Data science, machine learning, and artificial intelligence in Python for students and professionals
InstructorLazy Programmer Inc.
The curriculum emphasizes deriving solutions for 1-D and multi-dimensional models rather than just using libraries.
Theoretical concepts are paired with coding applications, including L1/L2 regularization and gradient descent.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This educational slide introduces linear regression as a prerequisite for deep learning, illustrating its application with a neural network schematic and biological data visualizations.
This slide outlines a hands-on approach to machine learning, emphasizing the implementation of Linear Regression from scratch in Python using real-world datasets.
This slide outlines the course's rigorous approach to machine learning by prioritizing first principles and real math over standard libraries like Scikit-Learn.
This slide contrasts the course with traditional academic teaching by displaying complex mathematical derivations alongside critiques about excessive content and lack of coding practice.
Instructional speed may increase during the gradient descent section, potentially leaving some learners needing external resources. Note that this signal was recorded prior to the displayed update date, which does not prove a correction has occurred.
Some sections may utilize shortcuts in mathematical explanations that require supplemental study. Note that this signal was recorded prior to the displayed update date, which does not prove a correction has occurred.
The content aligns with technical prerequisites but presents pacing challenges for some learners. Note that the signal regarding increased pace in advanced sections was recorded prior to the displayed update date, which does not prove a correction has occurred.