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Machine Learning Guide

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-c

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New episodes: Every two weeks
Typical length: 35 min
Latest: 26 Feb 2026
Language: English

Latest episode

26 Feb 2026 · 36 min

MLA 030 AI and Programming Jobs: What Happened and How to Position

The aggregate job market held, the entry-level door narrowed, and software postings sit a quarter below pre-pandemic. Why cheap implementation made specification, verification and domain scarce, how ML roles split five ways, and how to position. Links Try a walking desk - stay healthy & sharp while you learn & code More OCDevel shows - this one has siblings, each on its own subject and produced the same way What coding agents did to programming and machine learning jobs by late 2026: the labor data, the mechanism behind it, how the ML career splintered into five roles, and a concrete…

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Earlier episodes 29 most recent

S1 · E65 · 22 Feb 2026 · 37 min
MLA 029 OpenClaw and Personal Agents
OpenClaw as the worked example of the always-on personal agent: gateway, markdown memory, heartbeats, skills, coding agents from your phone, hosted vs local models, and the 2026 security record (exposed instances, two critical CVEs, ClawHavoc) with the posture that makes it survivable. Links Try a walking desk - stay healthy & sharp while you learn & code More OCDevel shows - this one has siblings, each on its own subject and produced the same way Second and last episode of the agents pair. AI Agents in 2026 covered the theory: loops, tools, memory, protocols, SDKs, evaluation. This one…
S1 · E64 · 22 Feb 2026 · 34 min
MLA 028 AI Agents: Loops, Tools, Memory, Protocols, and Evaluation
What an AI agent actually is, why coding agents got good first, how memory really works, what MCP and A2A standardize, which SDKs are alive, how to evaluate on trajectories, and where the products stand after browser agents contracted. Links Try a walking desk - stay healthy & sharp while you learn & code More OCDevel shows - this one has siblings, each on its own subject and produced the same way First of two episodes on AI agents. This one is the architecture: the loop, tools and verifiable feedback, memory, the protocols (MCP, A2A, computer use), the SDK landscape, evaluation and…
S1 · E63 · 14 Jul 2025 · 35 min
MLA 027 The AI Media Pipeline: Voice, Music, ComfyUI, APIs, and Finishing
How to automate AI media end to end: clone your own voice on open TTS, pick music that's actually licensed, run ComfyUI graphs headless, design around fal, Replicate and provider queues, finish with ffmpeg, and stay inside licensing at every layer. Links More OCDevel shows - this one has siblings, each on its own subject and produced the same way Companion show. This episode is the overview of the media pipeline. For weekly, hands-on coverage of the video half, from a first usable clip to scenes that cut together, listen to AI Video Generation on Gnothi. Try a walking desk - stay healthy &…
S1 · E62 · 12 Jul 2025 · 32 min
MLA 026 AI Video Generation 2026: Veo, Gemini, Kling, Runway, MiniMax, Sora
Sora is shut down, Google runs two video models, Kling 3 does lip-synced dialogue, and open-weight MiniMax H3 is what you can actually fine-tune. What a usable clip costs, which models do native audio, how reference consistency works, and why the unit of work is the shot. Links Notes and resources at ocdevel.com/mlg/mla-26 More OCDevel shows - this one has siblings, each on its own subject and produced the same way. Companion show: for weekly, hands-on coverage of the AI video pipeline, from a first usable clip to scenes that cut together, listen to AI Video Generation . Try a walking desk -…
S1 · E61 · 9 Jul 2025 · 31 min
MLA 025 AI Image Generation 2026: GPT Image, Nano Banana, Midjourney, Flux
Editing replaced generation as the core task. How GPT Image 2.5, Google's Nano Banana line, Midjourney V8.2 and Flux 2 differ, what open weights and LoRAs buy you, ControlNet vs instruction editing, and how licensing and C2PA provenance work now. Links More OCDevel shows - this one has siblings, each on its own subject and produced the same way Companion show. This episode is the overview. For weekly, hands-on coverage of the full image and video pipeline, from a first usable clip to scenes that cut together, listen to AI Video Generation . Try a walking desk - stay healthy & sharp while you…
S1 · E60 · 30 May 2025 · 1 hr 6 min
MLG 036 Autoencoders
Auto encoders are neural networks that compress data into a smaller "code," enabling dimensionality reduction, data cleaning, and lossy compression by reconstructing original inputs from this code. Advanced auto encoder types, such as denoising, sparse, and variational auto encoders, extend these concepts for applications in generative modeling, interpretability, and synthetic data generation. Links Notes and resources at ocdevel.com/mlg/36 Try a walking desk - stay healthy & sharp while you learn & code Build the future of multi-agent software with AGNTCY . Thanks to T.J. Wilder from…
S1 · E59 · 8 May 2025 · 45 min
MLG 035 Large Language Models 2
At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup using cosine similarity. LLM agents autonomously plan, act, and use external tools via orchestrated loops with persistent memory, while recent benchmarks like GPQA (STEM reasoning), SWE Bench (agentic coding), and MMMU (multimodal college-level tasks) test performance alongside prompt engineering techniques such as…
S1 · E58 · 7 May 2025 · 51 min
MLG 034 Large Language Models 1
Explains language models (LLMs) advancements. Scaling laws - the relationships among model size, data size, and compute - and how emergent abilities such as in-context learning, multi-step reasoning, and instruction following arise once certain scaling thresholds are crossed. The evolution of the transformer architecture with Mixture of Experts (MoE), describes the three-phase training process culminating in Reinforcement Learning from Human Feedback (RLHF) for model alignment, and explores advanced reasoning techniques such as chain-of-thought prompting which significantly improve complex…
S1 · E57 · 13 Apr 2025 · 33 min
MLA 024 Agentic Software Engineering: Specs, Verification, and the Review Loop
How working engineers ship with coding agents: issues an agent can verify, plan mode before code, a verification loop with a browser in it, agent review of agent code, worktrees and CI, cost discipline, and where agents still fail. Links Try a walking desk - stay healthy & sharp while you learn & code More OCDevel shows - this one has siblings, each on its own subject and produced the same way Third and last episode of the vibe-coding sequence. Vibe Coding in 2026 picked an agent; Inside a Coding Agent explained the mechanics. This one is the practice: how working engineers ship real…
S1 · E56 · 13 Apr 2025 · 34 min
MLA 023 Inside a Coding Agent: Context, Instructions, Hooks, Skills, MCP
A coding agent is a loop appending to a context window, and the window is what runs out. Instruction files, permissions, hooks, skills, MCP, subagents and memory explained as one mechanism, mapped across Claude Code, Codex and OpenCode. Links Try a walking desk - stay healthy & sharp while you learn & code More OCDevel shows - this one has siblings, each on its own subject and produced the same way Second of three episodes on coding agents: how Claude Code, Codex, OpenCode and pi handle context, instruction files, permissions, hooks, skills, MCP, subagents and memory. Claude Code is the…
S1 · E55 · 9 Feb 2025 · 28 min
MLA 022 Vibe Coding: Codex vs Claude Code vs Antigravity vs Grok
What a coding agent actually is, and how Claude Code, OpenAI Codex, Google Antigravity and xAI's Grok Build differ in philosophy rather than features. Plus open harnesses pi and OpenCode, the open-weight coders, and a decision rule by constraint. Links Try a walking desk - stay healthy & sharp while you learn & code More OCDevel shows - this one has siblings, each on its own subject and produced the same way First of three episodes on vibe coding: the field of coding agents (Claude Code, OpenAI Codex, Google Antigravity, Grok Build, pi, OpenCode) as of late 2026 and how to choose one. The…
S1 · E54 · 9 Feb 2025 · 43 min
MLG 033 Transformers
Links : Notes and resources at ocdevel.com/mlg/33 3Blue1Brown videos: https://3blue1brown.com/ Try a walking desk stay healthy & sharp while you learn & code Try Descript audio/video editing with AI power-tools Background & Motivation RNN Limitations: Sequential processing prevents full parallelization—even with attention tweaks—making them inefficient on modern hardware. Breakthrough: "Attention Is All You Need" replaced recurrence with self-attention, unlocking massive parallelism and scalability. Core Architecture Layer Stack: Consists of alternating self-attention and feed-forward (MLP)…
S1 · E53 · 22 Jun 2022 · 26 min
MLA 021 Databricks: Cloud Analytics and MLOps
Databricks is a cloud-based platform for data analytics and machine learning operations, integrating features such as a hosted Spark cluster, Python notebook execution, Delta Lake for data management, and seamless IDE connectivity. Raybeam utilizes Databricks and other ML Ops tools according to client infrastructure, scaling needs, and project goals, favoring Databricks for its balanced feature set, ease of use, and support for both startups and enterprises. Links Notes and resources at ocdevel.com/mlg/mla-21 Try a walking desk stay healthy & sharp while you learn & code Raybeam and…
S1 · E52 · 29 Jan 2022 · 1 hr 9 min
MLA 020 Kubeflow and ML Pipeline Orchestration on Kubernetes
Machine learning pipeline orchestration tools, such as SageMaker and Kubeflow, streamline the end-to-end process of data ingestion, model training, deployment, and monitoring, with Kubeflow providing an open-source, cross-cloud platform built atop Kubernetes. Organizations typically choose between cloud-native managed services and open-source solutions based on required flexibility, scalability, integration with existing cloud environments, and vendor lock-in considerations. Links Notes and resources at ocdevel.com/mlg/mla-20 Try a walking desk stay healthy & sharp while you learn & code…
S1 · E51 · 13 Jan 2022 · 1 hr 15 min
MLA 019 Cloud, DevOps & Architecture
The deployment of machine learning models for real-world use involves a sequence of cloud services and architectural choices, where machine learning expertise must be complemented by DevOps and architecture skills, often requiring collaboration with professionals. Key concepts discussed include infrastructure as code, cloud container orchestration, and the distinction between DevOps and architecture, as well as practical advice for machine learning engineers wanting to deploy products securely and efficiently. Links Notes and resources at ocdevel.com/mlg/mla-19 Try a walking desk stay…
S1 · E49 · 6 Nov 2021 · 1 hr 5 min
MLA 017 AWS Local Development Environment
AWS development environments for local and cloud deployment can differ significantly, leading to extra complexity and setup during cloud migration. By developing directly within AWS environments, using tools such as Lambda, Cloud9, SageMaker Studio, client VPN connections, or LocalStack, developers can streamline transitions to production and leverage AWS-managed services from the start. This episode outlines three primary strategies for treating AWS as your development environment, details the benefits and tradeoffs of each, and explains the role of infrastructure-as-code tools such as…
S1 · E48 · 5 Nov 2021 · 1 hr
MLA 016 AWS SageMaker MLOps 2
SageMaker streamlines machine learning workflows by enabling integrated model training, tuning, deployment, monitoring, and pipeline automation within the AWS ecosystem, offering scalable compute options and flexible development environments. Cloud-native AWS machine learning services such as Comprehend and Poly provide off-the-shelf solutions for NLP, time series, recommendations, and more, reducing the need for custom model implementation and deployment. Links Notes and resources at ocdevel.com/mlg/mla-16 Try a walking desk stay healthy & sharp while you learn & code Model Training and…
S1 · E47 · 4 Nov 2021 · 48 min
MLA 015 AWS SageMaker MLOps 1
SageMaker is an end-to-end machine learning platform on AWS that covers every stage of the ML lifecycle, including data ingestion, preparation, training, deployment, monitoring, and bias detection. The platform offers integrated tools such as Data Wrangler, Feature Store, Ground Truth, Clarify, Autopilot, and distributed training to enable scalable, automated, and accessible machine learning operations for both tabular and large data sets. Links Notes and resources at ocdevel.com/mlg/mla-15 Try a walking desk stay healthy & sharp while you learn & code Amazon SageMaker: The Machine Learning…
S1 · E46 · 18 Jan 2021 · 36 min
MLA 014 Hosting and Deploying ML: Managed APIs, Serverless GPUs, Self-Hosting
Where the model behind your product should run in 2026: managed APIs vs open weights, AWS native vs Modal, RunPod and Cloud Run GPU, vLLM and SGLang, quantized CPU inference on Spot, batch pricing, prod evals, and infra as code. Links Try a walking desk - stay healthy & sharp while you learn & code More OCDevel shows - this one has siblings, each on its own subject and produced the same way The infrastructure episode, standalone: where the model behind your product runs in 2026, and what each choice costs. Managed APIs versus self-hosted open weights, AWS native (Bedrock, SageMaker) versus…
S1 · E45 · 3 Jan 2021 · 48 min
MLA 013 Tech Stack for Customer-Facing Machine Learning Products
Primary technology recommendations for building a customer-facing machine learning product include React and React Native for the front end, serverless platforms like AWS Amplify or GCP Firebase for authentication and basic server/database needs, and Postgres as the relational database of choice. Serverless approaches are encouraged for scalability and security, with traditional server frameworks and containerization recommended only for advanced custom backend requirements. When serverless options are inadequate, use Node.js with Express or FastAPI in Docker containers, and consider adding…
S1 · E44 · 9 Nov 2020 · 32 min
MLA 012 Docker for Machine Learning Workflows
Docker enables efficient, consistent machine learning environment setup across local development and cloud deployment, avoiding many pitfalls of virtual machines and manual dependency management. It streamlines system reproduction, resource allocation, and GPU access, supporting portability and simplified collaboration for ML projects. Machine learning engineers benefit from using pre-built Docker images tailored for ML, allowing seamless project switching, host OS flexibility, and straightforward deployment to cloud platforms like AWS ECS and Batch, resulting in reproducible and…
S1 · E43 · 8 Nov 2020 · 42 min
MLG 032 Cartesian Similarity Metrics
Try a walking desk to stay healthy while you study or work! Show notes at ocdevel.com/mlg/32 . L1/L2 norm, Manhattan, Euclidean, cosine distances, dot product Normed distances link A norm is a function that assigns a strictly positive length to each vector in a vector space. link Minkowski is generalized. p_root(sum(xi-yi)^p) . "p" = ? (1, 2, ..) for below. L1: Manhattan/city-block/taxicab. abs(x2-x1)+abs(y2-y1) . Grid-like distance (triangle legs). Preferred for high-dim space. L2: Euclidean. sqrt((x2-x1)^2+(y2-y1)^2 . sqrt(dot-product) . Straight-line distance; min distance (Pythagorean…
S1 · E42 · 8 Nov 2020 · 35 min
MLA 011 Practical Clustering Tools
Primary clustering tools for practical applications include K-means using scikit-learn or Faiss, agglomerative clustering leveraging cosine similarity with scikit-learn, and density-based methods like DBSCAN or HDBSCAN. For determining the optimal number of clusters, silhouette score is generally preferred over inertia-based visual heuristics, and it natively supports pre-computed distance matrices. Links Notes and resources at ocdevel.com/mlg/mla-11 Try a walking desk stay healthy & sharp while you learn & code K-means Clustering K-means is the most widely used clustering algorithm and is…
S1 · E41 · 28 Oct 2020 · 26 min
MLA 010 NLP packages: transformers, spaCy, Gensim, NLTK
The landscape of Python natural language processing tools has evolved from broad libraries like NLTK toward more specialized packages such as Gensim for topic modeling, SpaCy for linguistic analysis, and Hugging Face Transformers for advanced tasks, with Sentence Transformers extending transformer models to enable efficient semantic search and clustering. Each library occupies a distinct place in the NLP workflow, from fundamental text preprocessing to semantic document comparison and large-scale language understanding. Links Notes and resources at ocdevel.com/mlg/mla-10 Try a walking desk…
S1 · E39 · 6 Nov 2018 · 25 min
MLA 009 Charting and Visualization Tools for Data Science
Python charting libraries - Matplotlib, Seaborn, and Bokeh - explaining, their strengths from quick EDA to interactive, HTML-exported visualizations, and clarifies where D3.js fits as a JavaScript alternative for end-user applications. It also evaluates major software solutions like Tableau, Power BI, QlikView, and Excel, detailing how modern BI tools now integrate drag-and-drop analytics with embedded machine learning, potentially allowing business users to automate entire workflows without coding. Links Notes and resources at ocdevel.com/mlg/mla-9 Try a walking desk stay healthy & sharp…
S1 · E38 · 26 Oct 2018 · 25 min
MLA 008 Exploratory Data Analysis (EDA)
Exploratory data analysis (EDA) sits at the critical pre-modeling stage of the data science pipeline, focusing on uncovering missing values, detecting outliers, and understanding feature distributions through both statistical summaries and visualizations, such as Pandas' info(), describe(), histograms, and box plots. Visualization tools like Matplotlib, along with processes including imputation and feature correlation analysis, allow practitioners to decide how best to prepare, clean, or transform data before it enters a machine learning model. Links Notes and resources at…
S1 · E37 · 16 Oct 2018 · 17 min
MLA 007 Jupyter Notebooks
Jupyter Notebooks, originally conceived as IPython Notebooks, enable data scientists to combine code, documentation, and visual outputs in an interactive, browser-based environment supporting multiple languages like Python, Julia, and R. This episode details how Jupyter Notebooks structure workflows into executable cells - mixing markdown explanations and inline charts - which is essential for documenting, demonstrating, and sharing data analysis and machine learning pipelines step by step. Links Notes and resources at ocdevel.com/mlg/mla-7 Try a walking desk stay healthy & sharp while you…
S1 · E36 · 19 Jul 2018 · 20 min
MLA 006 Salaries for Data Science & Machine Learning
O'Reilly's 2017 Data Science Salary Survey finds that location is the most significant salary determinant for data professionals, with median salaries ranging from $134,000 in California to under $30,000 in Eastern Europe, and highlights that negotiation skills can lead to salary differences as high as $45,000. Other key factors impacting earnings include company age and size, job title, industry, and education, while popular tools and languages—such as Python, SQL, and Spark—do not strongly influence salary despite widespread use. Links Notes and resources at ocdevel.com/mlg/mla-6 Try a…
S1 · E35 · 9 Jun 2018 · 27 min
MLA 005 Shapes and Sizes: Tensors and NDArrays
Explains the fundamental differences between tensor dimensions, size, and shape, clarifying frequent misconceptions—such as the distinction between the number of features ("columns") and true data dimensions—while also demystifying reshaping operations like expand_dims, squeeze, and transpose in NumPy. Through practical examples from images and natural language processing, listeners learn how to manipulate tensors to match model requirements, including scenarios like adding dummy dimensions for grayscale images or reordering axes for sequence data. Links Notes and resources at…

About Machine Learning Guide

Machine Learning Guide is a English learning podcast from OCDevel. Melo plays each episode straight from the publisher's own feed — no ads added, no account needed — and remembers where you stopped, in this browser only.

Publisher: OCDevel
Language: English
Category: Learning
New episodes: Every two weeks
Typical length: 35 min
Latest episode: 26 Feb 2026
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