Part 1 of a 2-part series
In recent years, machine learning is gaining more and more popularity, but what exactly is the Machine Learning? In this section, I will deep-dive so you will have a better understanding of what machine learning is, types and how machine learning is used.
Evolution of Machine Learning
The name “Machine Learning” initially originated from famous gaming researcher Arthur Lee Samuel. Samuel is the first person to bring self-learning programs into society. This remarkable discovery shortly laid the foundation for Machine Learning algorithms. In later years, raising popularity in Machine Learning (ML) and Artificial Intelligence (AI) give birth to many innovations in the field of Computers and Automation. Similar definitions and usage of ML & AI created ambiguity in distinguishing these two fields. In fact, few beginners in this field often use AI and Machine Learning interchangeably, but the fact is that they are the same.
Artificial Intelligence is the integration of Machine Learning algorithms. Artificial Intelligence models are used to perform multiple tasks such as self-driving cars, humanoid robots, etc.
On the other hand, Machine Learning is used to accomplish only specific tasks like spam detection, Movie recommendation, and Image classification.
Actually, Machine Learning is a sub-field of AI, the picture below clearly explains what I mean.
Machine Learning is Broadly Segmented into 3 Types
- Supervised Machine Learning
- Unsupervised Machine Learning
- Reinforcement Learning
Supervised Machine Learning
Supervised machine learning is the most commonly used technique. Many industries use supervised machine learning techniques to train machine learning algorithms.
In supervised Machine Learning, we supervise or teach the machine using labeled data. In other words, we show the sample data and tell the machine what the label is, likewise we do it for every sample in the data set.
Figure 1 explains the working of supervised Machine Learning.
In figure 1 Dataset consists of ‘n’ Labelled cat and dog images, each image is labeled with a tag. For instance, in figure 1 Image 1 is labeled as a cat. Likewise, there will be ‘n’ labeled images from 1 to n.
In supervised learning, the teacher holds the actual values for every corresponding image in the dataset. Similarly, the learning system will give predicted values for every corresponding image in the dataset. Once we got the image output values from the teacher and learning system error function will calculate the error between actual and predicted values.
Using the feedback error, the learning system will keep on updating its parameters (weights) to minimize the error value. Eventually, this process of learning parameters (weights) will help the Learning system to understand the model.
Visit the DELMIA Blog again for Part 2 which will include details on Unsupervised Machine Learning and Reinforcement Learning. Meanwhile, you can connect with our DELMIA experts in the DELMIA Fabrication community for free. https://r1132100503382-eu1-3dswym.3dexperience.3ds.com/#community:45
