Data Versioning and Experiment Tracking
data versioning and experiment tracking in AI engineering. Learn the principles of DVC and MLflow to ensure reproducible and scalable ML systems.
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Python
Data Pipeline Design and Implementation
Robust data pipelines for machine learning. Learn to build scalable batch and real-time ML workflows using Python, Airflow, and modern data engineering tools.
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Advanced Dimensionality Reduction: UMAP and Autoencoders
UMAP and autoencoders using Python, TensorFlow, and umap-learn for data visualization, feature engineering, and generative modeling.
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Dimensionality Reduction: PCA and t-SNE
Dimensionality reduction with PCA and t-SNE. Mathematical foundations, Python implementation, and industry applications for AI engineering and data science.
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Categorical Data Encoding: Onehot, Ordinal & Target Encoding
Master one-hot, ordinal, and target encoding with Python, scikit-learn, and real-world examples to handle high-cardinality data in machine learning.
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Automated Feature Engineering and Discovery
Learn to implement Deep Feature Synthesis with Featuretools, understand genetic programming, and deploy scalable ML pipelines.
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Feature Selection: Statistical and Model-based Methods
Learn statistical and model-based methods like RFE and Lasso, and implement them in Python with scikit-learn to improve model performance.
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Feature Engineering: Creating New Features from Raw Data
Learn to create polynomial features, interaction terms, and temporal features using Python, scikit-learn, and pandas to improve model performance.
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Data Validation and Quality Assurance for Machine Learning
Automated data validation and quality assurance pipelines. Learn to use Pandera and Great Expectations to prevent bad data from impacting model performance.
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Real-time Data Streaming Architecture
Design and implement real-time data streaming architectures for machine learning using Apache Kafka and Apache Storm, with practical Python examples.
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