Machine Learning Guide – Détails, épisodes et analyse

Détails du podcast

Informations techniques et générales issues du flux RSS du podcast.

Machine Learning Guide

OCDevel

Technologie

Éducation

Fréquence : 1 épisode/57j. Total Éps: 60

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.

Lire plusRéduire

Site

RSS

Apple

Classements récents

Dernières positions dans les classements Apple Podcasts et Spotify.

Apple Podcasts

🇨🇦 Canada - technology
19/08/2026
#98
🇨🇦 Canada - technology
11/08/2026
#98
🇩🇪 Allemagne - technology
11/08/2026
#75
🇩🇪 Allemagne - technology
02/08/2026
#96
🇨🇦 Canada - technology
28/07/2026
#84
🇬🇧 Grande Bretagne - technology
25/07/2026
#78
🇬🇧 Grande Bretagne - technology
23/07/2026
#88
🇨🇦 Canada - technology
22/07/2026
#75
🇬🇧 Grande Bretagne - technology
22/07/2026
#89
🇬🇧 Grande Bretagne - technology
18/07/2026
#98

Spotify

Aucun classement récent disponible

Liens partagés entre épisodes et podcasts

Liens présents dans les descriptions d'épisodes et autres podcasts les utilisant également.

https://ocdevel.com/llh
48 partages
https://ocdevel.com/blog/20240109-fitness-desk
44 partages
https://ocdevel.com/walk
12 partages

https://aws.amazon.com/batch/
6 partages
https://aws.amazon.com/sagemaker/
2 partages

Qualité et score du flux RSS

Évaluation technique de la qualité et de la structure du flux RSS.

Qualité du flux RSS

Correct

Score global : 73%

Historique des publications

Répartition mensuelle des publications d'épisodes au fil des années.

Year

Derniers épisodes publiés

Liste des épisodes récents, avec titres, durées et descriptions.

MLA 021 Databricks: Cloud Analytics and MLOps

Saison 1 · Épisode 53

mercredi 22 juin 2022 • Durée 26:28

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 Databricks

Raybeam is a data science and analytics company, recently acquired by Dept Agency.
While Raybeam focuses on data analytics, its acquisition has expanded its expertise into ML Ops and AI.
The company recommends tools based on client requirements, frequently utilizing Databricks for its comprehensive nature.

Understanding Databricks

Databricks is not merely an analytics platform; it is a competitor in the ML Ops space alongside tools like SageMaker and Kubeflow.
It provides interactive notebooks, Python code execution, and runs on a hosted Apache Spark cluster.
Databricks includes Delta Lake, which acts as a storage and data management layer.

Choosing the Right MLOps Tool

Raybeam evaluates each client's needs, existing expertise, and infrastructure before recommending a platform.
Databricks, SageMaker, Kubeflow, and Snowflake are common alternatives, with the final selection dependent on current pipelines and operational challenges.
Maintaining existing workflows is prioritized unless scalability or feature limitations necessitate migration.

Databricks Features

Databricks is accessible via a web interface similar to Jupyter Hub and can be integrated with local IDEs (e.g., VS Code, PyCharm) using Databricks Connect.
Notebooks on Databricks can be version-controlled with Git repositories, enhancing collaboration and preventing data loss.
The platform supports configuration of computing resources to match model size and complexity.
Databricks clusters are hosted on AWS, Azure, or GCP, with users selecting the underlying cloud provider at sign-up.

Parquet and Delta Lake

Parquet files store data in a columnar format, which improves efficiency for aggregation and analytics tasks.
Delta Lake provides transactional operations on top of Parquet files by maintaining a version history, enabling row edits and deletions.
This approach offers a database-like experience for handling large datasets, simplifying both analytics and machine learning workflows.

Pricing and Usage

Pricing for Databricks depends on the chosen cloud provider (AWS, Azure, or GCP) with an additional fee for Databricks' services.
The added cost is described as relatively small, and the platform is accessible to both individual developers and large enterprises.
Databricks is recommended for newcomers to data science and ML for its breadth of features and straightforward setup.

Databricks, MLflow, and Other Integrations

Databricks provides a hosted MLflow solution, offering experiment tracking and model management.
The platform can access data stored in services like S3, Snowflake, and other cloud provider storage options.
Integration with tools such as PyArrow is supported, facilitating efficient data access and manipulation.

Example Use Cases and Decision Process

Migration to Databricks is recommended when a client's existing infrastructure (e.g., on-premises Spark clusters) cannot scale effectively.
The selection process involves an in-depth exploration of a client's operational challenges and goals.
Databricks is chosen for clients lacking feature-specific needs but requiring a unified data analytics and ML platform.

Personal Projects by Ming Chang

Ming Chang has explored automated stock trading using APIs such as Alpaca, focusing on downloading and analyzing market data.
He has also developed drone-related projects with Raspberry Pi, emphasizing real-world applications of programming and physical computing.

Additional Resources

Lire plusRéduire

MLA 020 Kubeflow and ML Pipeline Orchestration on Kubernetes

Saison 1 · Épisode 52

samedi 29 janvier 2022 • Durée 01:08:47

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

Dirk-Jan Verdoorn - Data Scientist at Dept Agency

Managed vs. Open-Source ML Pipeline Orchestration

Cloud providers such as AWS, Google Cloud, and Azure offer managed machine learning orchestration solutions, including SageMaker (AWS) and Vertex AI (GCP).
Managed services provide integrated environments that are easier to set up and operate but often result in vendor lock-in, limiting portability across cloud platforms.
Open-source tools like Kubeflow extend Kubernetes to support end-to-end machine learning pipelines, enabling portability across AWS, GCP, Azure, or on-premises environments.

Introduction to Kubeflow

Kubeflow is an open-source project aimed at making machine learning workflow deployment on Kubernetes simple, portable, and scalable.
Kubeflow enables data scientists and ML engineers to build, orchestrate, and monitor pipelines using popular frameworks such as TensorFlow, scikit-learn, and PyTorch.
Kubeflow can integrate with TensorFlow Extended (TFX) for complete end-to-end ML pipelines, covering data ingestion, preprocessing, model training, evaluation, and deployment.

Machine Learning Pipelines: Concepts and Motivation Pipeline Orchestration Analogies and Advantages Choosing Between Managed and Open-Source Solutions Cross-Cloud and Local Development Relationship to TensorFlow Extended (TFX) and Machine Learning Frameworks Alternative Pipeline Orchestration Tools Selecting a Cloud Platform and Orchestration Approach Cost Optimization in Model Training Machine Learning Project Lifecycle Overview Educational Pathways for Data Science and Machine Learning Careers

MLG 032 Cartesian Similarity Metrics

Saison 1 · Épisode 43

dimanche 8 novembre 2020 • Durée 41:52

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 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 link. Dot product: inner product on a finite dimensional Euclidean space link

Cosine (normalized dot)

Lire plusRéduire

MLA 011 Practical Clustering Tools

Saison 1 · Épisode 42

dimanche 8 novembre 2020 • Durée 34:50

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 typically the first method to try for general clustering tasks.
The scikit-learn KMeans implementation is suitable for small to medium-sized datasets, while Faiss's kmeans is more efficient and accurate for very large datasets.
K-means requires the number of clusters to be specified in advance and relies on the Euclidean distance metric, which performs poorly in high-dimensional spaces.
When document embeddings have high dimensionality (e.g., 768 dimensions from sentence transformers), K-means becomes less effective due to the limitations of Euclidean distance in such spaces.

Alternatives to K-means for High Dimensions

For text embeddings with high dimensionality, agglomerative (hierarchical) clustering methods are preferable, particularly because they allow the use of different similarity metrics.
Agglomerative clustering in scikit-learn accepts a pre-computed cosine similarity matrix, which is more appropriate for natural language processing.
Constructing the pre-computed distance (or similarity) matrix involves normalizing vectors and computing dot products, which can be efficiently achieved with linear algebra libraries like PyTorch.

Semantic Search and Vector Indexing Determining the Optimal Number of Clusters Density-Based Clustering: DBSCAN and HDBSCAN Summary Recommendations and Links

MLA 010 NLP packages: transformers, spaCy, Gensim, NLTK

Saison 1 · Épisode 41

mercredi 28 octobre 2020 • Durée 26:22

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 stay healthy & sharp while you learn & code

Historical Foundation: NLTK

NLTK ("Natural Language Toolkit") was one of the earliest and most popular Python libraries for natural language processing, covering tasks from tokenization and stemming to document classification and syntax parsing.
NLTK remains a catch-all "Swiss Army knife" for NLP, but many of its functions have been supplemented or superseded by newer tools tailored to specific tasks.

Specialized Topic Modeling and Phrase Analysis: Gensim

Gensim emerged as the leading library for topic modeling in Python, most notably via its LDA Topic Modeling implementation, which groups documents according to topic distributions.
Topic modeling workflows often use NLTK for initial preprocessing (tokenization, stop word removal, lemmatization), then vectorize with scikit-learn's TF-IDF, and finally model topics with Gensim's LDA.
Gensim also provides effective Bigrams/Trigrams, allowing the detection and combination of commonly-used word pairs or triplets (n-grams) to enhance analysis accuracy.

Linguistic Structure and Manipulation: SpaCy and Related Tools High-Level NLP Tasks: Hugging Face Transformers Semantic Search and Clustering: Sentence Transformers Additional Resources and Library Landscape Summary of Library Roles and Use Cases

MLA 009 Charting and Visualization Tools for Data Science

Saison 1 · Épisode 39

mardi 6 novembre 2018 • Durée 24:43

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 while you learn & code

Core Phases in Data Science Visualization

Exploratory Data Analysis (EDA):
- EDA occupies an early stage in the Business Intelligence (BI) pipeline, positioned just before or sometimes merged with the data cleaning ("munging") phase.
- The outputs of EDA (e.g., correlation matrices, histograms) often serve as inputs to subsequent machine learning steps.

Python Visualization Libraries 1. Matplotlib

The foundational plotting library in Python, supporting static, basic chart types.
Requires substantial boilerplate code for custom visualizations.
Serves as the core engine for many higher-level visualization tools.
Common EDA tasks (like plotting via .corr(), .hist(), and .scatter() methods on pandas DataFrames) depend on Matplotlib under the hood.

2. Pandas Plotting

Pandas integrates tightly with Matplotlib and exposes simple, one-line commands for common plots (e.g., df.corr(), df.hist()).
Designed to make quick EDA accessible without requiring detailed knowledge of Matplotlib's verbose syntax.

3. Seaborn

A high-level wrapper around Matplotlib, analogous to how Keras wraps TensorFlow.

4. 5. Dedicated Visualization and BI Software Tableau Power BI QlikView Excel Trends & Insights

MLA 008 Exploratory Data Analysis (EDA)

Saison 1 · Épisode 38

vendredi 26 octobre 2018 • Durée 25:07

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 ocdevel.com/mlg/mla-8
Try a walking desk stay healthy & sharp while you learn & code

EDA in the Data Science Pipeline

Position in Pipeline: EDA is an essential pre-processing step in the business intelligence (BI) or data science pipeline, occurring after data acquisition but before model training.
Purpose: The goal of EDA is to understand the data by identifying:
- Missing values (nulls)
- Outliers
- Feature distributions
- Relationships or correlations between variables

Data Acquisition and Initial Inspection

Data Sources: Data may arrive from various streams (e.g., Twitter, sensors) and is typically stored in structured formats such as databases or spreadsheets.
Loading Data: In Python, data is often loaded into a Pandas DataFrame using commands like pd.read_csv('filename.csv').
Initial Review:
- df.info(): Displays data types and counts of non-null entries by column, quickly highlighting missing values.
- df.describe(): Provides summary statistics for each column, including count, mean, standard deviation, min/max, and quartiles.

Handling Missing Data and Outliers

Imputation:
- Missing values must often be filled (imputed), as most machine learning algorithms cannot handle nulls.

Visualization Techniques Feature Correlation and Dimensionality Data Transformation Prior to Modeling Summary of EDA Workflow

MLA 007 Jupyter Notebooks

Saison 1 · Épisode 37

mardi 16 octobre 2018 • Durée 16:52

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 learn & code

Overview of Jupyter Notebooks

Historical Context and Scope
- Jupyter Notebooks began as IPython Notebooks focused solely on Python.
- The project was renamed Jupyter to support additional languages - namely Julia ("JU"), Python ("PY"), and R ("R") - broadening its applicability for data science and machine learning across multiple languages.
Interactive, Narrative-Driven Coding
- Jupyter Notebooks allow for the mixing of executable code, markdown documentation, and rich media outputs within a browser-based interface.
- The coding environment is structured as a sequence of cells where each cell can independently run code and display its output directly underneath.
- Unlike traditional Python scripts, which output results linearly and impermanently, Jupyter Notebooks preserve the stepwise development process and its outputs for later review or publication.

Typical Workflow Example

Stepwise Data Science Pipeline Construction
- Import necessary libraries: Each new notebook usually starts with a cell for imports (e.g., matplotlib, scikit-learn, keras, pandas).
- Data ingestion phase: Read data into a pandas DataFrame via read_csv for CSVs or read_sql for databases.

Publishing and Documentation Features Practical Considerations and Limitations Summary

MLA 006 Salaries for Data Science & Machine Learning

Saison 1 · Épisode 36

jeudi 19 juillet 2018 • Durée 19:35

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 walking desk stay healthy & sharp while you learn & code

Global and Regional Salary Differences

Median Global Salary: $90,000 USD, up from $85,000 the previous year.
Regional Breakdown:
- United States: $112,000 median; California leads at $134,000.
- Western Europe: $57,000—about half the US median.
- Australia & New Zealand: Second after the US.
- Eastern Europe: Below $30,000.
- Asia: Wide interquartile salary range, indicating high variability.

Demographic and Personal Factors

Gender: Women's median salaries are $8,000 lower than men's. Women make up 20% of respondents but are increasing in number.
Age & Experience: Higher age/experience correlates with higher salaries, but the proportion of older professionals declines.
Education: Nearly all respondents have at least a master's; PhD holders earn only about $5,000 more than those with a master's.
Negotiation Skills: Self-reported strong salary negotiation skills are linked to $45,000 higher median salaries (from $70,000 for lowest to $115,000 for highest bargaining skill).

Industry, Company, and Role Tools, Languages, and Technologies Key Salary Differentiators (per Machine Learning Analysis) Summary Takeaways

MLA 005 Shapes and Sizes: Tensors and NDArrays

Saison 1 · Épisode 35

samedi 9 juin 2018 • Durée 27:18

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 ocdevel.com/mlg/mla-5
Try a walking desk stay healthy & sharp while you learn & code

Definitions

Tensor: A general term for an array of any number of dimensions.
- 0D Tensor (Scalar): A single number (e.g., 5).
- 1D Tensor (Vector): A simple list of numbers.
- 2D Tensor (Matrix): A grid of numbers (rows and columns).
- 3D+ Tensors: Higher-dimensional arrays, such as images or batches of images.
NDArray (NumPy): Stands for "N-dimensional array," the foundational array type in NumPy, synonymous with "tensor."

Tensor Properties Dimensions

Number of nested levels in the array (e.g., a matrix has two dimensions: rows and columns).
Access in NumPy: Via .ndim property (e.g., array.ndim).

Size

Total number of elements in the tensor.
Examples:
- Scalar: size = 1
- Vector: size equals number of elements (e.g., 5 for [1, 2, 3, 4, 5])
- Matrix: size = rows × columns (e.g., 10×10 = 100)
Access in NumPy: Via .size property.

Shape

Tuple listing the number of elements per dimension.

Common Scenarios & Examples Data Structures in Practice Conceptual Clarifications Reshaping and Manipulation in NumPy Reshaping Tensors Axis Reordering Practical Example Core NumPy Functions for Manipulation Summary Table of Operations Operation NumPy Function Purpose Add dimension np.expand_dims Convert (256,256) to (256,256,1) Remove dimension np.squeeze Convert (N,1) to (N,) General reshape np.reshape Any change matching total size Flatten np.ravel Convert (a,b) to (a*b,) Swap axes np.swapaxes Exchange positions of two axes Permute axes np.transpose Reorder any sequence of axes Closing Notes

Podcasts Similaires Basées sur le Contenu

Découvrez des podcasts liées à Machine Learning Guide. Explorez des podcasts avec des thèmes, sujets, et formats similaires. Ces similarités sont calculées grâce à des données tangibles, pas d'extrapolations !