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-curated resources for each episode's details at ocdevel.com. Audio is a great supplement during exercise, commute, chores, etc.

MLA 021 Databricks 06/22/2022

MLA 021 Databricks Discussing Databricks with Ming Chang from (part of ) /episode/index/show/machinelearningguide/id/23502782

MLA 020 Kubeflow 01/29/2022

MLA 020 Kubeflow Conversation with Dirk-Jan Kubeflow (vs cloud native solutions like SageMaker) - Data Scientist at Dept Agency . (From the website:) The Machine Learning Toolkit for Kubernetes. The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow. . If using TensorFlow with Kubeflow, combine with TFX for maximum power. (From the website:) TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines. When you're ready to move your models from research to production, use TFX to create and manage a production pipeline. Alternatives: /episode/index/show/machinelearningguide/id/21939530

MLA 019 DevOps 01/13/2022

MLA 019 DevOps Chatting with co-workers about the role of DevOps in a machine learning engineer's life Expert coworkers at Dept - Principal Software Developer - DevOps Lead (where Matt features often) Devops tools Pictures (funny and serious) /episode/index/show/machinelearningguide/id/21770120

MLA 018 Descript 11/07/2021

MLA 018 Descript (Optional episode) just showcasing a cool application using machine learning Dept uses Descript for some of their podcasting. I'm using it like a maniac, I think they're surprised at how into it I am. Check out the transcript & see how it performed. How to ship software, from the front lines. We talk with software developers about their craft, developer tools, developer productivity and what makes software development awesome. Hosted by your friends at Rocket Insights. AKA shipit.io An agency podcast with views on design, technology, art, and culture. Explore the new microsite at www.brandbeats.basicagency.com /episode/index/show/machinelearningguide/id/21074042

MLA 017 AWS Local Development 11/06/2021

MLA 017 AWS Local Development Show notes: Developing on AWS first (SageMaker or other) Consider developing against AWS as your local development environment, rather than only your cloud deployment environment. Solutions: Stick to AWS Cloud IDEs (, , Connect to deployed infrastructure via Infrastructure as Code /episode/index/show/machinelearningguide/id/21070127

MLA 016 SageMaker 2 11/05/2021

MLA 016 SageMaker 2 Part 2 of deploying your ML models to the cloud with SageMaker (MLOps) MLOps is deploying your ML models to the cloud. See for an overview of tooling (also generally a great ML educational run-down.) /episode/index/show/machinelearningguide/id/21059909

MLA 015 SageMaker 1 11/04/2021

MLA 015 SageMaker 1 Part 1 of deploying your ML models to the cloud with SageMaker (MLOps) MLOps is deploying your ML models to the cloud. See for an overview of tooling (also generally a great ML educational run-down.) And I forgot to mention , I'll mention next time. /episode/index/show/machinelearningguide/id/21048182

MLA 014 Machine Learning Server 01/18/2021

MLA 014 Machine Learning Server Server-side ML. Training & hosting for inference, with a goal towards serverless. AWS SageMaker, Batch, Lambda, EFS, Cortex.dev /episode/index/show/machinelearningguide/id/17581607

MLA 013 Customer Facing Tech Stack 01/03/2021

MLA 013 Customer Facing Tech Stack Client, server, database, etc. /episode/index/show/machinelearningguide/id/17400590

MLA 012 Docker 11/09/2020

MLA 012 Docker Use Docker for env setup on localhost & cloud deployment, instead of pyenv / Anaconda. I recommend Windows for your desktop. /episode/index/show/machinelearningguide/id/16726955

MLG 032 Cartesian Similarity Metrics 11/08/2020

MLG 032 Cartesian Similarity Metrics Show notes at . L1/L2 norm, Manhattan, Euclidean, cosine distances, dot product Normed distances A norm is a function that assigns a strictly positive length to each vector in a vector space. 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 triangle edge) Others: Mahalanobis, Chebyshev (p=inf), etc Dot product A type of inner product. Outer-product: lies outside the involved planes. Inner-product: dot product lies inside the planes/axes involved . Dot product: inner product on a finite dimensional Euclidean space Cosine (normalized dot) /episode/index/show/machinelearningguide/id/16722518

MLA 011 Practical Clustering 11/08/2020

MLA 011 Practical Clustering Kmeans (sklearn vs FAISS), finding n_clusters via inertia/silhouette, Agglomorative, DBSCAN/HDBSCAN /episode/index/show/machinelearningguide/id/16725809

MLA 010 NLP packages: transformers, spaCy, Gensim, NLTK 10/28/2020

MLA 010 NLP packages: transformers, spaCy, Gensim, NLTK NLTK: swiss army knife. Gensim: LDA topic modeling, n-grams. spaCy: linguistics. transformers: high-level business NLP tasks. /episode/index/show/machinelearningguide/id/16621373

MLA 009 Charting tools 11/06/2018

MLA 009 Charting tools matplotlib, Seaborn, Bokeh, D3, Tableau, Power BI, QlikView, Excel /episode/index/show/machinelearningguide/id/16622930

MLA 008 Exploratory Data Analysis 10/26/2018

MLA 008 Exploratory Data Analysis EDA + charting. DataFrame info/describe, imputing strategies. Useful charts like histograms and correlation matrices. /episode/index/show/machinelearningguide/id/16622954

MLA 007 Jupyter Notebooks 10/16/2018

MLA 007 Jupyter Notebooks Run your code + visualizations in the browser: iPython / Jupyter Notebooks. /episode/index/show/machinelearningguide/id/16622969

MLA 006 Salary 07/19/2018

MLA 006 Salary Salary based on location, gender, age, tech... from O'Reilly. /episode/index/show/machinelearningguide/id/16622978

MLA 005 Shapes & Sizes 06/09/2018

MLA 005 Shapes & Sizes Dimensions, size, and shape of Numpy ndarrays / TensorFlow tensors, and methods for transforming those. /episode/index/show/machinelearningguide/id/16622984

MLA 003 Storage: HDF, Pickle, Postgres 05/24/2018

MLA 003 Storage: HDF, Pickle, Postgres Comparison of different data storage options when working with your ML models. /episode/index/show/machinelearningguide/id/16622999

MLA 002 Numpy & Pandas 05/24/2018

MLA 002 Numpy & Pandas Some numerical data nitty-gritty in Python. /episode/index/show/machinelearningguide/id/16623014

MLA 001 Certificates & Degrees 05/24/2018

MLA 001 Certificates & Degrees Reboot on the MLG episode, with more confident recommends. /episode/index/show/machinelearningguide/id/16623032

MLG 029 Reinforcement Learning Intro 02/05/2018

MLG 029 Reinforcement Learning Intro Introduction to reinforcement learning concepts. ocdevel.com/mlg/29 for notes and resources. /episode/index/show/machinelearningguide/id/6226276

MLG 028 Hyperparameters 2 02/04/2018

MLG 028 Hyperparameters 2 Hyperparameters part 2: hyper-search, regularization, SGD optimizers, scaling. ocdevel.com/mlg/28 for notes and resources /episode/index/show/machinelearningguide/id/6222761

MLG 027 Hyperparameters 1 01/28/2018

MLG 027 Hyperparameters 1 Hyperparameters part 1: network architecture. ocdevel.com/mlg/27 for notes and resources /episode/index/show/machinelearningguide/id/6195814

MLG 026 Project Bitcoin Trader 01/27/2018

MLG 026 Project Bitcoin Trader Community project & intro to Bitcoin/crypto + trading. ocdevel.com/mlg/26 for notes and resources /episode/index/show/machinelearningguide/id/6194090

MLG 025 Convolutional Neural Networks 10/30/2017

MLG 025 Convolutional Neural Networks Convnets or CNNs. Filters, feature maps, window/stride/padding, max-pooling. ocdevel.com/mlg/25 for notes and resources /episode/index/show/machinelearningguide/id/5890712

MLG 024 Tech Stack 10/07/2017

MLG 024 Tech Stack TensorFlow, Pandas, Numpy, Scikit-Learn, Keras, TensorForce. ocdevel.com/mlg/24 for notes and resources /episode/index/show/machinelearningguide/id/5816352

MLG 023 Deep NLP 2 08/20/2017

MLG 023 Deep NLP 2 RNN review, bi-directional RNNs, LSTM & GRU cells. ocdevel.com/mlg/23 for notes and resources /episode/index/show/machinelearningguide/id/5660423

MLG 022 Deep NLP 1 07/29/2017

MLG 022 Deep NLP 1 Recurrent Neural Networks (RNNs) and Word2Vec. ocdevel.com/mlg/22 for notes and resources /episode/index/show/machinelearningguide/id/5589161

MLG 020 Natural Language Processing 3 07/23/2017

MLG 020 Natural Language Processing 3 Natural Language Processing classical/shallow algorithms. ocdevel.com/mlg/20 for notes and resources /episode/index/show/machinelearningguide/id/5566766

Machine Learning Guide

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