Introduction to the Machine Learning Guide
Who am I: Tyler Renelle (https://www.linkedin.com/in/lefnire)
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 (maybe transitioning from web/mobile development)
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 (https://twimlai.com)
** O'Reilly Data Show (https://www.oreilly.com/topics/oreilly-data-show-podcast)
** Talking machines (http://www.thetalkingmachines.com/)
- Misc Topics
** Linear Digressions (http://lineardigressions.com/)
** Data Skeptic (https://dataskeptic.com/)
** Partially Derivative (http://partiallyderivative.com/)
- iTunesU issues
- Learning machines 101 (http://www.learningmachines101.com/)
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
