MLG 001 Introduction
Machine Learning Guide
OCDevel
4.9 • 848 Ratings
🗓️ 1 February 2017
⏱️ 9 minutes
🧾️ Download transcript
Summary
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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
- "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
Transcript
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| 0:00.0 | Welcome to the first episode of Machine Learning Guide, or MLG, which is a podcast structured as an audio course, whose intent is to teach you the high-level principles of machine learning and artificial intelligence. |
| 0:15.1 | In this podcast, I will provide you a bird's eye overview of the fundamental concepts in machine learning. This includes things like |
| 0:22.5 | models and algorithms, both shallow learning models and deep learning models. Shallow learning |
| 0:28.7 | machine learning models include things like linear and logistic regression, naive bays, and |
| 0:33.7 | decision trees, which as a machine learning newbie you may not have heard of, but I will |
| 0:38.6 | also cover deep learning models, which I'm sure you have heard of, things like neural networks, |
| 0:44.4 | convolutional neural networks, and recurrent neural networks. |
| 0:47.6 | I'll discuss the languages and frameworks that you want to use in machine learning. |
| 0:52.5 | We'll talk about Python, TensorFlow, Psykit, Learn, |
| 0:56.3 | PiTorch, these types of topics. I'll discuss at a high level the math you need to know to succeed |
| 1:03.4 | in machine learning. This includes calculus, statistics, and linear algebra. And I will go into all |
| 1:09.8 | these topics in as much depth as audio allows, |
| 1:13.6 | and then I will provide you with the resources needed to deep dive any of these topics offline |
| 1:20.6 | to master the details that require a visual element, whether it be textbooks or videos. |
| 1:26.6 | This podcast is, of course, intended for anybody interested |
| 1:29.3 | in machine learning. But there tends to be two common subscribers to the podcast. The first is |
| 1:35.5 | managers and executives. They're interested in knowing just enough machine learning to be |
| 1:40.7 | dangerous, whether it's to assess what technologies are available to use in their projects |
| 1:46.0 | or at their company, or maybe they want to intelligently converse with their machine learning and data science employees. |
| 1:53.0 | The second are people who want to learn machine learning. Maybe they're considering pivoting from a different field into the machine learning field, |
| 2:02.7 | machine learning, artificial intelligence, and data science. |
| 2:06.3 | I myself come from a web and mobile app development background and decided that I wanted to become a machine learning engineer and self-taught myselfmachine learning and ended up getting work in the field. |
... |
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