This course provides a broad overview of the machine learning industry. What is learning? What types of training exist? What can be considered the criteria for effective learning? What are the machine learning methods and algorithms? Those who complete this course will be able to start solving real-world data science problems.
Main topics of the course:
- The problem of learning. Training and testing;
- Generalization theory. Feature descriptions and types of quality functionality;
- Decision trees;
- Linear regression;
- Logistic regression;
- Support Vector Machines;
- Clustering and dimensionality reduction;
- Introduction to neural networks;
- Learning without a teacher;
- Learning with reinforcement learning;
- Modern machine learning libraries.