DSCI-272: Predicting with Cloudera Machine Learning

Course Modules

• Introduction to Cloudera Machine Learning (CML)

  • Overview of Cloudera Machine Learning and its role in modern big data analytics
  • Key features and components of the Cloudera Data Platform (CDP)
  • The integration of CML with Hadoop, Spark, and other Cloudera tools for scalable machine learning workflows

• Data Preparation and Exploration for Predictive Modeling

  • Best practices for preparing and exploring data for machine learning
  • Understanding data types, cleaning, and transforming data for modeling
  • Utilizing Cloudera Data Engineering tools for data ingestion and preprocessing

• Building Predictive Models with CML

  • Introduction to popular machine learning algorithms for predictive analytics (e.g., linear regression, decision trees, random forests, logistic regression)
  • Using CML to build and train machine learning models on large datasets
  • Model validation and evaluation techniques, including cross-validation and performance metrics (e.g., accuracy, precision, recall, ROC curves)

• Advanced Machine Learning Techniques

  • Implementing advanced techniques such as ensemble methods, gradient boosting, and neural networks
  • Introduction to deep learning and the use of TensorFlow or PyTorch within the Cloudera environment
  • Hyperparameter tuning and optimization for improving model performance

• Model Deployment and Monitoring with CML

  • Deploying machine learning models in Cloudera Machine Learning
  • Managing model versioning and deployment pipelines
  • Monitoring model performance in production environments and implementing automated re-training strategies

• Predictive Analytics for Business Applications

  • Identifying key business problems that can benefit from predictive modeling
  • Applications of predictive analytics in areas such as marketing, finance, healthcare, and supply chain management
  • Case studies demonstrating the power of predictive analytics in real-world business scenarios

• Machine Learning Pipelines and Automation in CML

  • Building end-to-end machine learning workflows and pipelines
  • Automating data flow and model deployment processes in Cloudera Machine Learning
  • Utilizing CML’s autoML capabilities to streamline model training and hyperparameter tuning

• Ethical Considerations and Model Interpretability

  • Understanding the ethical implications of machine learning predictions
  • Ensuring fairness and transparency in predictive models
  • Using tools to interpret and explain machine learning model decisions (e.g., SHAP values, LIME)

• Best Practices and Future Trends in Predictive Analytics

  • Following best practices for building scalable, efficient, and accurate predictive models
  • Exploring emerging trends in machine learning and predictive analytics
  • Leveraging Cloudera’s advanced AI and machine learning solutions for future-proofing your predictive analytics projects