Optimization with Metaheuristics in Python
Optimization with Metaheuristics in Python, available at $79.99, has an average rating of 3.7, with 86 lectures, 7 quizzes, based on 969 reviews, and has 5636 subscribers.
You will learn about Learn the foundations of optimization Understand metaheuristics such as Simulated Annealing, Genetic Algorithm, Tabu Search, and Evolutionary Strategies Be able to code metaheuristics in Python Handle constraints though penalties This course is ideal for individuals who are Anyone who wants to learn about metaheuristics or Anyone who wants to learn Genetic Algorithm or Anyone who wants to learn Simulated Annealing or Anyone who wants to learn Tabu Search or Anyone who wants to learn Evolutionary Strategies or Anyone who wants to code metaheuristics in Python or Anyone who wants to learn how to handle constraints It is particularly useful for Anyone who wants to learn about metaheuristics or Anyone who wants to learn Genetic Algorithm or Anyone who wants to learn Simulated Annealing or Anyone who wants to learn Tabu Search or Anyone who wants to learn Evolutionary Strategies or Anyone who wants to code metaheuristics in Python or Anyone who wants to learn how to handle constraints.
Enroll now: Optimization with Metaheuristics in Python
Summary
Title: Optimization with Metaheuristics in Python
Price: $79.99
Average Rating: 3.7
Number of Lectures: 86
Number of Quizzes: 7
Number of Published Lectures: 83
Number of Published Quizzes: 7
Number of Curriculum Items: 93
Number of Published Curriculum Objects: 90
Original Price: $129.99
Quality Status: approved
Status: Live
What You Will Learn
- Learn the foundations of optimization
- Understand metaheuristics such as Simulated Annealing, Genetic Algorithm, Tabu Search, and Evolutionary Strategies
- Be able to code metaheuristics in Python
- Handle constraints though penalties
Who Should Attend
- Anyone who wants to learn about metaheuristics
- Anyone who wants to learn Genetic Algorithm
- Anyone who wants to learn Simulated Annealing
- Anyone who wants to learn Tabu Search
- Anyone who wants to learn Evolutionary Strategies
- Anyone who wants to code metaheuristics in Python
- Anyone who wants to learn how to handle constraints
Target Audiences
- Anyone who wants to learn about metaheuristics
- Anyone who wants to learn Genetic Algorithm
- Anyone who wants to learn Simulated Annealing
- Anyone who wants to learn Tabu Search
- Anyone who wants to learn Evolutionary Strategies
- Anyone who wants to code metaheuristics in Python
- Anyone who wants to learn how to handle constraints
This course will guide you on what optimizationis and what metaheuristicsare. You will learn why we use metaheuristics in optimization problems as sometimes, when you have a complex problem you’d like to optimize, deterministic methods will not do; you will not be able to reach the best and optimal solution to your problem, therefore, metaheuristics should be used.
This course covers information on metaheuristics and four widely used techniques which are:
-
Simulated Annealing
-
Genetic Algorithm
-
Tabu Search
-
Evolutionary Strategies
By the end of this course, you will learn what Simulated Annealing, Genetic Algorithm, Tabu Search, and Evolutionary Strategies are, why they are used, how they work, and best of all, how to code them in Python! With no packages and no libraries, learn to code them from scratch!! You will also learn how to handle constraints using the penalty method.
Here’s the awesome part –> you do NOT need to know Python programming!
-
This course will teach you how to optimize continuous and combinatorial problems using Python
-
Where every single line of code is explained thoroughly
-
The code is written in a simple manner that you will understand how things work and how to code the algorithms even with zero knowledge in Python
-
Basically, you can think of this as not only a course that teaches you 4 well known metaheuristics, but also Python programming!
Please feel free to ask me any question! Don’t like the course? Ask for a 30-day refund!!
Real Testaments –>
1) “I can say that this is the best course I’ve had on Udemy ! Dana is a very good instructor. She not only explains the problems and the coding, but also reassures you and remove the fears you might have when learning complex concepts. For someone with a business background, this topic was close to a nightmare ! I highly recommend this course for anyone interested in learning about Metaheuristics. Again, big THANK YOU Dana ! :)” — Logistics Knowledge Bank, 5 star rating
2) “I am half way through the course. What I learnt so far is far beyond what I expected. What I really liked is the applicability of the examples to real world problems. The most exciting feature in the course is the hands on, what you learn will be implemented in python and you can follow every single step. If you did not understand, the instructor is there to help. I even felt like it is a one to one course. Thanks a lot to the instructor.” — Ali, 5 star rating
3) “The best introduction to Metaheuristics bar none. Best value course on Udemy. I love that we cover a bit of theory and code the actual algorithm itself. The course doesn’t just give you some package to use but presents you with code very easy to follow. The code is not optimized or written for maximum performance but for maximum readability. This means you can play around with it once you really understand it and speed it up. Thank you Dana for this amazing course. It has given me the confidence to code my own slightly more advanced algorithms from Sean Luke’s book: Essential Metaheuristics. I feel the two are great companions.” — Dylan, 5 star rating
4) “It is a great introduction to Metaheuristics. The course deserves five stars for the overall information on this topic. The instructor is talented and knowledgeable about the optimization problems. I recommend the course for someone looking to solve an optimization problem.” — Abdulaziz, 5 star rating
5) “I still not finished the course, but until now, I am really satisfied with I’ve seen. THEORETICAL EXPLANATIONS: Dana is very didactic, before presenting the code she always briefly present the theory in a simple way, much easier to understand than books and journal papers explanations. Of course, it is necessary to complement this with other materials, but if you already have a theoretical base, it is just great! Dana, I loved your explanation about crossover and mutation! FOR BEGINNERS IN PYTHON: I am a beginner in Python and even in programming, so Dana’s code helped me a lot to understand the meaning of each step and variable since she wrote a very readable code. GOOD TIME-MANAGEMENT: Dana presents the code already done but she explains what she has done in each step. Thus, in 5 minutes we can learn a lot, without being bored. I prefer this way of doing because I’ve done courses with teachers that do the code during the classes and we waste a lot of time fixing errors and bugs. She is objective and efficient on teaching, I like that. There are things not totally clear to me on courses, so I ask questions to Dana. She takes some days to give us an answer, but she replies anyway. I would appreciate an example of constraint handling for combinatorial problems.” — Rachel, 4.5 star rating
6) “Nice course that really does explain Metaheuristics in a very practical way. Highly recommended!” — David, 5 star rating
Course Curriculum
Chapter 1: Introduction
Lecture 1: Promo Video
Lecture 2: Course Outline
Lecture 3: Operations Research
Lecture 4: Continuous vs. Combinatorial Problems
Lecture 5: P vs. NP Problems Resource (IMPORTANT!!!)
Lecture 6: Metaheuristics
Lecture 7: Search Techniques #1
Lecture 8: Search Techniques #2
Lecture 9: Search Techniques #3
Chapter 2: Simulated Annealing
Lecture 1: Simulated Annealing #1
Lecture 2: Simulated Annealing #2
Lecture 3: Simulated Annealing #3
Lecture 4: Updated Course (IMPORTANT)
Lecture 5: SA: Continuous Problem – The Himmelblau Function
Lecture 6: SA: Continuous Problem – Python #1 – Basics
Lecture 7: SA: Continuous Problem – Python #2 – User-Set Parameters
Lecture 8: SA: Continuous Problem – Python #3 – Neighborhood Search
Lecture 9: SA: Continuous Problem – Python #4 – Do We Accept Worse Moves?
Lecture 10: SA: Continuous Problem – Python #5 – Iterations and Solving
Lecture 11: SA: Continuous Problem – Python #6 – Different Runs for Different Outcomes
Lecture 12: SA: Continuous Problem – Python #7 – Plotting Results #1
Lecture 13: SA: Continuous Problem – Python #8 – Plotting Results #2
Lecture 14: Updated Course Reminder (IMPORTANT)
Lecture 15: SA: Coding a Combinatorial Problem with Python, Part #1
Lecture 16: SA: Coding a Combinatorial Problem with Python, Part #2
Lecture 17: SA: Coding a Combinatorial Problem with Python, Part #3
Chapter 3: Genetic Algorithm
Lecture 1: Genetic Algorithm #1
Lecture 2: Genetic Algorithm #2
Lecture 3: Genetic Algorithm #3
Lecture 4: Chromosome Length
Lecture 5: Genetic Algorithm – Pseudocode and Flowchart
Lecture 6: Genetic Algorithm – Methodology
Lecture 7: Updated Course (IMPORTANT)
Lecture 8: GA: Coding a Continuous Problem – The Himmelblau Function
Lecture 9: GA: Continuous Problem – Python #1 – Objective Function Value #1
Lecture 10: GA: Continuous Problem – Python #2 – Objective Function Value #2
Lecture 11: GA: Continuous Problem – Python #3 – Objective Function Value #3
Lecture 12: GA: Continuous Problem – Python #4 – Selecting Parents #1
Lecture 13: GA: Continuous Problem – Python #5 – Selecting Parents #2
Lecture 14: GA: Continuous Problem – Python #6 – Selecting Parents #3
Lecture 15: GA: Continuous Problem – Python #7 – Selecting Parents #4
Lecture 16: GA: Continuous Problem – Python #8 – Selecting Parents #5
Lecture 17: GA: Continuous Problem – Python #9 – Selecting Parents #6
Lecture 18: GA: Continuous Problem – Python #10 – Crossover Operator #1
Lecture 19: GA: Continuous Problem – Python #11 – Crossover Operator #2
Lecture 20: GA: Continuous Problem – Python #12 – Crossover Operator #3
Lecture 21: GA: Continuous Problem – Python #13 – Mutation Operator #1
Lecture 22: GA: Continuous Problem – Python #14 – Mutation Operator #2
Lecture 23: GA: Continuous Problem – Python #15 – Mutation Operator #3
Lecture 24: GA: Continuous Problem – Python #16 – Mutation Operator #4
Lecture 25: GA: Continuous Problem – Python #17 – Functions and Packages
Lecture 26: GA: Continuous Problem – Python #18 – Solving #1
Lecture 27: GA: Continuous Problem – Python #19 – Solving #2
Lecture 28: GA: Continuous Problem – Python #20 – Solving #3
Lecture 29: GA: Continuous Problem – Python #21 – Solving #4
Lecture 30: GA: Continuous Problem – Python #22 – Solving #5
Lecture 31: GA: Continuous Problem – Python #23 – Solving #6
Lecture 32: GA: Continuous Problem – Python #24 – Solving #7
Lecture 33: GA: Continuous Problem – Python #25 – Solving #8
Lecture 34: GA: Continuous Problem – Python #26 – Solving #9
Lecture 35: GA: Continuous Problem – Python #27 – Plotting Results
Lecture 36: Updated Course Reminder (IMPORTANT)
Lecture 37: GA: Coding a Combinatorial Problem with Python, Part #1
Lecture 38: GA: Coding a Combinatorial Problem with Python, Part #2
Lecture 39: GA: Coding a Combinatorial Problem with Python, Part #3
Lecture 40: GA: Coding a Combinatorial Problem with Python, Part #4
Lecture 41: GA: Coding a Combinatorial Problem with Python, Part #5
Chapter 4: Tabu Search
Lecture 1: Tabu Search #1
Lecture 2: Tabu Search #2
Lecture 3: Tabu Search #3
Lecture 4: TS: Coding a Combinatorial Problem with Python, Part #1
Lecture 5: TS: Coding a Combinatorial Problem with Python, Part #2
Lecture 6: TS: Coding a Combinatorial Problem with Python, Part #3
Chapter 5: Evolutionary Strategies
Lecture 1: Evolutionary Strategies #1
Lecture 2: Evolutionary Strategies #2
Lecture 3: Evolutionary Strategies #3
Lecture 4: ES: Coding a Continuous Problem with Python, Part #1
Lecture 5: ES: Coding a Continuous Problem with Python, Part #2
Lecture 6: ES: Coding a Continuous Problem with Python, Part #3
Chapter 6: Constraint Handling
Lecture 1: Constraint Handling #1
Lecture 2: Constraint Handling #2
Lecture 3: Constraint Handling #3
Chapter 7: BONUS OFFER!!
Lecture 1: Bonus Lecture: Discounted Coupons
Instructors
-
Curiosity for Data Science
Architect and Industrial Engineer
Rating Distribution
- 1 stars: 18 votes
- 2 stars: 32 votes
- 3 stars: 130 votes
- 4 stars: 339 votes
- 5 stars: 450 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
You may also like
- Top 10 Video Editing Courses to Learn in November 2024
- Top 10 Music Production Courses to Learn in November 2024
- Top 10 Animation Courses to Learn in November 2024
- Top 10 Digital Illustration Courses to Learn in November 2024
- Top 10 Renewable Energy Courses to Learn in November 2024
- Top 10 Sustainable Living Courses to Learn in November 2024
- Top 10 Ethical AI Courses to Learn in November 2024
- Top 10 Cybersecurity Fundamentals Courses to Learn in November 2024
- Top 10 Smart Home Technology Courses to Learn in November 2024
- Top 10 Holistic Health Courses to Learn in November 2024
- Top 10 Nutrition And Diet Planning Courses to Learn in November 2024
- Top 10 Yoga Instruction Courses to Learn in November 2024
- Top 10 Stress Management Courses to Learn in November 2024
- Top 10 Mindfulness Meditation Courses to Learn in November 2024
- Top 10 Life Coaching Courses to Learn in November 2024
- Top 10 Career Development Courses to Learn in November 2024
- Top 10 Relationship Building Courses to Learn in November 2024
- Top 10 Parenting Skills Courses to Learn in November 2024
- Top 10 Home Improvement Courses to Learn in November 2024
- Top 10 Gardening Courses to Learn in November 2024