AI & Machine Learning / MEASUREMENT REPORT
A follow-on to the same instructors' machine learning survey, this one narrows in on neural network architectures — feed-forward networks, convolutional networks for images, recurrent networks for sequences, and a few older architectures kept for historical context — built hands-on with Python libraries rather than derived from scratch mathematically.
Measured data
| Rating | 4.5 / 5.0 |
|---|---|
| Reviews | 49,755 |
| Learners | 409,871 |
| Total video | 22.8 h |
| Price | $140 Real price (verified 2026-07-24) |
| Audio / subtitles | English audio / English (auto), AR, FI, FR, DE, +14 more |
| Last refresh | 2026-06-13 |
| Level | Intermediate |
| Measured | 2026-07-04 |
◎ Right for you if
- Learners who've already covered classical ML and want to move into neural networks specifically
- People who want each architecture tied to a concrete, working project rather than slides alone
- Python users comfortable installing and running notebook-based code
× Skip it if
- Complete beginners to ML — this assumes the fundamentals are already in place
- Anyone looking for the current generation of transformer/LLM architectures, which this does not center on
CALIBRATION — VERDICTA solid, hands-on architecture-by-architecture tour that holds up well as a second course rather than a first. Its center of gravity is pre-transformer deep learning, so pair it with newer material if large language models are the actual goal.
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