Understand the machine learning behind a prediction, then learn to build and run the system that makes it. This AI course is for programmers who want a rigorous, practical education, starting with the field's basic ideas and progressing toward production systems. Concepts, mathematics, code and engineering develop together: use a model on real data, examine why it works, and return to the idea at greater depth. Probability, statistics and Bayesian inference receive sustained attention alongside linear algebra, calculus and optimization. The curriculum moves through classical machine learning, neural networks and PyTorch, then opens up transformers and large language models. Later lessons cover retrieval-augmented generation, fine-tuning, AI agents and MLOps, including evaluation, deployment and monitoring on AWS and Google Cloud. Worked examples connect an intuition to a derivation, a piece of Python or a system design, giving you material to practice beyond the audio. AI news connects
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Contact path
Public web form
Booking probability
36%
Guest openness
Selective
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Signup to Generate a PitchUnderstand the machine learning behind a prediction, then learn to build and run the system that makes it. This AI course is for programmers who want a rigorous, practical education, starting with the field's basic ideas and progressing toward production systems.
Gnothi AI is hosted by OCDevel. The show has published 8 episodes to date.
Recent episodes include "10. Twenty Questions About Pixels" (30 October 2026), "9. Classifying by the Closest Examples" (30 October 2026), and "8. Ranking Every Threshold at Once" (23 October 2026).
Gnothi AI publishes roughly weekly across 8 episodes.
Booking probability for Gnothi AI is 36%, and the host is rated Selective on guests. Gnothi AI requires a Pod Score of 80 or higher. Gnothi AI's contact route on record: public web form.
The strongest fits for Gnothi AI are IT Services & Software Development. Gnothi AI has a technology audience.
OCDevel hosts Gnothi AI, a technology podcast with 8 episodes published.
Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.
Gnothi AI
A decision tree picks yes-or-no pixel questions by measuring how much each cut reduces Gini impurity or entropy, shown with a hand-worked example. The chapter then covers how greedy splitting limits it, how tree depth...
Generating… This episode is narrated by AI-generated synthetic voices. Episode page & show notes Visit website A model that stores instead of learning k-nearest neighbors keeps every labeled training image. It...
Gnothi AI
How ROC curves and AUC measure a model's ranking rather than a single threshold, worked by hand and then on MNIST in scikit-learn. It also covers when precision-recall and average precision reveal what ROC hides...
How a confusion matrix, precision, recall and F1 expose which mistakes a handwritten-digit classifier makes, why accuracy alone can mislead, and how moving a decision threshold trades precision against recall. Also...
Linear regression is fitted by hand to show least squares, then logistic regression with softmax and log loss is trained on MNIST, reaching about 92 percent. A short news segment covers the one-week delay of Python 3.15...
Gnothi AI
Using the MNIST arrays, this chapter inspects the data with NumPy and pandas, seals off the test set, and measures an honest floor of about 11.35 percent with a model that ignores the pixels. It closes with what Python...
Gnothi AI
<p>This chapter builds a machine-learning workspace that someone else can rerun, using uv, a lock file, git and a documented MNIST download. It also covers what a random seed can and cannot guarantee, and how uv 0.12...
This chapter builds a machine-learning workspace that someone else can rerun, using uv, a lock file, git and a documented MNIST download. It also covers what a random seed can and cannot guarantee, and how uv 0.12...
<p>A walk through the four learning settings — supervised, unsupervised, reinforcement, self-supervised — organised around what feedback the world actually returns and when, with MNIST, tic-tac-toe and next-word...
A walk through the four learning settings — supervised, unsupervised, reinforcement, self-supervised — organised around what feedback the world actually returns and when, with MNIST, tic-tac-toe and...
<p>This chapter traces artificial intelligence from Turing's 1950 test through the Dartmouth meeting, two funding collapses, expert systems, statistical learning, and the deep learning and transformer breakthroughs...
This chapter traces artificial intelligence from Turing's 1950 test through the Dartmouth meeting, two funding collapses, expert systems, statistical learning, and the deep learning and transformer breakthroughs...
This chapter contrasts problems with hand-writable rules against problems like spam filtering and digit recognition where only examples work, sorts out five commonly confused terms in the field, and walks a vague...
<p>This chapter contrasts problems with hand-writable rules against problems like spam filtering and digit recognition where only examples work, sorts out five commonly confused terms in the field, and walks a vague...
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