Overview

BigML is a leading cloud-based, no-code machine learning platform designed for ease, scalability, and automation. Whether you're a data analyst, business intelligence professional, or product owner with little to no coding experience, this course will help you understand and apply machine learning with BigML's intuitive GUI, REST APIs, and automation tools like AutoML, WhizzML, Deepnets, and OptiML.We've crafted this course around 12 key modules that reflect both the technical depth and interview-oriented focus you need to demonstrate confidence in BigML-based solutions. With 350+ concept-based and scenario-based Q & A, you will develop the readiness to handle practical, business-driven machine learning problems using BigML.Course Syllabus (Structured with Modules)1. Introduction to BigMLUnderstand the BigML ecosystem, no-code ML capabilities, and key use cases like customer segmentation, demand forecasting, and fraud detection.Compare BigML with other platforms like AWS SageMaker and Azure ML.2. BigML ArchitectureLearn the core elements: data sources, datasets, models, evaluations, predictions.Master the BigML workflow-from data ingestion to real-time predictions and WhizzML automation.Explore deployment models (cloud vs. on-premise).3. Data PreparationUpload and transform data using BigML.Perform feature engineering, handle missing values, and apply built-in preprocessing steps.Use interactive visualizations to explore and understand data.4. Model CreationBuild supervised models (classification and regression).Apply unsupervised models like clustering and anomaly detection.Learn time-series forecasting for trend analysis and ARIMA predictions.5. Feature Engineering and SelectionEvaluate feature importance.Use Smart Feature Selection for automatic optimization of input variables.6. Evaluations and MetricsLearn evaluation techniques and performance metrics (accuracy, precision, recall, F1, RMSE, R-squared).Visualize model quality using ROC curves, confusion matrices, and error distributions.Apply k-Fold Cross-validation to validate models effectively.7. Model DeploymentImplement batch and real-time predictions.Integrate models into business applications using REST APIs.Automate workflows with task chaining and pipeline execution.8. Automating WorkflowsAutomate repetitive ML tasks with WhizzML scripting.Use AutoML to automate model training and optimization.Connect multiple steps into automated workflows using task chaining.9. BigML Special FeaturesDive into Deepnets for deep learning.Use OptiML for automated hyperparameter tuning.Leverage Fusions to ensemble models for improved accuracy.10. BigML and Industry ApplicationsLearn real-life applications across retail, healthcare, and finance.Explore case studies where BigML was successfully used in production environments.11. BigML Security and ComplianceUnderstand GDPR-compliant practices in BigML.Apply role-based access controls, encrypted data handling, and token-based model security.12. Performance OptimizationLearn techniques to optimize model performance and reduce prediction latency.

Skills

Reviews