Optimization Algorithms in Python

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.

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.