ML for Business professionals using No-Code AI tools

Plan a controlled camera quality comparison by capturing Pineapple 15 and Black MI ten images across ten frames, collecting five-point ratings, and computing rating differences to guide business decisions.

Explore objective decision methods by comparing profits across products using sum, mean, median, and standard deviation, while avoiding biases and considering business context.

Examine survivorship bias, confirmation bias, omitted variable bias, and other biases in decision making, with real-world examples and practical steps to mitigate via preexisting views and diverse reviews.

Explore outlier bias and why the median better represents data with extreme values than the mean. Learn how population and sample data relate and how proper sampling reduces selection bias.

Select representative samples to approximate population parameters, using online calculators to balance margin of error and confidence level. Address biases to base business decisions on data.

Apply no-code AI tools to a Madani Airlines case, analyze flights to and from New Delhi, and decide a pilot destination within the 60 days of 2022 data sharing.

Madani Airlines uses AAI data on seats, occupancy, and 2022 economy fares for the high-demand New Delhi–Singapore sector to shape a center price figure and a low-cost pricing strategy.

Pineapple compares Pineapple 15 and BlackMI ten image quality using ten frames and a five-point rating to analyze rating differences for business decisions.

Explore data problems graphically with Tableau, a no-code visualization tool, in hands-on exercises for Casa Electro. Learn to select charts and track profitability, regions, and sales across two years.

Visualizing data through charts with no-code tools accelerates decision making by simplifying complex data and revealing trends, profits, and regional patterns using bar, area, map, and donut visuals.

Open the W public interface, import an Excel dataset, and explore fields by dimensions and measures. Create charts on sheets, use Show Me for visuals, and apply analytics like forecasts.

Master basic data preparation to turn raw data into analytics-ready figures using Tableau. Learn header promotion, pivoting, missing value handling, and splitting columns like addresses.

Build and customize a Tableau dashboard that combines KPIs, maps, and filters to monitor sales and guide marketing and sales strategy decisions.

Understand why predictions matter in daily life and business, illustrated by travel time, movie trailer, fruit freshness, and demand forecasts for textiles and food delivery, and learn common prediction methods.

Explore how to make predictions using regression and classification concepts, persistence and moving averages, and rule-based models, with a hands-on look at subscribers data, features, and targets.

Predict next-day subscribers using a seven-day average with 20% weekend and 10% promotion adjustments, then weigh rule-based methods against machine learning for regression and classification tasks.

Explore evaluating predictions for regression with root mean squared error (RMSE), an interpretable metric. Learn to compute squared errors and RMSE to compare rule-based and machine learning predictions.

Define accuracy as the percentage of correct predictions in classification with discrete targets; illustrate with purchase and elevator examples, explain class imbalance, and introduce data division and confusion-matrix metrics.

Divide data into train and test to evaluate predictions against future scenarios, detect overfitting, and simulate real life performance; use random shuffling for non time data, with 70:30 or ratios.

Repeat cross-validation across multiple parts, testing each record once and averaging performance across three-fold, five-fold, or ten-fold approaches to reduce sample bias in small to medium datasets.

Define benchmark performance by comparing predictions to human performance, earlier systems, or practical usefulness, and align metrics with stakeholder goals.

Explore how machine learning learns from data to predict outcomes faster and without human bias, unlike rule-based systems, and how no-code tools lower coding barriers.

Discover how machine learning powers daily life and business across finance, marketing, operations, HR, healthcare, education, agriculture, and e-commerce, including fraud detection, predictive maintenance, and AI tools.

Explore the three main types of machine learning, with a focus on supervised learning, and distinguish classification and regression by target variables and practical examples.

Explore unsupervised machine learning, its lack of a target variable, and use cases like clustering, association rules, and dimensionality reduction to uncover hidden data patterns.

Compare no-code and code-based tools for machine learning, including Python and Julia options, and popular no-code platforms like Monkey Learn, Loop, Teachable Machine, and Orange for business applications.

Explore Orange's no-code interface for building machine learning models with drag-and-drop widgets, loading datasets like iris, visualizing data, preprocessing, modeling, evaluating, and adding image, text, and time series tools.

Deploy a trained model into production using Orange saved models to make real-world predictions with no-code tools, and forecast next month’s sales using a saved decision tree.

Explore regression as a supervised method to predict continuous targets, focusing on linear regression and decision trees. Learn the simple linear equation, coefficients, and the tree's handling of non-linear relationships.

Explore popular classification models including k-nearest neighbors, logistic regression, random forest, and naive bayes, noting when they suit binary or multi-class tasks, speed, and explainability.

Use a no-code machine learning approach to detect fraudulent transactions, achieving recall 80% and precision 90% with random forests selected after cross-validation.

Explore unsupervised machine learning through clustering, comparing hierarchical clustering and k-means, using dendrograms and silhouette scores to determine clusters, applying a business case study in Orange for segmentation.

Evaluate ROI, data quality, data quantity, and consent to decide when machine learning provides value. Avoid ML with poor data, small datasets, confidential information, or high training and deployment costs.

Introduce deep learning and its subfields NLP and computer vision through hands-on Orange cases, including NLP sentiment analysis of employees and a computer vision workflow for a pizza chain.

Learn natural language processing fundamentals and build a sentiment analysis workflow in Orange using the Brinn employee feedback dataset, applying Vader to score sentiments and identify churn drivers.

Explore computer vision applications from detection to facial recognition and medical imaging, and build an image classification model in Orange with a pizza toppings dataset, addressing data imbalance.

Learn how generative AI enables non-technical professionals to create websites, blogs, ads, images, and videos with no-code tools, transforming marketing, education, and design.

Explore hands-on use of generative AI tools to draft client communications, design visuals, summarize articles, and refine prompts using no-code AI workflows.

Explore ethical concerns in AI, including bias, privacy, employment impact, transparency, and protection against misuse, and discuss proactive safeguards and multidisciplinary collaboration to maximize benefits.

Finish the course by embracing no-code tools to boost efficiency, become less dependent on others, and unlock unique solutions across different datasets, with paths to generative AI without coding.