Software
Basic understanding of Machine Learning Algorithms
The learning outline covers Machine Learning Algorithms. The curriculum below sets out the topics and lessons in more detail.
The learning outline.
Machine Learning Algorithms
- Machine Learning 101
- Classification and Regression
- Values
- Variables
- Distributions
- Intro to Decision Tree (Reading)
- Creating a Decision Tree
- How Decision Tree Splits
- Decision Tree Algorithm
- Split Criteria in Decision Trees
- Measure of Impurity in Decision Tree
- Decision Tree Revision and Points Discussion
- Naive Bayes
- Naive Bayes Complete Understanding (Reading)
- What is Data Normalisation
- Understanding Problems by Outliers
- Deciding Best Boundary
- Understanding KNN
- Support Vector Machines
- Sentiment Analysis using NB classifier
- Conclusion of this module