No-Code and No-Math Machine Learning
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Unclaimed ProfileThe 'No-Code and No-Math Machine Learning' course, crafted by Jones Granatyr and the AI Expert Academy, represents a major shift in how artificial intelligence is taught. Traditionally, entering the machine learning field required a deep understanding of advanced mathematics, linear algebra, and complex programming languages like Python or R. This course democratizes access to AI by removing those technical barriers entirely. Designed for business professionals, entrepreneurs, product managers, and curious beginners, the curriculum focuses on hands-on application rather than theoretical abstractions. Students will explore an impressive suite of modern no-code platforms, including Google Vertex AI, DataRobot, Obviously AI, BigML, Microsoft Azure Machine Learning, and the visual programming tool Orange. Through practical demonstrations, learners discover how to upload datasets, preprocess data, select algorithms, and train predictive models visually. By mastering classification, regression, and time-series forecasting, participants gain the ability to extract actionable insights and deploy working machine learning models directly into production environments, empowering them to drive data-backed decision-making in their organizations without writing a single line of code.
About the creator
Jones Granatyr, AI Expert Academy
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Reviews
Jones Granatyr is the visionary founder of AI Expert Academy, a specialized educational platform dedicated to demystifying complex technologies for professionals across diverse sectors. With a deep focus on bridging the gap between cutting-edge research and practical implementation, Jones has spent years refining a curriculum that prioritizes actionable results over theoretical jargon. His pedagogical approach is uniquely tailored to practitioners who need to integrate automation, AI agents, and generative tools into their existing workflows without needing a background in computer science or advanced mathematics. By stripping away the intimidation factor often associated with high-tech development, Jones empowers accountants, creative directors,…Show more
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Program Overview
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Price may change · updated within 1–2 weeks
Course language
Learning format
Subcategory
Price
Price may change · updated within 1–2 weeks
Course language
What You'll Learn
- Design and train predictive machine learning models for real-world scenarios without writing a single line of code or performing complex mathematical calculations.
- Solve diverse business problems by applying classification, regression, and time-series forecasting techniques using intuitive visual interfaces.
- Navigate and implement end-to-end machine learning workflows across leading industry platforms including Google Vertex AI, DataRobot, Obviously AI, BigML, and Microsoft Azure.
- Deploy trained machine learning models into production environments and understand how to integrate predictions into external applications.
- Perform data preprocessing, handle missing values, and execute feature selection visually using the Orange data mining tool suite.
- Evaluate and compare model performance metrics like accuracy, precision, recall, and R-squared through graphical dashboards without manual computation.
Best For
- Business managers and entrepreneurs seeking to implement AI solutions without hiring data scientists.
- Product managers who need to prototype predictive features rapidly using no-code interfaces.
- Non-technical professionals who want to leverage their organizational data for actionable forecasting.
- Beginners interested in the mechanics of machine learning who prefer visual tools over traditional programming.
Not For
- Software engineers looking to master custom machine learning algorithms using Python or C++.
- Data scientists aiming to build foundational knowledge in linear algebra and multivariate calculus.
- Researchers requiring complete control over model architecture and hyperparameter tuning at the source code level.
- Learners who prefer deep theoretical academic frameworks over practical, platform-specific workflows.
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