Show notes: ocdevel.com/mlg/1. MLG teaches the fundamentals of machine learning and artificial intelligence. It covers intuition, models, math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your syllabus, with highly-curated resources for each episode's details at ocdevel.com. Audio is a great supplement during exercise, commute, chores, etc.

MLG, Resources Guide
Gnothi (podcast project): website, Github
Tyler's Battlestation

What is this podcast?

"Middle" level overview (deeper than a bird's eye view of machine learning; higher than math equations)
No math/programming experience required

Who is it for

Anyone curious about machine learning fundamentals
Aspiring machine learning developers

Why audio?

Supplementary content for commute/exercise/chores will help solidify your book/course-work

What it's not

News and Interviews: TWiML and AI, O'Reilly Data Show, Talking machines
Misc Topics: Linear Digressions, Data Skeptic, Learning machines 101
iTunesU issues

Planned episodes

What is AI/ML: definition, comparison, history
Inspiration: automation, singularity, consciousness
ML Intuition: learning basics (infer/error/train); supervised/unsupervised/reinforcement; applications
Math overview: linear algebra, statistics, calculus
Linear models: supervised (regression, classification); unsupervised
Parts: regularization, performance evaluation, dimensionality reduction, etc
Deep models: neural networks, recurrent neural networks (RNNs), convolutional neural networks (convnets/CNNs)
Languages and Frameworks: Python vs R vs Java vs C/C++ vs MATLAB, etc; TensorFlow vs Torch vs Theano vs Spark, etc

MLG 001 Introduction

Machine Learning Guide