Machine learning has become popular in the modern days with applications in numerous fields like automobile and healthcare with self driving capability and cancer detection ability. There are numerous machine learning algorithms that are used like simple and multiple linear regression, regression trees, KNN, SVM, multiclass prediction, logistic regression, decision trees, and k means. Each algorithm has its differences and has its benefits depending on different scenarios. When you utilize a streaming platform and it makes recommendations to you, they are using machine learning to make those recommendations. They reinforce the program by doing a survey afterwards at times as well to ensure the result is what was expected.
Machine learning is heavily based on data sets. For example, if you want to use machine learning to squash a venomous spider, you will need to feed the machine the data sets related to venomous spiders first, so machine learning can take action. Let’s say you have gathered data sets on numerous spiders, each with different ID numbers and characteristics. ID 1 for example can be a redback spider with its characteristics to include colors, location, size, length of its limbs, the thickness of its limbs, thickness of the hairs, and if the spider is venomous or not. More data sets with different types of spiders and characteristics, the better. With the data, machine learning that is hooked up to a camera or sort can give you a prediction if a never-before seen spider is likely venomous. If it is venomous, it will squash the bug if it’s programmed to do so. A good example is the medical field using machine learning to identify if a certain skin sample is benign or malignant. Depending on what you are dealing with, you can use machine learning to help confirm information or fast-track decision making with high accuracy.
There are different classifications of these techniques. What we talked about is classification technique. But you can use clustering technique, regression and estimation technique, association technique, sequence mining, anomaly detection and so on, which all will be dependent on what project you’ll be dealing with.
AI and ML sounds alike, but think of AI as the bigger field that includes machine learning. AI focuses on making computers more intelligent and that includes computer vision and language processing for example while machine learning is more focused on the statistical part of it. Deep learning is a next level up from machine learning, which uses machine learning and allows computers to make a decision on its own. Python is the most popular language for machine learning due to available libraries.
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