Optimization Algorithms in Python

Optimize your work and projects with AI! Learn from the ground up and get all the math you need to build and deploy your own optimization algorithms.

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Learn from Industry Leaders

Jones Granatyr has a PhD in AI, and he specializes in working with ANNs to predict trust and reputation. Jones has taught several courses on AI, from genetic algorithms and facial detection to text mining and recommendation systems. He likes to dive deep into programming languages ​​(Python, R, Java) and tools (tensorflow, keras, pandas, sklearn, opencv, dlib, weka, nltk). Jones loves demystifying AI ​and helping students to understand how such technology can be used in business. For this reason, he founded the IA Expert portal, a website all about artificial intelligence.

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Progress in business, faster

Improving investments; syncing calendars; transforming networks: Optimization algorithms present the ultimate life hacks.

Learn all the logic and math

Our step-by-step tutorials in Python make the principles easy to learn, no matter how rudimentary your coding skills may be.

Apply your knowledge to the real world

Build an optimization algorithm from scratch and then deploy it to accomplish your next business idea.

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  • Solve real-world problems with optimization algorithms: optimize flight calendars and allocate dorm rooms fairly.
  • Build optimization algorithms from scratch and improve your mathematical knowledge.
  • Work with random search algorithms (for using on functions that aren’t continuous).
  • Work with hill-climbing algorithms (for making incremental changes to an arbitrary solution).
  • Work with the simulated annealing method (for selecting a ‘global optimum’; the best value compared to all other solutions).
  • Work with genetic algorithms (for selecting the ‘fittest’ data)

Why Should I Take This Course?

Gain industry knowledge

Learn the method behind optimization algorithms

No expensive software installation

Get to know the four key optimization algorithms that are used to supercharge business operations.

High Job Market Demand

AI Skills Gap

Top Emerging Job

Dr. Jones Granatyr has taught several courses with SuperDataScience and is a popular Udemy instructor, teaching hundreds of students worldwide.

Become a Machine Learning engineer!

“From 2015 to 2018, job postings for Machine Learning Engineers grew 344%” Forbes

Average salary

$146,085
for a ML Engineer in the U.S.
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What You Will Get

Expert Instructors

  • Learn from the Industry Leaders
  • 1M+ students
  • 50,000 five-star reviews
  • 49 Courses Rated 4.5+

On-Demand Lifetime Content

  • Learn at your own pace with on-demand video
  • Enjoy lifetime access to the course
  • Available to view on mobiles and TV

Intensive Content

  • Mathematical concepts reviewed
  • 7 hours of on-demand video content
  • 11 articles
  • 5 downloadable resources

On-Demand Lifetime Content

  • Learn at your own pace
  • Lifetime access to the course
  • View on mobile or TV

People Talk About Us

The course is well structured so you incrementally develop your intuition from a simple (random search) towards a more complex algorithm (genetic algorithm). Very nice…

Guilherme Matos Passarini, MSc

I’m so excited about this course. Because it combines excellent content theoretical and practical examples. It’s amazing learning!

Fabio Spak

Very clear explanations. For now everything makes sense. I believe this introduction will help me to complete other SuperDataScience AI courses. Thank you.

Tautvaldas

Optimization Algorithms in Python: A great addition to any data scientist’s toolkit

What are optimization algorithms?
Optimization algorithms help us to make our lives and work easier and better. They first identify ‘losses’ in our datasets – obstacles that stop us from improving, or that slow down an operation – and then they work out how to reduce them.

Several types of optimization algorithm exist. Among them are:
· Gradient descent
· Hill climbing
· Random search
· Genetic

None of these is one-size-fits-all. We have to know which algorithm to use (and yes, that means finding the optimal optimization algorithm!) to get the best results.

Optimization Algorithms in Python will help you to do just that.

What exactly do optimization algorithms help us do?
In the world of business, optimization algorithms will quickly become any machine learning engineer’s best friend. Such algorithms ensure that we are working in the most efficient way, which frees us up to do the important stuff.

We can use optimization algorithms to solve several real-world business problems.

Among others, they help us to:
· aid managers in choosing wise investments
· sync staff calendars
· help telecommunication companies design new optical networks
· assist couriers in planning goods deliveries

Why should I learn about optimization algorithms?
Learning to find the best solution based on evidence is an important life skill, and it is also critical to data science. Optimization algorithms weigh up the multiple aspects of a problem to find the best solution.

But how can we get our hands on them? How can we choose the right algorithm for the task at hand? And how do we get an optimization algorithm to identify what the best solution is for us?

In Artificial Intelligence: Optimization Algorithms in Python, we’ll explain how you can define all possible solutions to a problem, as well as their variables, restrictions and parameters. Then we’ll show you which algorithm will find the best pathway to the best solution.

Artificial Intelligence: Optimization Algorithms in Python helps you build and deploy optimization algorithms wherever you need to apply them.

Learn why optimization algorithms are so useful in business, and start building your own!

In Artificial Intelligence: Optimization Algorithms in Python, course instructor Jones Granatyr takes you step-by-step through each of four key algorithms. He’ll teach you what they are, their unique aspects, and how you can apply them.

In our course, you’ll get all the logic behind these algorithms, and we’ll show you how to implement them from scratch, without using pre-built libraries.

Learn by doing
Implement each technique using Python’s primary resources. You’ll even get to debug the source code in Python!

No coding skills? No problem. Jones will take you step-by-step through each unit with two practical case studies:

1. Optimize travel plans: Six thrifty friends who are flying from the same airport want to share transport to and from the same terminal. We will use four optimization algorithms to find the best outbound and return flights for each person. The aims:
· to find the best value airline tickets
· to reduce the waiting time for each holidaymaker at the airport.
We will compare the results from each approach to find the best solution.

2. Optimize dorm allocation: Anyone who has been to university will be familiar with the scattergun approach some administrators take to granting dorm rooms! Let’s improve the process. Our algorithms will indicate which dormitory each student should be allocated, based on their submitted preferences.

Course outcomes
By the end of the course, you will have built four optimization algorithms, which you can take away to solve problems in business and beyond.

Why should I take this course?
Optimization algorithms are a vital element in a machine learning engineer’s toolkit. As an engineer, you must understand how these algorithms work.

Learn how to build and deploy these algorithms, and you’ll instantly make yourself much more attractive to a job market that thrives on making processes faster and better.

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