Machine Learning For Big Data Training Course: Predictive Analytics Mastery

Introduction

Unlock the potential of your large datasets with our Machine Learning for Big Data Training Course. This program is designed to equip you with the skills to effectively apply machine learning algorithms to massive datasets, enabling powerful predictive analytics. In today's data-driven world, the ability to extract valuable insights from big data using machine learning is crucial for informed decision-making. Our machine learning training course provides hands-on experience and expert guidance, empowering you to build scalable and accurate predictive models.

This Big Data machine learning training delves into the core concepts of machine learning, covering topics such as supervised and unsupervised learning, model evaluation, and deployment. You'll gain expertise in using industry-standard tools and techniques to build predictive analytics solutions that drive business value. Whether you're a data scientist, analyst, or engineer, this machine learning for big data course will empower you to leverage the full potential of your data.

Target Audience:

• Data Scientists
• Data Analysts
• Big Data Engineers
• Machine Learning Engineers
• Business Intelligence Professionals
• Software Developers
• Anyone needing machine learning skills for large datasets

Course 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.

Duration

10 Days

Course content

Module 1: Introduction to Machine Learning for Big Data

• Fundamentals of machine learning and big data.
• Overview of machine learning algorithms and applications.
• Setting up a development environment for machine learning on big data.
• Introduction to machine learning tools and frameworks.
• Best practices for machine learning on big data.

Module 2: Supervised Learning for Predictive Analytics

• Linear and logistic regression for predictive modeling.
• Decision trees and random forests for classification and regression.
• Support vector machines (SVMs) for complex data patterns.
• Gradient boosting algorithms (e.g., XGBoost, LightGBM).
• Model evaluation and hyperparameter tuning.

Module 3: Unsupervised Learning for Big Data Insights

• Clustering algorithms (e.g., K-means, DBSCAN).
• Dimensionality reduction techniques (e.g., PCA, t-SNE).
• Association rule mining for pattern discovery.
• Anomaly detection for outlier identification.
• Applications of unsupervised learning in big data.

Module 4: Machine Learning Tools and Frameworks for Big Data

• Utilizing Spark MLlib for distributed machine learning.
• Using TensorFlow and PyTorch for deep learning on big data.
• Implementing scikit-learn for machine learning workflows.
• Integrating machine learning with Hadoop and Spark.
• Best practices for tool selection and integration.

Module 5: Feature Engineering for Big Data

• Feature selection and transformation techniques.
• Handling missing data and outliers.
• Creating new features from raw data.
• Utilizing domain knowledge for feature engineering.
• Best practices for feature engineering.

Module 6: Model Evaluation and Performance Optimization

• Evaluating model performance using various metrics.
• Implementing cross-validation and hyperparameter tuning.
• Optimizing models for performance and accuracy.
• Handling imbalanced datasets.
• Best practices for model evaluation.

Module 7: Model Deployment and Productionization

• Deploying machine learning models in production environments.
• Utilizing containerization and orchestration tools (e.g., Docker, Kubernetes).
• Implementing model serving and API endpoints.
• Monitoring model performance in production.
• Best practices for model deployment.

Module 8: Troubleshooting and Debugging Machine Learning Models

• Debugging machine learning models and pipelines.
• Analyzing model errors and performance issues.
• Utilizing debugging tools and techniques.
• Identifying and resolving model biases.
• Best practices for model troubleshooting.

Module 9: Data Security and Access Control in Machine Learning

• Implementing data security in machine learning workflows.
• Utilizing authentication and authorization.
• Implementing data encryption and masking.
• Auditing and compliance in machine learning.
• Best practices for data security.

Module 10: Integrating Machine Learning with Big Data Platforms

• Integrating machine learning models with Hadoop and Spark.
• Utilizing cloud-based machine learning services (e.g., AWS SageMaker, Azure Machine Learning).
• Implementing real-time machine learning pipelines.
• Best practices for integration.

Module 11: Model Monitoring and Maintenance

• Monitoring model performance and drift.
• Implementing model retraining and updating.
• Utilizing model monitoring tools and techniques.
• Handling model versioning and rollback.
• Best practices for model maintenance.

Module 12: Advanced Machine Learning Techniques for Big Data

• Deep learning for complex data patterns.
• Natural language processing (NLP) for text data.
• Time series analysis for forecasting.
• Reinforcement learning for decision-making.
• Advanced techniques for large-scale data processing.

Module 13: Machine Learning on Cloud Platforms

• Utilizing cloud-based machine learning services.
• Deploying machine learning models on AWS, Azure, and GCP.
• Optimizing cloud resources for machine learning.
• Best practices for cloud-based machine learning.

Module 14: Machine Learning and Data Governance

• Implementing data governance policies in machine learning.
• Utilizing metadata management tools.
• Implementing data lineage and data dictionary.
• Best practices for data governance.

Module 15: Future Trends in Machine Learning for Big Data

• Emerging trends in machine learning for big data.
• Utilizing AI and automation in machine learning workflows.
• Implementing federated learning and privacy-preserving machine learning.
• Best practices for future machine learning.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.