No-Code Machine Learning: Practical Guide to Modern ML Tools

Explore no-code machine learning with Google Vertex AI to build, train, and deploy models for higher education. Predict admission chances using GRE, TOEFL, GPA, SOP, LOR, and research experience.

Train a regression model using Google Vertex AI AutoML on the university admission dataset, select the target column chance of admission, and apply 80/10/10 data splits with auto transformation.

Assess model performance using MAE, MAPE, MSE, RMSE, and r-squared, then deploy a trained university admissions model to a Google Vertex AI endpoint for live inference with explainable feature importance.

Discover how to build, train, and deploy a no-code machine learning regression model for student admissions using Google Vertex AI, with tabular data features and AutoML-driven evaluation.

Explore how no-code machine learning enables finance applications—from robo advisors to fraud detection and high-frequency trading—and build a loan risk predictor with DataRobot.

Build a no-code classifier to predict bank loan risk using borrower features such as income, employment length, debt-to-income, loan amount, term, and interest rate, outputting good or bad loans.

Sign up for Data Robot and upload your training dataset to automatically build and deploy a loan risk classifier, exploring features like employment length, income, and loan terms.

Train multiple models in parallel using auc as the optimization metric, monitor training progress, and review feature associations and feature importance to prepare model deployment.

Explore how a confusion matrix visualizes classifier performance and apply precision, recall, roc, and auc to evaluate no-code ml models, including handling imbalanced data and key classification kpis.

Assess model performance using confusion matrix, ROC curve, and AUC across 13 no-code Data Robot models on the leaderboard, with metrics like accuracy, precision, recall, and F1.

Explore hyper parameter tuning with data robot, focusing on learning rate adjustments for XGBoost and gradient boosted models, comparing tuned versus default performance on validation metrics.

Deploy the best model to an endpoint and build a no-code predictor app to make real-time loan predictions from new data.

No-code machine learning tools guide finance-focused classifiers, from fraud detection to loan underwriting, using DataRobot for data preparation, target selection, model evaluation, and deployment of endpoints for inference.

Explore a no-code machine learning project that predicts used car prices using features like make, model, origin, engine size, horsepower, mpg, weight, drag-and-drop blocks in Azure ML designer.

Sign up for Azure ML, launch Studio, and create a no-code machine learning workspace to build and deploy ML pipelines without coding.

Create and upload a dataset in Microsoft Azure Machine Learning Studio using the drag-and-drop designer to define schema for tabular data and prepare inputs for model training.

Build a no-code machine learning pipeline in Microsoft Azure Machine Learning Studio Designer using templates and drag-and-drop blocks. Train models, score results, and evaluate performance with ready-made pipelines.

Execute and test the Azure ML pipeline end-to-end, including data prep, training, scoring, and evaluation. Configure compute targets and review metrics like R squared around 0.807, MAE, and RMSE.

Deploy the model with a real time inference pipeline in Azure ML designer, create and deploy a price-prediction endpoint, and test it to obtain MSRP predictions and key metrics.

Explore building a pipeline in Azure Machine Learning Designer to test boosted decision trees regression and decision forest regression in parallel. Compare models by coefficient of determination to guide deployment.

Create, train, and deploy no-code machine learning models with Azure ML designer using a drag-and-drop pipeline for price prediction. Compare regression models and deploy a real-time endpoint.

Celebrate completing the course and thank learners, inviting them to explore more courses on machine learning, data science, and artificial intelligence.

Introduce XGBoost, the extreme gradient boosting algorithm that ensembles weak models for robust regression and classification, with hyperparameter tuning in SageMaker.

Discover boosting, a sequential ensemble method that learns from residual errors by training weak models, reweighting data, and combining predictions to achieve stronger outcomes.

Explore no-code machine learning with ensemble learning and boosting, featuring decision trees and Xgboost to reduce variance and predict user preferences.

Explore gradient boosting algorithms and XGBoost, build an ensemble by predicting residuals with a learning rate, improving generalization and reducing overfitting in regression.

Delve into extreme gradient boosting with xgboost, exploring its scalable tree learning, sparsity handling, and weighted quantile sketch, and preview deploying it with AWS Sagemaker.

Explore AWS SageMaker XGBoost, a robust ensemble algorithm for regression and classification, including fraud detection applications, on tabular data, with csv or libsvm inputs and cpu-based training on m4 instances.