This course provides an overview of machine learning fundamentals on modern Intel® architecture. Topics covered include:
Reviewing the types of problems that can be solved
Understanding building blocks
Learning the fundamentals of building models in machine learning
Exploring key algorithms
By the end of this course, students will have practical knowledge of:
Supervised learning algorithms
Key concepts like under- and over-fitting, regularization, and cross-validation
How to identify the type of problem to be solved, choose the right algorithm, tune parameters, and validate a model
The course is structured around 12 weeks of lectures and exercises. Each week requires three hours to complete. The exercises are implemented in Python*, so familiarity with the language is encouraged (you can learn along the way).
This class introduces the basic data science toolset:
Jupyter* Notebook for interactive coding
NumPy, SciPy, and pandas for numerical computation
Matplotlib and seaborn for data visualization
Scikit-learn* for machine-learning libraries
You’ll use these tools to work through the exercises each week.
So far, the course has been heavily focused on supervised learning algorithms. This week, learn about unsupervised learning algorithms and how they can be applied to clustering and dimensionality reduction problems.
Dimensionality refers to the number of features in the dataset. Theoretically, more features should mean better models, but this is not true in practice. Too many features could result in spurious correlations, more noise, and slower performance. This week, learn algorithms that can be used to achieve a reduction in dimensionality, such as: