1. What Is Machine Learning?
What it means to learn
()
Work with data
()
Apply machine learning
()
Different types of machine learning
()
2. Different Ways a Machine Learns
Supervised
()
Unsupervised
()
Semi-supervised
()
Reinforcement
()
3. Popular Machine Learning Algorithms
Problems that use machine learning
()
Decision trees
()
k-nearest neighbor
()
K-mean clustering
()
Regression
()
Naive Bayes
()
4. Applying Algorithms
Follow the data
()
Fit the data
()
Select the best algorithm
()
5. Common Challenges
Machine learning challenges
()
Ex_Files_Glossary_ML.zip
(102 KB)