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Machine learning is a branch of AI technology. It focuses on using data and algorithms to replicate the way human learning is accomplished. The more data and time a machine has, the more it learns and improves in the task it is set out to do.
The term was first coined in 1962 when an IBM programmer created a program of checkers that was able to beat the world champion at that time. While this seems pretty basic compared to the tasks machines can achieve today at the time it was revolutionary, and still considered a monumental achievement.
Machine learning is all around us. It’s how we get recommendations for ads, and new things to watch. Machine learning is the algorithm and it learns more about us as it collects more and more data. The most recent phenomenon in Machine learning technology of course is the wave of AI chatbots, like Chat GPT and Bing.
This technology is a subcategory of Data Sciences, a field that depends heavily on large inputs of data to learn from and advance. It can identify patterns in these large amounts of data and forecast future results.
Machine learning is often confused and lumped into three other subcategories of Machine learning such as; Deep Learning, Neural Networks, and Machine Learning.
Machine learning is a wider term that envelopes both deep learning and neural networks as subcategories, while deep learning is a subclass of neural networks.
Deep Learning
Deep learning and machine learning vary primarily in that with machine learning, we must describe the type of data, how it should be utilized, and which portions of it should be used. In contrast, deep learning can automatically identify the collection of characteristics that separate several types of data from one another after ingesting unstructured material in its raw forms, such as text, voice, or movies. Deep learning is essentially just machine learning with deeper layers of complexity and neural networks used to learn. This reduces the need for some human interaction and makes it possible to handle bigger data sets. Deep learning is basically scalable machine learning.
Three crucial elements make up the core of a standard supervised machine learning algorithm: a decision process, an error function, and an optimization mechanism.
Consider a weather forecasting system that predicts the upcoming weather for the next several days based on inputs like temperature, humidity, and air pressure. These variables are used by the algorithm to identify patterns and give them weights. Every time an algorithm makes a prediction that is closer to correct it remembers the methods it took to arrive at that prediction, if it’s false it forgets those paths. So every trial and error attempt, it learns more and grows to become a better model for accuracy.
Machine learning comes in three tasty flavors: semi-supervised, unsupervised, and supervised.
Supervised Learning
When learning is done under supervision, the user can instruct the computer on how to reproduce an outcome that they are already familiar with. Giving the data examples it expects to receive.
Unsupervised Learning
Unsupervised learning occurs when the machine discovers new hidden patterns and connections between the data on its own without the user having prior knowledge of the patterns.
Semi-Supervised
Semi-supervised learning strikes a balance between the two approaches by providing sufficient data along with a predetermined result while also allowing for the discovery of new connections in the data based on existing ones.
Machine learning uses multiple algorithms to make these predictions, below are only a few of the more popular methods such as Neural Networks, Linear regression, Data Clusters, and Random Forests.
Neural Networks
Neural networks are algorithmic representations of the neural networks found in the human brain. Since they are excellent pattern recognizers, neural networks are valuable in applications like picture and speech recognition. You may have used a speech recognition system like Siri or Alexa, which uses neural networks to read and understand your spoken instructions.
Linear Regression
Linear regression is another approach to predicting numerical values based on a linear connection between multiple values. For instance, suppose you wish to estimate a person’s grocery budget depending on their income. By examining the data and determining the correlation between income and grocery consumption, linear regression can assist you in making that prediction.
Also, there are methods for clustering data that find patterns using data clustering algorithms and put them in groups. This can be helpful for client segmentation or locating groups of people with comparable preferences.
Random forests integrate the outcomes from various decision trees to forecast values or categories, whereas decision trees employ a branching sequence of linked decisions to achieve the same results.
The Impacts
Self-driving cars and IT behemoths are not the only industries being impacted by machine learning’s advancements. Machine learning is already all around us in other areas like farming, insurance, and healthcare are already using machine learning to great effect! For instance, farmers are utilizing machine learning algorithms to maximize crop yields, minimize water use, and protect the environment. Machine learning is used in the insurance sector to identify fraudulent claims, evaluate risks, and customize policies for clients. It’s used every time you use face recognition to open your phone, and the good news is that data is probably being sold, and could eventually be used for police enforcement procedures. (/s)
Ethics
This leads me to the ethical considerations of this fascinating technology For instance, biased data used to train machine learning algorithms may reflect and reinforce already-existing societal inequities. Being aware of this and taking action to reduce prejudice can help to guarantee that machine learning models are just and equal.
In conclusion, machine learning is an important component of artificial intelligence that entails using data and algorithms to mimic how people learn. It won’t go away any time soon, and it’s likely to start getting better at an exponential pace that’s disturbingly rapid. Government intervention to ensure rules are in place to safeguard citizens from the economic catastrophe this technology has the potential to cause has never been more crucial
Resources:
https://www.ibm.com/topics/machine-learning
https://ischoolonline.berkeley.edu/blog/what-is-machine-learning/
https://www.sas.com/en_us/insights/analytics/machine-learning.html