Master the practical skills to formulate, solve, and interpret optimization problems for analytics-driven decision making. Learn hands-on modeling of linear and integer programs, resource allocation, transportation, assignment, and sensitivity analysis using Python and industry-standard solvers.

Master the mathematical and practical foundations of neural network optimization, explore advanced regularization techniques, and gain hands-on experience with PyTorch and TensorFlow for robust model training.

Learn the core PHP essentials needed to build dynamic, interactive web applications. Master functions, handle form data safely, work with superglobals, and manage file operations with confidence to create clean, secure, and reliable PHP projects.

A comprehensive intermediate course guiding learners through the motivation, mathematical foundations, and practical implementation of Principal Component Analysis (PCA) for dimensionality reduction in data science and machine learning.

Build strong intuition for probability distributions central to machine learning. Explore the exponential family, Gaussian, Bernoulli, and Multinomial distributions, and understand their roles in modeling, loss functions, and inference, with minimal code and clear visualizations.

A beginner-friendly course introducing Python programming through real-world bioinformatics problems. Learn to handle biological data formats, analyze DNA sequences, process next-generation sequencing data, and build reproducible analysis pipelines—all with engaging, hands-on tasks and clear explanations.

Master practical Python skills for everyday tasks, from calculations and data organization to visualizing information and automating routines. This course blends engaging theory with hands-on challenges, empowering you to solve real-world problems efficiently using Python.

Unlock the power of Python to analyze financial data, evaluate investment opportunities, and automate key investor workflows. This course guides you through practical, real-world scenarios using Python, focusing on data analysis, visualization, and quantitative techniques relevant to investors.

A comprehensive, theory-focused course on the core concepts, architectures, and evaluation strategies behind Retrieval-Augmented Generation (RAG) systems. Designed for learners seeking a deep understanding of why RAG exists, how retrieval and generation are integrated, and how to evaluate and improve RAG pipelines.

Explore the fundamentals and advanced concepts of Reactive Programming in Java. This course provides a comprehensive, theory-driven approach with practical, real-world examples to help you understand and apply reactive principles in Java applications.

Master the essentials of simulation and Monte Carlo modeling in Python. Learn to generate random samples, build uncertainty models, estimate risk, and simulate simple financial scenarios using practical code and hands-on exercises.

Launch your own Social Media Marketing Agency from scratch with this powerhouse course that walks you through every step—from understanding platforms and trends to setting up your business, landing clients, and running high-performing campaigns. Whether you're starting with zero experience or scaling a freelance hustle, you'll gain the skills, strategies, tools, and templates to build, grow, and automate a results-driven agency confidently. Learn how to define your niche, price your services, master content and ads, and scale sustainably—all while delivering serious ROI for your clients. If you're ready to turn your digital know-how into a thriving business, this is the roadmap you've been waiting for. And when you've mastered it, you'll feel a sense of accomplishment like never before.

Explore the mathematical foundations of spectral methods in machine learning. Understand how eigenvalues, eigenvectors, and spectral decompositions underpin dimensionality reduction, graph learning, and kernel methods, with a focus on theory and structure.

Explore the mathematical foundations of machine learning generalization. This course covers empirical risk minimization, bias–variance tradeoff, VC dimension, generalization bounds, and the theory of overfitting, equipping you with rigorous intuition for model selection and evaluation.

Master the art of writing and applying subqueries in SQL through real-world scenarios. This course covers the theory and practice of subqueries, including their use in SELECT, WHERE, and FROM clauses, as well as correlated subqueries. Each section features a unique dataset and practical challenges to reinforce your learning.