Master AI and Machine Learning to excel in the AI-900 Exam
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|---|---|---|---|---|
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The curriculum provides specific coverage of machine learning methods like the Ward Method, K-sMeans, Logistic Regression, and Support Vector Machines.
Instruction includes essential data preprocessing steps such as handling missing data, aggregation, and visualization.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
A screenshot of the Azure AI Machine Learning Studio interface displaying a Jupyter notebook with Python code for a Keras neural network and a row of truck images.
Presentation slide titled Hierarchical Clustering (Ward Method) displaying a baseball statistics table, a dendrogram, and a K-Means scatter plot.
A presentation slide explaining Random Forest ensemble learning with a decision tree diagram, a majority vote table, and a list of key features.
A presentation slide displaying a table of logistic regression coefficients for baseball statistics, with orange text annotations questioning the results.
While hands-on segments exist for specific algorithms, the curriculum lacks broader project-based learning or comprehensive Azure service labs.
The curriculum aligns with the goal of AI-900 preparation by covering both algorithms and Azure infrastructure.
