Udemy – Introduction to Machine Learning Models 2023-2 – Download

Explanation

Deploying Machine Learning Models This course will show you how to take your machine learning models from a research environment to a full production environment. Embedding machine learning models, or simply, embedding production models, means making your models accessible to other systems in the organization or the network, so they can access the data and return their predictions. With machine learning models, you can start to take full advantage of the model you have built.

I’ll take you step-by-step through a video tutorial that will teach you everything you need to know to start creating a model in a research environment, and then convert your Jupyter notebook to code-soo. – production, package code and deploy API, and add continuous integration and continuous delivery. We will discuss the concept of production, why it is important, and how to increase production during deployment, through versioning, code storage and using docker. We will also discuss the tools and platforms available to deploy machine learning models.

What will you learn?

Build machine learning model APIs and deploy models to the cloud
Send and receive requests for deployed machine learning models
Design testable, version controllable and reproducible production code for deployment models
Create consistent and automated interfaces to use your models
Understand the best model of machine learning
Understand the different sources available to produce your models.
Identify and reduce barriers to entry into production models

Who is this course for?

Data scientists who want to deploy their first version of machine learning
Data scientists who want to learn the best way to deploy
Software developers who want to transition to machine learning

Introduction to Machine Learning Models

Publisher: Udemy
Instructor: Soledad Galli, Christopher Samiullah
Language: English
Level: Medium
Number of courses: 151
Duration: 10 hours and 26 minutes

The essence

Deploying Machine Learning Models

Requirements

Installing Python
Installing Git
Confidence in Python programming, including knowledge of Numpy, Pandas and Scikit-learn
Knowledge of using IDEs, such as Pycharm, Sublime, Spyder or similar
Knowledge of writing Python scripts and running them from the command line
Knowledge of basic git commands, including clone, fork, branch creation and branch checkout
Knowledge of basic git commands, including git status, git add, git commit, git pull, git push
Knowledge of basic CLI commands, including logging and using Git and Python in the CLI
Knowledge of linear regression and model evaluation parameters such as MSE and R2

Pictures

Deploying Machine Learning Models

Sample Clip

Installation Guide

Extract files and watch your favorite player

Subtitle: English

Quality: 1080p

Changes:

Version 2023/2 compared to 2021/5 increased the number of 11 lessons and the duration of 50 minutes. Also, the quality of the course has increased from 720p to 1080p.