Data science, machine learning, and artificial intelligence in Python for students and professionals
InstructorLazy Programmer Inc.Editorial course preview
These 2 complementary views highlight concrete, legible examples from the course presentation.
This educational slide introduces logistic regression as a prerequisite for deep learning, illustrating the connection between biological neurons and artificial neural networks.
This slide introduces the hands-on portion of the course, emphasizing that machine learning is practical and that students will build real programs to implement Logistic Regression from scratch in Python.
The course emphasizes building models by pairing mathematical theory with manual code implementation, covering topics like neuron output and weight updates via gradient descent.
Practical application is demonstrated through specific tracks, including e-commerce data pre-processing and facial expression recognition addressing class imbalance.
The transition from mathematical derivation to Python code can be difficult, as some sections present complex equations rapidly through static slides. Note that these signals originated in reviews dated before the displayed update; therefore, it is uncertain if recent changes have addressed this pacing.
The course aligns with technical prerequisites but presents challenges for those not yet comfortable bridging math and code. Note that some difficulty reported in reviews predates the displayed update; it is uncertain if recent changes have addressed this gap.
