Machine Learning is only really fun when you evaluate real data. That's why you analyze a lot of practical examples in this course:
- Estimate the value of used cars
- Write a spam filter
- Diagnose breast cancer
All code examples are shown in both programming languages - so you can choose whether you want to see the course in Python, R, or in both languages!
After the course you can apply Machine Learning to your own data and make informed decisions:
You know when which models might come into question and how to compare them. You can analyze which columns are needed, whether additional data is needed, and know which data needs to be prepared in advance.
This course covers the important topics:
On all these topics you will learn about different algorithms. The ideas behind them are simply explained - not dry mathematical formulas, but vivid graphical explanations.
We use common tools (Sklearn, NLTK, caret, data.table, ...), which are also used for real machine learning projects.
What do you learn?
- Linear Regression
- Polynomial Regression
- Logistic Regression
- Naive Bayes
- Decision trees
- Random Forest
You will also learn how to use Machine Learning:
- Read in data and prepare it for your model
- With complete practical example, explained step by step
- Find the best hyper parameters for your model
- "Parameter Tuning"
- Compare models with each other:
- How the accuracy value of a model can mislead you and what you can do about it
- K-Fold Cross Validation
- Coefficient of determination
My goal with this course is to offer you the ideal entry into the world of machine learning.