Udemy - Practical Ai And Machine Learning With Model Builder Automl torrent

Practical Ai And Machine Learning With Model Builder Automl

https://DevCourseWeb.com

Published 11/2023
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.16 GB | Duration: 2h 33m

Master machine learning by doing it in practice, using an automated machine learning GUI that requires little/no coding.

What you'll learn
See an end-to-end, supervised machine learning process to tackle a regression problem, using Microsoft's Model Builder and ML .Net.
Understand the tasks and activities that take place behind the scenes. From data preparation all the way to model training and evaluation.
Understand data transformation, feature scaling, iterating through algorithms, evaluation metrics, overfitting, cross-validation and regularization.
Understanding the impact of evaluation metrics on model performance, and how to check for overfitting.
Understand the lasting fundamentals of machine learning that are independent of the tools or platforms one can use.
Gain a deep understanding of machine learning concepts by seeing them in action, during a practical machine learning nstration.
Understand the importance of Exploratory Data Analysis (EDA) and the impact that the statistical distribution of the data has on model performance.
Learn how to set up Visual Studio and to configure it to enable Model Builder, the graphical tool that will be used to nstrate the machine learning process.
Learn how to use Model Builder to train models without having to code.

Requirements
A basic understanding of supervised machine learning is required. The student would at the very least need to understand what regression is, what features are, and what it means for a model to be trained to fit a function to input features in order to predict labels.
The student needs to have a Windows machine with a few GB of free disk space to install Visual Studio, in order to replicate the machine learning process I will nstrate. However, this is not essential.
A Windows machine is ideal, but a student with a Mac will still be able to follow along. The course content is visual enough to nstrate the concepts, without the student having to physically do the machine learning exercise.