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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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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EDA: Advanced Visualization and Pattern Discovery
Advanced EDA, covering dimensionality reduction (t-SNE, UMAP), interactive plots, and multidimensional visualization for discovering patterns in complex data.
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Exploratory Data Analysis (EDA): Statistical Analysis
Exploratory Data Analysis (EDA) for AI engineers. Learn to analyze data distributions, correlations, and anomalies to build better machine learning models.
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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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Data Transformation: Scaling, Normalization, and Encoding
Essential data transformation techniques in AI engineering. Learn to implement scaling, normalization, and encoding with Python, scikit-learn.
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Feature Stores: Centralized Feature Management
Enterprise feature stores. Learn the architecture, implementation strategies, and business value of centralizing feature management for ML.
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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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