Demo-Course

Demo-Course

Demo-Course

Summary

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).

Week 1

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 be using these tools to work through the exercises each week.

Week 2

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 be using these tools to work through the exercises each week.

Week 3

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 be using these tools to work through the exercises each week.

No Comments

Comments are closed.

Loading...
Contact Us
close slider

GIVE US A MESSAGE