Machine Learning

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Introduction
Self LEarning
Why Statstics in ML Or Data Science
How important is interpretability for a model in Machine Learning?
What are the most important machine learning techniques to master at this time?
Learning
Steps in machine learning model development and deployment
Statistical fundamentals and terminology
Statistics
Spark MLib
Terminology
Machine Learning Steps
- test
Preprocessing and Feature selection techniues
The importance of variables feature selection/attribute selection
- Feature Selection
Feature engineering
Hyperplanes
cross-validation
Machine learning losses
When to stop tuning machine learning models
Train, validation, and test data
input data structure
Why are matrices/vectors used in machine learning/data analysis?
- Linear Algebra
OverView
Data scaling and normalization
Questions
Which machine learning algorithm should I use?

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Learning
Supervised Learning
Evaluating supervised learning

Using train/test to prevent overfitting of a polynomial regression

PreviousK-fold cross validation NextRegression

Last updated 5 years ago

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