tools applied:
Python, sci-kit learn, NumPy and SciPy, matplotlib, pandas
Jupyter Notebook
and a bit of Git
The course:
I didn't make it only for the paper, but to get more practice in the way of creation and application of different Machine Learning methods. In concepts it does not only provide you with some data(set) and offers you to follow the pre-defined steps, no... it would be way too easy and no one would suggest this course to anyone any more. This course has a good reputation and high suggestion rates. Now I know why and I have to admit this course is well-structured and worth to do, even if you pay for it, which I actually avoided by finishing the 5-6 weeks plan in the 7-day free trial period. I mean during 7 nights, as I am working daytime.
This is a well structured, explanatory course, giving theoretical background for the models, helps with examples to learn in general and about when and which model to chose.
Of course, all tasks starts with
ETL - or preprocessing, extract, transform, load (wiki article)
then varied datasets are used to practice
Regression models - linear, nonlinear, single and multivariate
Classification models - KNN (K nearest neighbor), Decision trees, Logistic regression, SupportVectorMachine
Clustering models - Hierarchical, Kmeans, DSCN (DBSCAN)
Recommender systems - Content based, Collaborative filtering systems
Final project - it is a kind of an exam, peer-graded, so at least two people verified and accepted the work uploaded to the system.
