
This module introduces how data science and AI are used in industrial settings to improve efficiency, quality, reliability, cost reduction, and enhanced decision-making. Learners gain a practical understanding of real-world applications across different levels of AI ML.
Explore data visualization and dashboarding for AI and ML projects, using Power BI to connect data sources, monitor KPIs, and communicate insights with interactive dashboards.
Check the official Microsoft web page to download the free Power BI desktop software for Windows
Build a regression model in Orange with widgets to import data, define year as input and profit as target, then train and predict future profits.
Explore how MSE, RMSE, MAE, MAPE, and R2 score quantify model performance, and how unit-based MAP errors in profit forecasts guide the choice of better models.
Save the trained linear regression model with the save model widget, storing it in pqls format; to save the whole workflow, export the orange workflow in ows format.
Import the dataset, set L1, W1, H1 as features and parts as the target, then train a KNN classifier and evaluate with confusion matrix and accuracy.
Install the image analytics add-on in the Orange tool to enable image inputs for a machine vision case study, configure add-ons, and begin the next case study.
"This course contains the use of artificial intelligence."
The course “Industrial Foundation of AI-ML – No Code” is designed to provide a comprehensive and practical understanding of the Artificial Intelligence (AI) and Machine Learning (ML) ecosystem with a strong emphasis on real-world industrial application. Instead of limiting learning to theoretical concepts, the course follows an industry-aligned methodology that mirrors how AI-ML solutions are actually designed, developed, and implemented in business environments.
A core strength of the course is its hands-on, no-code approach, which enables learners to build end-to-end AI-ML solutions using free and industry-relevant tools such as Power BI and Orange Data Mining. Power BI is used extensively for data visualization, dashboarding, and business intelligence, helping participants transform raw data into actionable insights. Learners explore how Power BI is applied across industries—including manufacturing, finance, healthcare, retail, logistics, and services—for performance monitoring, predictive analysis, and data-driven decision-making.
The course also leverages Orange Data Mining, a powerful no-code visual programming tool for machine learning. Participants use Orange to perform data preprocessing, feature selection, model building, evaluation, and interpretation without writing code. Through industry-inspired use cases, learners understand how machine learning models are applied for tasks such as quality prediction, demand forecasting, anomaly detection, customer segmentation, and risk analysis across multiple sectors.
The curriculum is structured around industrial use cases, helping learners connect AI-ML concepts directly to practical challenges across domains such as manufacturing, operations, quality, supply chain, finance, and services. Participants learn how to frame business problems, select appropriate AI-ML techniques, interpret results, and communicate insights effectively to stakeholders.
Designed for both programming and non-programming professionals, the course lowers entry barriers while maintaining industrial depth. By emphasizing practical implementation over theory alone, it bridges the gap between academic understanding and industry expectations. Participants develop the mindset of AI practitioners and solution designers, ensuring immediate applicability, adaptability, and measurable impact across diverse industrial and business domains.