Course Outline:
Introduction to DataRobot:
Overview of DataRobot and its role in machine learning and data science
Understanding the architecture and components of DataRobot
Navigating the DataRobot interface (Web App, Notebooks, Model Deployment)
Key concepts: Projects, Datasets, Models, Pipelines, and Deployments
Getting Started with DataRobot:
Creating and managing DataRobot projects
Importing datasets from various sources (CSV, Excel, SQL databases, cloud storage)
Setting up and configuring data for analysis
Introduction to DataRobot’s AutoML capabilities and workflow
Data Preparation:
Data import and initial inspection
Handling missing values and outliers
Data transformation techniques (normalization, encoding, feature scaling)
Feature engineering and selection (creating new features, selecting important variables)
Building and Training Models:
Understanding DataRobot’s automated modeling process
Exploring different machine learning algorithms (regression, classification, clustering)
Evaluating model performance (accuracy, precision, recall, ROC curve)
Comparing multiple models and selecting the best performing one
Advanced Model Techniques:
Using ensemble models and stacking
Hyperparameter tuning and optimization
Advanced feature engineering techniques
Customizing model settings and configurations
Model Deployment and Management:
Deploying models for real-time and batch scoring
Managing and monitoring deployed models
Integrating models with production systems and applications
Handling model versioning and retraining
Interpreting and Explaining Models:
Understanding model interpretability and explainability
Using DataRobot’s tools for model insights (SHAP values, feature importance)
Generating and interpreting model explanations
Communicating results and insights effectively to stakeholders
Automation and Workflows:
Automating repetitive tasks and workflows
Using DataRobot’s API for advanced integrations
Creating and managing custom scripts and automation tasks
Scheduling and running automated model training and evaluation
Best Practices and Case Studies:
Best practices for using DataRobot effectively
Real-world case studies and practical examples
Solving business problems with DataRobot
Practical exercises and hands-on projects
Collaboration and Team Management:
Working with teams and managing user access
Collaborating on projects and sharing insights
Using DataRobot’s collaboration features
Managing permissions and roles within the platform
Skills Gained:
Proficiency in using DataRobot for automated machine learning
Ability to prepare and preprocess data for analysis
Skills in building, evaluating, and deploying machine learning models
Knowledge of advanced modeling techniques and hyperparameter tuning
Competence in interpreting and explaining model results
Experience with automation and integration using DataRobot
Target Audience:
Data scientists
Business analysts
Machine learning engineers
IT professionals
Students and professionals interested in data science and machine learning
Course Logistics:
Location: Training centers, educational institutions, or online platforms (including options in Mianwali or similar regions)
Duration: Typically ranges from a few days to several weeks, depending on the course depth and schedule
Format: Classroom-based, online, or hybrid