1: Introduction to Scalable Machine Learning
1.1 Overview of machine learning and its applications
1.2 Challenges and benefits of scaling machine learning
1.3 Introduction to Apache Spark and its role in machine learning
1.4 Spark MLlib: Overview and capabilities

2: Setting Up the Environment
2.1 Installing and configuring Apache Spark for machine learning
2.2 Setting up a Java development environment for Spark MLlib
2.3 Overview of Spark’s machine learning libraries and tools
2.4 Running and testing Spark MLlib applications

3: Data Preparation and Feature Engineering
3.1 Loading and preprocessing data with Spark(Ref: Real-Time Analytics with Databricks and Spark Streaming)
3.2 Feature extraction and transformation techniques
3.3 Handling missing values and data imputation
3.4 Scaling and normalizing features for machine learning

4: Machine Learning Algorithms with Spark MLlib
4.1 Introduction to supervised learning algorithms: Classification and Regression
4.2 Implementing classification models (e.g., Logistic Regression, Decision Trees)
4.3 Building regression models (e.g., Linear Regression, Gradient-Boosted Trees)
4.4 Exploring unsupervised learning techniques (e.g., Clustering, Dimensionality Reduction)

5: Building and Training Machine Learning Models
5.1 Creating and tuning machine learning pipelines with Spark MLlib
5.2 Hyperparameter tuning and model selection
5.3 Evaluating model performance: Metrics and validation techniques
5.4 Using cross-validation and grid search for model optimization

6: Advanced Machine Learning Techniques
6.1 Implementing advanced algorithms (e.g., Random Forests, Naive Bayes)
6.2 Leveraging ensemble methods and model stacking
6.3 Advanced feature engineering and selection techniques
6.4 Working with complex data structures and large-scale datasets

7: Model Deployment and Integration
7.1 Deploying machine learning models in production environments
7.2 Integrating Spark MLlib models with external applications
7.3 Real-time predictions and batch processing with Spark
7.4 Managing and monitoring deployed models

8: Performance Optimization and Scalability
8.1 Optimizing machine learning workflows for performance
8.2 Techniques for scaling machine learning applications
8.3 Handling large datasets and distributed computing challenges
8.4 Best practices for optimizing Spark jobs and resource management

9: Case Studies and Hands-On Projects
9.1 Real-world case studies of scalable machine learning applications
9.2 Hands-on project: Building a complete machine learning pipeline with Spark and Java
9.3 Analyzing and optimizing a sample machine learning project
9.4 Discussing real-world challenges and solutions

10: Future Trends and Further Learning
10.1 Emerging trends in scalable machine learning and big data technologies
10.2 Resources and tools for continued learning and development
10.3 Exploring advanced topics: Deep learning with Spark, integration with other frameworks