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Machine Learning Guide

Machine Learning Guide

By: OCDevel
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About this listen

 Machine learning audio course, teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models (shallow and deep), 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.OCDevel copyright 2025 Education
Episodes
  • MLG 001 Introduction

    MLG 001 Introduction
    Feb 1 2017

    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
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    8 mins
  • MLG 002 Difference Between Artificial Intelligence, Machine Learning, Data Science

    MLG 002 Difference Between Artificial Intelligence, Machine Learning, Data Science
    Feb 9 2017
    Artificial intelligence is the automation of tasks that require human intelligence, encompassing fields like natural language processing, perception, planning, and robotics, with machine learning emerging as the primary method to recognize patterns in data and make predictions. Data science serves as the overarching discipline that includes artificial intelligence and machine learning, focusing broadly on extracting knowledge and actionable insights from data using scientific and computational methods. Links Notes and resources at ocdevel.com/mlg/2 Try a walking desk stay healthy & sharp while you learn & code Data Science Overview Data science encompasses any professional role that deals extensively with data, including but not limited to artificial intelligence and machine learning.The data science pipeline includes data ingestion, storage, cleaning (feature engineering), and outputs in data analytics, business intelligence, or machine learning.A data lake aggregates raw data from multiple sources, while a feature store holds cleaned and transformed data, prepared for analysis or model training.Data analysts and business intelligence professionals work primarily with data warehouses to generate human-readable reports, while machine learning engineers use transformed data to build and deploy predictive models.At smaller organizations, one person ("data scientist") may perform all data pipeline roles, whereas at large organizations, each phase may be specialized.Wikipedia: Data Science describes data science as the interdisciplinary field for extracting knowledge and insights from structured and unstructured data. Artificial Intelligence: Definition and Sub-disciplines Artificial intelligence (AI) refers to the theory and development of computer systems capable of performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. (Wikipedia: Artificial Intelligence)The AI discipline is divided into subfields: Reasoning and problem solvingKnowledge representation (such as using ontologies or knowledge graphs)Planning (selecting actions in an environment, e.g., chess- or Go-playing bots, self-driving cars)LearningNatural language processing (simulated language, machine translation, chatbots, speech recognition, question answering, summarization)Perception (AI perceives the world with sensors; e.g., cameras, microphones in self-driving cars)Motion and manipulation (robotics, transforming decisions into physical actions via actuators)Social intelligence (AI tuned to human emotions, sentiment analysis, emotion recognition)General intelligence (Artificial General Intelligence, or AGI: a system that generalizes across all domains at or beyond human skill) Applications of AI include autonomous vehicles, medical diagnosis, creating art, proving theorems, playing strategy games, search engines, digital assistants, image recognition, spam filtering, judicial decision prediction, and targeted online advertising.AI has both objective definitions (automation of intellectual tasks) and subjective debates around the threshold for "intelligence."The Turing Test posits that if a human cannot distinguish an AI from another human through conversation, the AI can be considered intelligent.Weak AI targets specific domains, while general AI aspires to domain-independent capability.AlphaGo Movie depicts the use of AI planning and learning in the game of Go. Machine Learning: Within AI Machine learning (ML) is a subdiscipline of AI focused on building models that learn patterns from data and make predictions or decisions. (Wikipedia: Machine Learning)Machine learning involves feeding data (such as spreadsheets of stock prices) into algorithms that detect patterns (learning phase) and generate models, which are then used to predict future outcomes.Although ML started as a distinct subfield, in recent years it has subsumed many of the original AI subdisciplines, becoming the primary approach in areas like natural language processing, computer vision, reasoning, and planning.Deep learning has driven this shift, employing techniques such as neural networks, convolutional networks (image processing), and transformers (language tasks), allowing generalizable solutions across multiple domains.Reinforcement learning, a form of machine learning, enables AI systems to learn sequences of actions in complex environments, such as games or real-world robotics, by maximizing cumulative rewards.Modern unified ML models, such as Google’s Pathways and transformer architectures, can now tackle tasks in multiple subdomains (vision, language, decision-making) with a single framework. Data Pipeline and Roles in Data Science Data engineering covers obtaining and storing raw data from various data sources (datasets, databases, streams), aggregating into data lakes, and applying schema or permissions.Feature engineering cleans and transforms raw data (imputation,...
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    1 hr and 5 mins
  • MLG 003 Inspiration

    MLG 003 Inspiration
    Feb 10 2017

    Try a walking desk to stay healthy while you study or work!

    Show notes at ocdevel.com/mlg/3.

    This episode covers four major philosophical topics related to artificial intelligence. The purpose is to give broader context to why AI matters, before moving into technical details in later episodes.

    1. Economic Automation

    AI is automating not just simple tasks like data entry or tax prep, but also high-skill jobs such as medical diagnostics, surgery, and creative work like design, music, and art. There are two common reactions:

    • Fear: Concern over job displacement, similar to past economic shifts like the agricultural and industrial revolutions.
    • Is your job safe?
    • Optimism: Automation may lead to more comfortable living conditions and economic structures like Universal Basic Income. New job types could emerge, as they have in past transitions.
    2. The Singularity

    The singularity refers to a point of runaway technological growth, where AI becomes capable of improving itself recursively. This concept is tied to "artificial general intelligence" and "seed AI"—systems that not only perform tasks but create better versions of themselves. The idea is that this could trigger extremely rapid change, possibly representing a new phase of evolution beyond humanity.

    3. Consciousness

    I explore whether consciousness can emerge from machines. Since the brain is a physical machine and consciousness arises from it, it's possible that artificial systems could develop similar properties. Related ideas:

    • Qualia: Subjective experiences.
    • Functionalism: If something behaves like it’s conscious, it may be conscious.
    • Turing Test: If a machine is indistinguishable from a human in conversation, it passes the test.
    4. Misaligned Goals and Risk

    I discuss scenarios where AI causes harm not through malevolence but through poorly defined objectives. One example is the "paperclip maximizer" thought experiment, where an AI tasked with maximizing paperclip production might consume all resources to do so. This has led some public figures to raise concerns about AI safety. I don't share the same level of concern, but the topic is worth being aware of.

    References
    • Ray Kurzweil, The Singularity is Near
    • Ray Kurzweil, How to Create a Mind
    • Daniel Dennett, Consciousness Explained
    • Nick Bostrom, Superintelligence
    • The Great Courses, Philosophy of Mind, Brain, Consciousness, and Thinking Machines

    In the next episode, I begin covering the technical foundations of machine learning, starting with supervised, unsupervised, and reinforcement learning.
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    19 mins
Most relevant  
Fell upon this series completely by accident while searching for some introduction to ML I could listen to while commuting and wow what a series!

It’s so well paced and informative, so many good recommendations and explained so excellently over audio. It’s literally transformed my view and involvement with ML in a huge way.

Definitely recommend for anyone interested in, starting to study or looking for an ML resource in audio.

The best Machine Learning audio resource out there!

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Have been learning ML and currently doing a project, this podcast was a brilliant supplementary resource!

Fantastic Resource for ML

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