Executive Development Programme in Machine Learning Engineering with H2O

This program is designed for mid-to-senior level professionals in data science, engineering, and related fields who are looking to enhance their expertise in machine learning engineering. Participants will gain hands-on experience with H2O, a leading open-source platform for deploying machine learning models at scale.

Upon completion, attendees will be proficient in building, deploying, and managing complex machine learning pipelines, and will have the skills to implement advanced machine learning models in production environments, driving innovation and competitive advantage in their organizations.

1. 1. Introduction to Machine Learning: Learners will understand the basics of machine learning, its applications, and the ethical considerations. They will gain foundational knowledge of different types of learning algorithms and how to prepare data for machine learning.
2. 2. Data Preprocessing and Feature Engineering: Learners will study the importance of data cleaning, normalization, and feature selection in preparing data for machine learning models. Practical skills include using Python for data manipulation and feature engineering.
3. 3. Supervised Learning Algorithms: This module covers essential supervised learning algorithms such as linear regression, logistic regression, and decision trees. Learners will implement these models using H2O and evaluate their performance.
4. 4. Unsupervised Learning and Clustering: Learners will explore unsupervised learning techniques like clustering and dimensionality reduction. They will learn to use H2O for clustering algorithms such as k-means and hierarchical clustering.
5. 5. Deep Learning Fundamentals: This module introduces artificial neural networks, backpropagation, and deep learning frameworks. Learners will gain hands-on experience with building and training deep neural networks using H2O.
6. 6. Model Evaluation and Validation: Learners will study various techniques for evaluating and validating machine learning models, including cross-validation, AUC, and precision-recall curves. They will apply these techniques to optimize model performance.
7. 7. Advanced H2O Features and Applications: This module delves into advanced H2O features such as automated machine learning (AutoML), time series analysis, and ensemble methods. Learners will apply these features to real-world problems.
8. 8. Deployment and Management of Machine Learning Models: Learners will learn how to deploy machine learning models in production environments, manage model performance, and handle model drift. They will use H2O’s model deployment tools and APIs.
9. 9. Case Studies and Project Work: Through detailed case studies and a final project, learners will apply their knowledge to solve complex business problems. They will work on end-to-end projects, from data collection to model deployment.
10. 10. Advanced Topics in Machine Learning: This module covers cutting-edge topics in machine learning, including reinforcement learning, transfer learning, and ethical considerations in AI. Learners will gain insights into the latest research and industry trends.