BigML Interview Mastery: 350+ Important Questions & Answers
Master BigML Interviews with 6 Comprehensive Practice Tests Covering Real-World Scenarios and Core Concepts
BigML Interview Mastery: 350+ Important Questions & Answers udemy course
Master BigML Interviews with 6 Comprehensive Practice Tests Covering Real-World Scenarios and Core Concepts
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 BigML
Understand 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 Architecture
Learn 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 Preparation
Upload 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 Creation
Build 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 Selection
Evaluate feature importance.
Use Smart Feature Selection for automatic optimization of input variables.
6. Evaluations and Metrics
Learn 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 Deployment
Implement batch and real-time predictions.
Integrate models into business applications using REST APIs.
Automate workflows with task chaining and pipeline execution.
8. Automating Workflows
Automate 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 Features
Dive into Deepnets for deep learning.
Use OptiML for automated hyperparameter tuning.
Leverage Fusions to ensemble models for improved accuracy.
10. BigML and Industry Applications
Learn real-life applications across retail, healthcare, and finance.
Explore case studies where BigML was successfully used in production environments.
11. BigML Security and Compliance
Understand GDPR-compliant practices in BigML.
Apply role-based access controls, encrypted data handling, and token-based model security.
12. Performance Optimization
Learn techniques to optimize model performance and reduce prediction latency.
