ML.NET

MLOps: MLflow Hands On, Session 2, part 2

About MLflow Code example Package model & environment Configurations Model flavours App example Better way: mlflow.pyfunc Model wrapper Packaging

From playlist ML Ops (hands-on)

(ML 4.1) Maximum Likelihood Estimation (MLE) (part 1)

Definition of maximum likelihood estimates (MLEs), and a discussion of pros/cons. A playlist of these Machine Learning videos is available here: http://www.youtube.com/my_playlists?p=D0F06AA0D2E8FFBA

From playlist Machine Learning

(ML 10.2) Posterior for linear regression (part 1)

How to compute the posterior distribution for the weight vector w under a Bayesian model for linear regression.

From playlist Machine Learning

(ML 1.4) Variations on supervised and unsupervised

A broad overview. A playlist of these Machine Learning videos is available here: http://www.youtube.com/my_playlists?p=D0F06AA0D2E8FFBA

From playlist Machine Learning

(ML 15.5) Logistic regression (binary) - computing the gradient

From playlist Machine Learning

Introduction to C# Language Integrated Query (LINQ)

Language-Integrated Query (LINQ) is a feature of the C# programming language that lets you work with data with SQL-like syntax. This presentation starts with the basic syntax of a LINQ query. You'll also learn about core features like filters, joins, and grouping. Additionally, you'll see

From playlist Getting Started with Data Engineering

(ML 6.1) Maximum a posteriori (MAP) estimation

Definition of maximum a posteriori (MAP) estimates, and a discussion of pros/cons. A playlist of these Machine Learning videos is available here: http://www.youtube.com/view_play_list?p=D0F06AA0D2E8FFBA

From playlist Machine Learning

(ML 10.7) Predictive distribution for linear regression (part 4)

How to compute the (posterior) predictive distribution for a new point, under a Bayesian model for linear regression.

From playlist Machine Learning

(ML 10.6) Predictive distribution for linear regression (part 3)

How to compute the (posterior) predictive distribution for a new point, under a Bayesian model for linear regression.

From playlist Machine Learning

(ML 10.4) Predictive distribution for linear regression (part 1)

How to compute the (posterior) predictive distribution for a new point, under a Bayesian model for linear regression.

From playlist Machine Learning

Machine Learning in Environmental Science and Prediction: An Overview | AISC

For slides and more information on the paper, visit https://ai.science/e/machine-learning-in-environmental-science-and-prediction-an-overview--sBSFNhGyawkmyoLeFBks Speaker: Andre Erler; Host: Peetak Mitra; Discussion Facilitator: Amir Feizpour Motivation: This presentation is the debut

From playlist ML in Environmental Science

🔥Microsoft Azure Tutorial For Beginners 2022 | Microsoft Azure Certification | Azure | Simplilearn

🔥Explore our FREE Courses: https://www.simplilearn.com/skillup-free-online-courses?utm_campaign=AzureFC24Aug2022&utm_medium=Description&utm_source=youtube This YouTube live video on Microsoft Azure is curated in collaboration with real-time industry experts. This Azure Full Course will

From playlist Simplilearn Live