MLG 001 Introduction

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 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