Python for Optimization From Basics to Pyomo & MEALPy

Master Python programming, build optimization models in Pyomo, and explore metaheuristics with MEALPy , all in one hands
What you'll learn
Write clean, organized Python programs with control flow and functions
Apply Object-Oriented Programming (OOP) using classes, inheritance, and polymorphism
Manipulate and analyze data with Pandas and NumPy
Formulate and solve linear, nonlinear, and integer problems in Pyomo
Implement multi-objective techniques: Weighted Sum, Epsilon-Constraint, and Goal Programming
Model binary systems such as TSP and N-Queens
Design graphical interfaces using Tkinter
Use MEALPy to implement and tune metaheuristic algorithms (PSO, GA, GWO, etc.)
Evaluate and visualize optimization results with SciPy and Matplotlib
Understand how optimization integrates with AI pipelines and decision systems
Requirements
Basic math knowledge (algebra, functions, simple graphs)
Installed Anaconda + Jupyter Notebook
No prior coding experience required, everything is explained from scratch
Description
Do you want to connect Python programming with real-world optimization and AI applications?This course takes you step-by-step from the very basics of Python to solving advanced optimization problems using Pyomo and MEALPy inside Anaconda / Jupyter Notebook.You'll learn to write efficient code, model mathematical problems, handle data with Pandas and NumPy, and apply both deterministic and metaheuristic optimization methods. By the end of the course, you will be able toesign and solve optimization problems such as the Traveling Salesman Problem and N-QueensCompare exact Pyomo solvers with MEALPy's population-based algorithmsBuild GUI applications and connect optimization with AI fundamentalsThis course is structured for beginners to intermediate learners who want practical, research-oriented skills.All notebooks, datasets, and source codes are provided, ready to run in both online and offline environments.What You'll LearnWrite clean, organized Python programs with control flow and functionsApply Object-Oriented Programming (OOP) using classes, inheritance, and polymorphismManipulate and analyze data with Pandas and NumPyFormulate and solve linear, nonlinear, and integer problems in PyomoImplement multi-objective techniques: Weighted Sum, Epsilon-Constraint, and Goal ProgrammingModel binary systems such as TSP and N-QueensDesign graphical interfaces using TkinterUse MEALPy to implement and tune metaheuristic algorithms (PSO, GA, GWO, etc.)Evaluate and visualize optimization results with SciPy and MatplotlibUnderstand how optimization integrates with AI pipelines and decision systemsAll in one integrated package of 50 lectures, 10 hours of HD video, and 40 + code demonstrations.RequirementsBasic math knowledge (algebra, functions, simple graphs)Installed Anaconda + Jupyter NotebookNo prior coding experience required, everything is explained from scratchInstructorAssoc. Prof. Shady H. E. Abdel Aleem AI & Optimization Specialist | Industry Consultant | Research LeaderPh.D., Electrical Power & Machines - Cairo University (2013)Fellow, Basic Sciences Council, Academy of Scientific Research and Technology, CairoSenior Member, IEEE (SM'21) | Former Member, IETState Encouragement Award (2017) | Medal of Distinction - First Class (2020)Ranked among Stanford University's Top 2 % Scientists WorldwideAuthor of 250 + research papers and 13 books on power systems and optimizationCourse Features10 + hours of HD content50 structured lectures with source codeAll resources and datasets includedFull lifetime access on mobile & desktopCertificate of CompletionStart Learning TodayTake the leap from Python beginner to optimization professional.Join now and learn how to model, analyze, and optimize real-world systems using Python, the language that powers AI and scientific discovery.
Who this course is for
Engineering / Computer Science students who want a solid, practical start in optimization
Researchers who need ready-to-run Python models for academic projects
Professionals seeking to automate or optimize systems using Python tools
Anyone curious about bridging programming + mathematical modeling + AI
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