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.
Data Collection Strategies and Sources Read More »
AI Engineering Course
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.
Jupyter Notebooks and Development Environment Setup Read More »
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.
Matplotlib and Seaborn: Data Visualization for AI Read More »
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.
Pandas for Data Manipulation: DataFrames and Series Read More »
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.
NumPy Mastery: Array Operations and Broadcasting Read More »
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.
Advanced Data Structures and Algorithms for AI Engineering Read More »
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.
Convex Optimization and Lagrange Multipliers Read More »
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.
Information Theory: Entropy, Cross-entropy, & KL Divergence Read More »
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.
Bayesian Statistics and Probability in Machine Learning Read More »
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.
Statistics: Descriptive Statistics and Hypothesis Testing Read More »