Learn data science & machine learning topics with simple, step-by-step demos and user-friendly Excel models (NO code!)
InstructorMaven Analytics • 1,500,000 LearnersThe curriculum explores mathematical foundations including Entropy, Information Gain, and Least Squared Error alongside model diagnostics.
Learning is driven by specific scenarios, such as using KNN for classification and applying seasonality techniques to time-series forecasting.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
A conceptual flowchart mapping the machine learning landscape by categorizing algorithms into supervised, unsupervised, and advanced topics.
A decision tree visualization built in a spreadsheet, showing data splits based on Follow and Sessions_10 with calculated entropy and information gain values.
A spreadsheet interface displaying a K-Nearest Neighbors classification demo with data tables, scatter plots, and a predicted outcome panel.
Excel interface displaying a cone zone sales dataset alongside a scatter plot with a linear regression trendline and statistical metrics.
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Instructional signals indicate issues with low voice volume and poor audio clarity during key lectures.
The curriculum aligns with the goal of teaching machine learning via Excel, though success depends on existing spreadsheet proficiency.