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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AI Engineering Volume 1
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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Advanced Data Structures and Algorithms for AI Engineering
Master data structures, algorithms, OOP, and functional programming of python to build high-performance, scalable AI systems.
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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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Bayesian Statistics and Probability in Machine Learning
Bayesian statistics in AI. Learn to implement Bayesian inference, quantify uncertainty, and build probabilistic models in Python using PyMC.
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Statistics: Descriptive Statistics and Hypothesis Testing
Descriptive statistics and hypothesis testing for AI engineers. Learn to analyze data, run t-tests, and use Python for statistical inference.
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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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