Data Quality Assessment and Profiling
Data quality assessment and profiling for AI. Learn to implement systematic data validation and profiling using Python, Great Expectations & other tools.
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Python
Data Collection Strategies and Sources
Learn to gather high-quality data using APIs, web scraping with Python, and database queries. Includes practical code examples and industry best practices.
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Jupyter Notebooks and Development Environment Setup
Learn to use Jupyter, VS Code, Git, and Poetry for reproducible and scalable machine learning projects for AI Engineering.
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Matplotlib and Seaborn: Data Visualization for AI
Data visualization for AI. Learn to implement, analyze, and design effective plots for exploratory data analysis and model interpretation.
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Pandas for Data Manipulation: DataFrames and Series
Pandas for AI and ML Applications. Learn to use DataFrames and Series for data cleaning, transformation, aggregation, and preparation for ML pipelines.
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NumPy Mastery: Array Operations and Broadcasting
NumPy for AI and machine learning. This chapter covers array operations, broadcasting, vectorization, and advanced indexing with practical Python examples.
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Convex Optimization and Lagrange Multipliers
Convex optimization, Lagrange multipliers, and KKT conditions for AI. Learn the theory and implement solutions for SVMs and other ML models in Python.
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Information Theory: Entropy, Cross-entropy, & KL Divergence
Detailed chapter on Information Theory in AI. Learn to implement and apply entropy, cross-entropy, and KL divergence in Python for machine learning.
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Probability Theory: Basic Concepts and Distributions
Introduction to probability theory for AI engineering. Learn core concepts, distributions, and Python implementation for machine learning.
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Multivariable Calculus and Gradient Descent
Multivariable calculus for AI. Learn to implement Gradient Descent, understand optimization landscapes, and use Python to train ML models.
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