INE's world-class IT trainingInstructor for this course
Santiago Basulto
This course is composed by the following modules
What is Machine Learning?
Typologies: Supervised, Unsupervised and Reinforcement Learning
Common Models: Regressions, Classifications and More
Machine Learning Methodologies
Machine Learning Concepts
Statistical Techniques
Visualization with Python
COVID-19 Analysis
Scikit-learn Introduction
Our First Machine Learning Project - Part 1
Our First Machine Learning Project - Part 2
Diabetes analysis
Balancing Data and Confusion Matrix
Balancing diabetes observations
Cross-validation and Parameter Tuning - Part 1
Cross-validation and Parameter Tuning - Part 2
Tuning diabetes prediction model
Scale, Standardize and Normalize Data
Overfitting and Underfitting
Spot-checking Algorithms
Spot-checking algorithms on tracks data
Credit card applications
Common Course Questions
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