15 Topics
10 Days
71 Sessions
Programme Overview
Training Description
Who Should Attend
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
This advanced training equips participants with the knowledge and practical skills required to apply machine learning techniques to large-scale, complex, and high-volume datasets. The course combines machine learning principles with big data technologies to enable participants to build scalable predictive models and intelligent data solutions.Participants will explore data preparation, feature engineering, supervised and unsupervised learning, distributed machine learning, model evaluation, optimization, and deployment. Emphasis is placed on handling large datasets efficiently, addressing scalability challenges, and applying machine learning to real-world business, financial, operational, and analytical problems.
Curriculum & Topics
15 Topics | 71 Sessions
-
Workshop 1.1:
Fundamentals of machine learning and big data.
-
Workshop 1.2:
Overview of machine learning algorithms and applications.
-
Workshop 1.3:
Setting up a development environment for machine learning on big data.
-
Workshop 1.4:
Introduction to machine learning tools and frameworks.
-
Workshop 1.5:
Best practices for machine learning on big data.
-
Workshop 2.1:
Linear and logistic regression for predictive modeling.
-
Workshop 2.2:
Decision trees and random forests for classification and regression.
-
Workshop 2.3:
Support vector machines (SVMs) for complex data patterns.
-
Workshop 2.4:
Gradient boosting algorithms (e.g., XGBoost, LightGBM).
-
Workshop 2.5:
Model evaluation and hyperparameter tuning.
-
Workshop 3.1:
Clustering algorithms (e.g., K-means, DBSCAN).
-
Workshop 3.2:
Dimensionality reduction techniques (e.g., PCA, t-SNE).
-
Workshop 3.3:
Association rule mining for pattern discovery.
-
Workshop 3.4:
Anomaly detection for outlier identification.
-
Workshop 3.5:
Applications of unsupervised learning in big data.
-
Workshop 4.1:
Utilizing Spark MLlib for distributed machine learning.
-
Workshop 4.2:
Using TensorFlow and PyTorch for deep learning on big data.
-
Workshop 4.3:
Implementing scikit-learn for machine learning workflows.
-
Workshop 4.4:
Integrating machine learning with Hadoop and Spark.
-
Workshop 4.5:
Best practices for tool selection and integration.
-
Workshop 5.1:
Feature selection and transformation techniques.
-
Workshop 5.2:
Handling missing data and outliers.
-
Workshop 5.3:
Creating new features from raw data.
-
Workshop 5.4:
Utilizing domain knowledge for feature engineering.
-
Workshop 5.5:
Best practices for feature engineering.
-
Workshop 6.1:
Evaluating model performance using various metrics.
-
Workshop 6.2:
Implementing cross-validation and hyperparameter tuning.
-
Workshop 6.3:
Optimizing models for performance and accuracy.
-
Workshop 6.4:
Handling imbalanced datasets.
-
Workshop 6.5:
Best practices for model evaluation.
-
Workshop 7.1:
Deploying machine learning models in production environments.
-
Workshop 7.2:
Utilizing containerization and orchestration tools (e.g., Docker, Kubernetes).
-
Workshop 7.3:
Implementing model serving and API endpoints.
-
Workshop 7.4:
Monitoring model performance in production.
-
Workshop 7.5:
Best practices for model deployment.
-
Workshop 8.1:
Debugging machine learning models and pipelines.
-
Workshop 8.2:
Analyzing model errors and performance issues.
-
Workshop 8.3:
Utilizing debugging tools and techniques.
-
Workshop 8.4:
Identifying and resolving model biases.
-
Workshop 8.5:
Best practices for model troubleshooting.
-
Workshop 9.1:
Implementing data security in machine learning workflows.
-
Workshop 9.2:
Utilizing authentication and authorization.
-
Workshop 9.3:
Implementing data encryption and masking.
-
Workshop 9.4:
Auditing and compliance in machine learning.
-
Workshop 9.5:
Best practices for data security.
-
Workshop 10.1:
Integrating machine learning models with Hadoop and Spark.
-
Workshop 10.2:
Utilizing cloud-based machine learning services (e.g., AWS SageMaker, Azure Machine Learning).
-
Workshop 10.3:
Implementing real-time machine learning pipelines.
-
Workshop 10.4:
Best practices for integration.
-
Workshop 11.1:
Monitoring model performance and drift.
-
Workshop 11.2:
Implementing model retraining and updating.
-
Workshop 11.3:
Utilizing model monitoring tools and techniques.
-
Workshop 11.4:
Handling model versioning and rollback.
-
Workshop 11.5:
Best practices for model maintenance.
-
Workshop 12.1:
Deep learning for complex data patterns.
-
Workshop 12.2:
Natural language processing (NLP) for text data.
-
Workshop 12.3:
Time series analysis for forecasting.
-
Workshop 12.4:
Reinforcement learning for decision-making.
-
Workshop 12.5:
Advanced techniques for large-scale data processing.
-
Workshop 13.1:
Utilizing cloud-based machine learning services.
-
Workshop 13.2:
Deploying machine learning models on AWS, Azure, and GCP.
-
Workshop 13.3:
Optimizing cloud resources for machine learning.
-
Workshop 13.4:
Best practices for cloud-based machine learning.
-
Workshop 14.1:
Implementing data governance policies in machine learning.
-
Workshop 14.2:
Utilizing metadata management tools.
-
Workshop 14.3:
Implementing data lineage and data dictionary.
-
Workshop 14.4:
Best practices for data governance.
-
Workshop 15.1:
Emerging trends in machine learning for big data.
-
Workshop 15.2:
Utilizing AI and automation in machine learning workflows.
-
Workshop 15.3:
Implementing federated learning and privacy-preserving machine learning.
-
Workshop 15.4:
Best practices for future machine learning.
