15 Topics
10 Days
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Scientists
- Data Analysts
- Big Data Engineers
- Machine Learning Engineers
- Business Intelligence Professionals
- Software Developers
- Anyone needing machine learning skills for large datasets
Session Objectives
- Understand the fundamentals of machine learning and its application to big data.
- Master supervised and unsupervised learning algorithms for predictive analytics.
- Utilize industry-standard tools and frameworks for machine learning on big data.
- Develop and evaluate machine learning models for various use cases.
- Optimize machine learning models for performance and accuracy.
- Implement feature engineering techniques for big data.
- Deploy machine learning models in production environments.
- Troubleshoot and debug machine learning models and pipelines.
- Implement data security and access control in machine learning workflows.
- Integrate machine learning models with big data platforms.
- Understand how to monitor and maintain machine learning models.
- Explore advanced machine learning techniques for large datasets.
- Apply real world use cases for machine learning in Big Data.
About the Course
Unlock actionable insights from massive datasets with our Machine Learning for Big Data Training Course. Designed for data scientists, analysts, and engineers, this hands-on program equips you to build scalable, high-accuracy predictive models using enterprise-grade algorithms. Through expert instruction, you will master supervised and unsupervised learning, model evaluation, and deployment techniques—enabling you to extract value from complex data and drive strategic decision-making.
Curriculum & Topics
15 Topics | 10 Days
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Subtopic 1.1:
Fundamentals of machine learning and big data.
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Subtopic 1.2:
Overview of machine learning algorithms and applications.
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Subtopic 1.3:
Setting up a development environment for machine learning on big data.
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Subtopic 1.4:
Introduction to machine learning tools and frameworks.
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Subtopic 1.5:
Best practices for machine learning on big data.
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Subtopic 2.1:
Linear and logistic regression for predictive modeling.
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Subtopic 2.2:
Decision trees and random forests for classification and regression.
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Subtopic 2.3:
Support vector machines (SVMs) for complex data patterns.
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Subtopic 2.4:
Gradient boosting algorithms (e.g., XGBoost, LightGBM).
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Subtopic 2.5:
Model evaluation and hyperparameter tuning.
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Subtopic 3.1:
Clustering algorithms (e.g., K-means, DBSCAN).
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Subtopic 3.2:
Dimensionality reduction techniques (e.g., PCA, t-SNE).
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Subtopic 3.3:
Association rule mining for pattern discovery.
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Subtopic 3.4:
Anomaly detection for outlier identification.
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Subtopic 3.5:
Applications of unsupervised learning in big data.
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Subtopic 4.1:
Utilizing Spark MLlib for distributed machine learning.
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Subtopic 4.2:
Using TensorFlow and PyTorch for deep learning on big data.
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Subtopic 4.3:
Implementing scikit-learn for machine learning workflows.
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Subtopic 4.4:
Integrating machine learning with Hadoop and Spark.
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Subtopic 4.5:
Best practices for tool selection and integration.
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Subtopic 5.1:
Feature selection and transformation techniques.
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Subtopic 5.2:
Handling missing data and outliers.
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Subtopic 5.3:
Creating new features from raw data.
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Subtopic 5.4:
Utilizing domain knowledge for feature engineering.
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Subtopic 5.5:
Best practices for feature engineering.
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Subtopic 6.1:
Evaluating model performance using various metrics.
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Subtopic 6.2:
Implementing cross-validation and hyperparameter tuning.
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Subtopic 6.3:
Optimizing models for performance and accuracy.
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Subtopic 6.4:
Handling imbalanced datasets.
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Subtopic 6.5:
Best practices for model evaluation.
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Subtopic 7.1:
Deploying machine learning models in production environments.
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Subtopic 7.2:
Utilizing containerization and orchestration tools (e.g., Docker, Kubernetes).
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Subtopic 7.3:
Implementing model serving and API endpoints.
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Subtopic 7.4:
Monitoring model performance in production.
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Subtopic 7.5:
Best practices for model deployment.
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Subtopic 8.1:
Debugging machine learning models and pipelines.
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Subtopic 8.2:
Analyzing model errors and performance issues.
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Subtopic 8.3:
Utilizing debugging tools and techniques.
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Subtopic 8.4:
Identifying and resolving model biases.
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Subtopic 8.5:
Best practices for model troubleshooting.
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Subtopic 9.1:
Implementing data security in machine learning workflows.
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Subtopic 9.2:
Utilizing authentication and authorization.
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Subtopic 9.3:
Implementing data encryption and masking.
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Subtopic 9.4:
Auditing and compliance in machine learning.
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Subtopic 9.5:
Best practices for data security.
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Subtopic 10.1:
Integrating machine learning models with Hadoop and Spark.
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Subtopic 10.2:
Utilizing cloud-based machine learning services (e.g., AWS SageMaker, Azure Machine Learning).
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Subtopic 10.3:
Implementing real-time machine learning pipelines.
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Subtopic 10.4:
Best practices for integration.
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Subtopic 11.1:
Monitoring model performance and drift.
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Subtopic 11.2:
Implementing model retraining and updating.
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Subtopic 11.3:
Utilizing model monitoring tools and techniques.
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Subtopic 11.4:
Handling model versioning and rollback.
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Subtopic 11.5:
Best practices for model maintenance.
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Subtopic 12.1:
Deep learning for complex data patterns.
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Subtopic 12.2:
Natural language processing (NLP) for text data.
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Subtopic 12.3:
Time series analysis for forecasting.
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Subtopic 12.4:
Reinforcement learning for decision-making.
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Subtopic 12.5:
Advanced techniques for large-scale data processing.
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Subtopic 13.1:
Utilizing cloud-based machine learning services.
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Subtopic 13.2:
Deploying machine learning models on AWS, Azure, and GCP.
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Subtopic 13.3:
Optimizing cloud resources for machine learning.
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Subtopic 13.4:
Best practices for cloud-based machine learning.
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Subtopic 14.1:
Implementing data governance policies in machine learning.
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Subtopic 14.2:
Utilizing metadata management tools.
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Subtopic 14.3:
Implementing data lineage and data dictionary.
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Subtopic 14.4:
Best practices for data governance.
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Subtopic 15.1:
Emerging trends in machine learning for big data.
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Subtopic 15.2:
Utilizing AI and automation in machine learning workflows.
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Subtopic 15.3:
Implementing federated learning and privacy-preserving machine learning.
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Subtopic 15.4:
Best practices for future machine learning.
