A primer on Machine Learning for Data Science. Revealed for everyday people, by the Backyard Data Scientist.
InstructorDavid ValentineThe curriculum provides a structured overview of core terms including Artificial intelligence, Big Data, and the specific steps within a Machine Learning workflow.
A multi-part series introduces Python usage via Anaconda and Jupyter, including a practical application using the Titanic dataset.
Editorial course preview
These 2 complementary views highlight concrete, legible examples from the course presentation.
This course overview slide outlines the curriculum structure, covering topics from core concepts and impacts to the machine learning process and practical applications.
This image shows the physical infrastructure of a data center, illustrating the server racks and cabling that power machine learning systems.
Learner signals suggest a need for more technical variety, such as specific methods for overcoming overfitting or more detailed train/test splitting examples.
One signal indicates the content could benefit from more visual aids like flow charts or Venn diagrams to illustrate algorithmic processes.
The curriculum aligns well with the stated goal of providing a beginner-friendly introduction to data science and Python.