# *AI-900 Machine Learning – Training the model*
In this AI-900 module, we dive deep into the practical heart of Machine Learning: training and evaluating both Supervised and Unsupervised Machine Learning models. This video provides critical insights for anyone aiming to master the AI-900 concepts and understand real-world Machine Learning applications.
This AI-900 video is essential for understanding the core mechanics of how a Machine Learning model learns, makes predictions, and how data scientists measure its accuracy—all vital concepts for passing your AI-900 certification. Get ready to boost your Machine Learning knowledge!
—————————————————————————————————————————-
*Here’s What We’ll Be Covering for Your AI-900 Success in Machine Learning: *
The Supervised Learning Cycle: Master the four-step process used to train all regression and classification Machine Learning models, a key part of AI-900.
Regression Metrics: Understand the purpose of Mean Absolute Error (MAE), Mean Squared Error (MSE), RMSE, and R-squared – crucial for evaluating Machine Learning models.
Classification Metrics: Learn the differences between Accuracy, Precision, Recall, and the F1 Score —and why the Confusion Matrix is the starting point in Machine Learning evaluations.
Unsupervised Clustering: Explore how the K-Means algorithm works and how metrics like the Silhouette score are used to evaluate its success, as covered in Machine Learning practice.
Practical Concepts: See how data scientists iteratively refine Machine Learning models by adjusting features and algorithms, reinforcing your AI-900 understanding.
——————————————————————————————————————————
▶️ CHAPTERS:
00:00 Introduction and Welcome
00:16 How a model learns to predict
01:04 Training a regression model
01:43 Error or Residual
02:00 Regression evaluation matrix
02:07 Mean absolute error (MAE)
02:20 Mean Squared Error (MSE)
02:36 Root mean squared error (RMSE)
02:51 Coefficient of Determination (R2)
03:28 Refining the model
03:55 Binary classification
04:24 Logistic regression
05:09 Evaluating a Binary Classification Model
05:34 Accuracy
05:58 Recall
06:22 Precision
06:42 F1 Score
07:18 Area under the curve (AUC)
07:49 Multiclass classification
09:27 Evaluating a multiclass classification model
10:05 K-Means clustering
11:19 Evaluating a clustering model
——————————————————————————————————————————
* If you’re finding these tech tutorials useful, take a second to subscribe.*
——————————————————————————————————————————
Resources & Next Steps
Official Microsoft Learn Module: Get started with language in Azure
https://learn.microsoft.com/en-gb/training/modules/get-started-language-azure/
Full AI-900 Course Playlist:
https://www.youtube.com/playlist?list=PLN8NhqFJ6PSK72PYGv13FWNR67XJX0AI9
—————————————————————————————————————————————————”
#AI900 #MachineLearning
Source
