Mathematics-and-Methods-in-Machine-Learning

Materials from Mathematics and Methods in Machine Learning and Neural Networks course as a part of Metropolia University of Applied Sciences Software Engineering field.

Technologies and libraries learned while attending the course:

• Pandas
• Numpy
• Sklearn
• Mathematical methods used in Machine learning such as
  • cluster analysis
  • linear regression
  • logistic regression
  • gradient descent
  • association
  • recommendation
  • text-analysis
  • backpropagation

Notes

Course notes

Projects

Project 1 - Simple data manipulation, correlation matrix

Project 2 - Cluster analysis, kmeans and agglomerative hierarchical cluster algorithms

Project 3 - Decision trees, creating a decision tree for a given dataset

Project 4 - Linear regression, creating, prediting values and validating a model

Project 5 - Logistic regression, creating, prediting values and validating a model

Project 6 - Recommendation system, kNN, SVD algorithms, collaborative filtering

Project 7 - Text analysis, sentiment analysis