Artificial Intelligence and Machine Learning are nowadays two very popular terms which seem to be used interchangeably. Both topics often appear in the context of Big Data, data analysis and solution optimization.
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Machine Learning – what is it?
However, Artificial Intelligence is a wider notion. The term was coined in the 1950s and means the ability of machines to perform tasks typical of humans. Machine Learning is an application of Artificial Intelligence. It consists of feeding programs large amounts of data in order for the programs to learn from them. In such a way, computers can learn without having a pre-programmed algorithm for solving specific problems.
For example, instead of writing complicated programs for recognizing a particular item in a picture, millions of pictures including the item are provided. On such a basis, the program creates a model and learns to recognize the appropriate patterns on its own.
Types Of Machine Learning
Main types of machine learning:
Supervised learning – teach a computer how to do something. This is the most common type of machine learning. It is called ‘supervised learning’ because the answers are known before training. The algorithm learns by predicting the answers to training data which are corrected by the teacher. After the training process, the algorithm knows how to predict new dataset on its own.
– Data prediction,
– Regression – when the answer is a real value, e.g. prediction of average temperature in following months,
– Classification – when the answer is class or category, e.g. prediction of the subject of the article.
Unsupervised learning – computer learns on its own. There is no answers and no teacher. The algorithm has to learn on its own, without a teacher who can supervise it. The goal is to find information about the data and the relationship between individual samples.
– Deep learning, neural networks,
– Clustering – grouping the data, e.g. similar products based on the description,
– Detecting anomalies – finding association rules, e.g. people that buy ProductA also buy ProductB.
Machine Learning approaches
Another popular notion is Deep Learning – one of the many approaches to Machine Learning. Deep Learning was inspired by the way the human brain learns, and in particular how neurons are connected to one another. Artificial Neural Networks are algorithms which mimic the brain’s biological structure. The data which enter the network are transmitted between the layers like impulses in the body. Each layer is able to detect one characteristic, which it learns in order to recognize input data.
What’s the best language for?
When creating Machine Learning-based solutions, it is important to choose the technology for the software. Currently, the most widely-used language for such tasks is Python. Its popularity is due to the fact that it is easy to learn and has numerous libraries with ready-to-use tools.
How can it help your business?
Many business processes, such as defining client segmentation, anti-fraud analysis or creation of prediction models are performed manually. These processes are often time-consuming and related to a large risk of making a mistake. The incredible capabilities of Machine Learning make it possible to use it for automating such processes and solving non-trivial business problems.
Another area Algorithms can be used are various types of virtual assistants, commonly called ChatBots, which are used for direct communication with customers. Such tools, based on Artificial Intelligence and particularly Machine Learning Algorithms, can automatically process text, human speech and images.
Head of Product
12 years of experience in IT. A co-author of the BeeBot chatbot, he has participated in strategic projects for small and large organizations, such as Orange, Polkomtel, PZU or Raiffeisen. Mentor of start-ups, passionate about creating new business models.
During his 9+ years at Bluesoft, he has gone from a Software Developer to Business Development Manager. Since September 2017, he has been working on the Mr Wolf Project as Head of Product.