No-Code and No-Math Machine Learning

Discover Google Vertex AI, a no-code platform to train, deploy, and scale machine learning models with $300 in free credits, pricing details, and dashboards for datasets and pipelines.

Create a Google Vertex AI project, enable the API, and load a tabular dataset (medical costs) to generate statistics and visualizations for regression modeling.

Train a regression model to predict medical costs using the no-code platform. Select data, set the target, explore correlation, and start training with early stopping to manage costs.

Learn to evaluate regression results using mean absolute error, mean squared error, and root mean squared error, where lower values indicate better predictions.

Analyze model results and deploy endpoints to test predictions with Google Vertex AI, using metrics like mean absolute error, root mean squared error, and R squared, plus feature importance.

Explore a no-code workflow to predict wine quality using a Kaggle red wine dataset with Google Vertex, covering data loading, regression training, evaluation, and endpoint deployment.

Explore data robots, a no-code ai cloud platform for auto machine learning, data preparation, and deployable ml modules with industry case studies.

Load the heart attack dataset from Kaggle with data robots, perform exploratory data analysis, and assess data quality and outliers for binary classification.

Explore metrics for evaluating classification algorithms: confusion matrix, accuracy, precision, recall, ROC curves, thresholding, and AUC, using malignant vs benign tumor examples.

Train the module by selecting the target, starting training, and evaluating multiple classification algorithms, with elastic net classifier performing best and shown metrics like AUC, ROC, and confusion matrix.

Deploy the model for binary classification, set the 0.5 threshold, and generate predictions with the predictor heart from CSV data, then review the deployment status.

Use wine data to predict quality with regression. The workflow automatically tests regression models, identifies random forest as best, and deploys a wine predictor with dashboards.

Explore no-code machine learning with a fast tool for classification, regression, and time series, featuring web deployment, API endpoints, a free plan, and easy dataset testing.

Learn to load a banking fraud dataset, use no-code auto ml to build a fraud predictor, and deploy the module to generate predictions via api and an interactive web interface.

Use a no-code regression workflow on a property price dataset to set the target price and features like bedrooms and square feet. Build a gradient boosting model and evaluate predictions.

Explore BigML, a 100% online no-code machine learning platform, to build supervised and unsupervised models with a free account via a graphical interface. Export models to JSON and API-ready formats.

Explore no-code machine learning with big m l by building a dataset from a csv, inspecting attributes like transaction amount and fraud label, and creating a dataset for modeling.

Train and evaluate fraud-detection models by loading data, selecting fields, and splitting 80/20 for training and testing; compare a decision tree and neural network, and report accuracy, precision, and recall.

Explore predicting fraud with no-code machine learning using a decision tree and a deep neural network. Predict by question with the type attribute or provide full values for a prediction.

Use a no-code workflow to load the wine quality dataset, perform an 80/20 train-test split, compare linear regression and neural networks, evaluate with mae, mse, and r^2, and generate predictions.

Explore Microsoft Azure, a complete cloud platform with machine learning among many services, featuring free 12-month access and a $200 starter credit to test and build data projects.

learn to set up an Azure machine learning workspace, load an insurance dataset for regression, explore data types, visualize statistics, and prepare features for a no code workflow.

Learn to build a no-code machine learning pipeline with the designer, loading data, converting strings to categoricals, splitting data 80/20, and training a boosted decision tree regression model with evaluation.

Evaluate model performance with coefficients of determination and key regression metrics, then deploy a real-time inference endpoint and test it with JSON inputs.

Learn to use Azure automated machine learning to preprocess data, select the best regression model for insurance charges (including motor, age, BMI, and children), and deploy a real-time prediction.

Explore a no-code machine learning workflow for binary classification on the adult census income dataset. Preprocess string features to categorical, split data, train a classifier, and deploy an inference endpoint.

Discover no-code machine learning with Orange, an open source visual programming tool that uses drag-and-drop components for data visualization and simple algorithms like random forest and neural networks.

Explore no-code machine learning with orange two to classify animals from features, train a tree algorithm, evaluate with confusion matrices, and save the model for future predictions.

Learn to build a time series forecast in Orange using Yahoo Finance data, loading Amazon stock prices, converting to time series, and evaluating ARIMA-style models with 60-day forecasts.

Use no-code tools to load wine quality data, visualize features with distributions, and compare regression models such as linear regression, SVM, and neural networks.

Recap no-code, no-math machine learning by revisiting basics, applying Vertex AI to regression and classification, and building ML flows by linking components in Azure and Orange tool.

Explore no-code and no-math machine learning through practical, certificate-bearing courses on ai, data science, and essential topics like deep learning and natural language processing.