Contents:
- Engineering Features for Machine Learning to assess the impact of data quality and size, transform various data formats, and address business risks and ethical considerations.
- Training and Tuning ML Systems and Models to design, optimize, train, validate, and evaluate machine and deep learning models while ensuring ethical practices.
- Operationalizing ML Models to deploy models, secure pipelines, maintain post-production systems, and manage risks and compliance.
- Solving Business Problems Using AI and ML to apply AI techniques to real-world challenges across industries using a vendor-neutral approach.
- Collecting and refining the data set to prepare high-quality data for model training and ensure relevance and accuracy.
- Setting Up and Training a Model to configure machine learning workflows and train models using appropriate algorithms.
- Finalizing a Model to validate, optimize, and prepare models for deployment in production environments.
- Building Linear Regression Models to predict continuous outcomes and understand relationships between variables.
- Building Classification Models to categorize data into predefined classes using supervised learning techniques.
- Building Clustering Models to group data based on similarity without predefined labels using unsupervised learning.
- Advanced Models to explore complex architectures and techniques for specialized AI applications.
- Building Support-Vector Machines to implement robust classification and regression models for high-dimensional data.
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