Practical Machine Learning for Business
Artificial Intelligence helps business grow faster than ever before. This AI boom is due to the methods of machine learning, a subfield of AI, which is used for extracting insights from big data and automating task. This text gives an overview of machine learning for business.
What is machine learning?
Machine learning is a subfield of artificial intelligence (AI). This subfield is most closely associated with big data analysis. Machine learning provides algorithms and software tools for extracting insights from large sets of data. There is also a lot of machine learning experimentation with randomised or semi-randomised data, where the machine learns by trial and error.
Machine learning is the practice of using computer algorithms to emulate or simulate a human brain in order to improve the way computers understand, process, interpret and make predictions. Computers or other systems learn and make predictions by applying specialized algorithms to data. These algorithms are designed to teach themselves how to make different decisions by weighing different factors, rather than pre-programming such decisions.
The benefit of such a method is that it allows computers to learn how to perform tasks that are very difficult or even impossible for humans to carry out. For example, computers can learn to play chess or Go just from studying previous games or even by just playing with itself as was the case with AlphaGo Zero created by Deepmind.
What are machine learning frameworks?
Machine learning frameworks enable you to deploy a machine learning application. These frameworks simplify the process of designing and developing applications, with built-in abstractions to make this easier for the user.
The most popular are:
Using one of those frameworks allow data scientists to save time and use optimized versions of algorithms, but still giving a lot of flexibility.
What are neural networks?
Neural networks rely on layers of hidden units called neurons. It is an important area for computer vision and machine learning. When one sees patterns in imagery, then we assume that a specific entity or feature has been associated with that pattern. If this is the case, then the neural network will be trained to predict the next location or feature that will be associated with the pattern.
Here’s how a person would set up their own neural network. First, you need to have data to train it on. This means you need to have photos of cats, and it needs to have learned to recognize cats. But if you give it lots of cats to train on, it will learn how to recognize cats — but probably not how to recognize people.
Datasets are datasets for training Neural Networks. They can be either single-item, where a sequence of items are presented to the network and is evaluated, or multi-item, where each item has multiple parameters that are evaluated for accuracy. There are many different dataset types.
Datasets are also the data-source for supervised learning. The datasets can be analysed for a particular feature using the error function and the prediction for that feature can be made using the likelihood function. And finally, the model is used to classify the data and report on its accuracy. A model can be also been trained to make predictions. The dataset can be fed into the network to generate predictions, which it then uses to predict data that is more similar to the training set. This is a supervised learning system.
Deep learning creates new ways of looking at data, and by employing these methods, it can provide better solutions than simple analysis. It allows the intelligence community to make predictions that are up to 99% accurate.
How to use Artificial Intelligence in your business?
It’s far more profitable for the company to invest its existing and prospective profits in hiring people to create new business ideas and create a pipeline of successful products and services instead of paying each person to bring them up to speed.
This includes hiring someone who can train AI to create value in ways that simply providing data is not capable of.
Set aside a budget for innovation and start hiring data scientists for projects oriented towards growth.