What is Machine Learning (ML)?
Machine Learning (ML) is a subset of Artificial Intelligence (AI). Mainly, it mixes AI with Computer Science. To do so, it uses algorithms and structural models to analyze data patterns. As a result, it imitates the way humans learn. In short, ML gives computers the ability to learn by mimicking human behavior. Rhe concept of Machine Learning has been around for quite a while. Yet, only in the last couple of decades has it got the chance to be fully exploited. This possibility arises due to having more data and better computers and algorithms.
Machine Learning (ML) vs. Artificial Intelligence (AI)
Are ML and AI the same? Yes and no. Machine Learning is a subfield or application of Artificial Intelligence. In turn, ML is part of AI. The notion behind AI is that computers can learn with existing decision-making examples. As a result, we can build intelligent machines that can perform human tasks. Since AI is quite broad, it's divided into many subfields. In addition, these may also have their own subfields. Some Artificial Intelligence subfields are speech processing, fuzzy language, neural networks, and robotics. And, of course, Machine Learning.
What sets Machine Learning apart from AI is that ML can evolve. AI aims to get computers to simulate human behavior. Meanwhile, ML focuses on computers learning automatically without previous programming. If you want to learn more about AI in Software Development, check our article about it!
Machine Learning vs. Deep Learning
Like ML is a subfield of AI, Deep Learning (DL) is a sub-field of Machine Learning. Deep Learning's basis is artificial neural networks. These networks share the same principles as biological ones. Consequently, they learn to perform tasks based on datasets and examples. Here, neural networks need more than three processing layers to extract higher-level features. Although ML and DL have vast connections, we can still point out some differences.
● Machine Learning requires ongoing human intervention, which is minimal in Deep Learning.
● Algorithms of Machine Learning are less complex and can run on conventional computers. Deep Learning algorithms need more powerful hardware and resources, including a dedicated GPU.
● Machine Learning systems take less time to set up but are less effective. Meanwhile, Deep Learning takes longer to set up but provides instantaneous results.
● Machine Learning comprises thousands of data points, while Deep Learning consists of millions. Thus, DL relies heavily on Big Data.
How Does Machine Learning (ML) Work?
Machine Learning consists of three main elements. These edges are computational algorithms, variables and features, and the base training data. The training data, labeled or unlabeled, is introduced into the algorithm. Then, new data is fed to the algorithm to check if it works as intended. Finally, the prediction and the results get checked to ensure a match. If there isn’t a match, the algorithm will need retraining.
What are the Types of Machine Learning (ML)?
Depending on who you ask, there are three or four Machine Learning types (or models). Supervised, unsupervised, and reinforcement learning are the three core ones. Some experts add semi-supervised learning to the mix. Let’s take a look at all four below.
1. Supervised Learning
In this Machine Learning model, machines learn by example. The process means that algorithms have historical, labeled data. These labels help the machine understand the relation between two data points. This type of ML is often used to predict future events from previously experienced data. Some of its algorithms are Decision Trees and Linear Regression.
2. Unsupervised Learning
This type of Machine Learning can work with unlabeled data. Thus, it does not need human intervention. However, since the data is not labeled, the output may only sometimes be correct. Here, algorithms identify patterns in the data by studying that data. Then, this ML algorithm will attempt to organize data to create hidden structures. Unsupervised Learning focuses on building Predictive Models. Some algorithms used in Unsupervised Learning include k-means and Gaussian Mixture Models.
3. Semi-Supervised Learning
Semi-Supervised Learning is a mid-point between supervised and unsupervised learning. In this scenario, algorithms have both labeled and unlabeled data. In most cases, although, most data is unlabeled.
4. Reinforcement Learning
Reinforcement Learning mimics how humans learn. Here, the algorithm improves and learns using a trial-and-error model. It receives reinforcement for favorable outcomes and is “punished” for unfavorable ones. A reinforcement agent AI is in charge of rewarding and punishing the algorithm. Temporal Difference and Q-learning are examples of reinforcement learning algorithms.
8 Top Machine Learning Algorithms
In this section, we’ll look at the eight most common Machine Learning (ML)algorithms and what they are used for.
1. Linear Regression. This algorithm is used for predictive analysis. It represents the relationship between two or more variables. Linear Regression is functional, for example, to evaluate business trends and forecasts.
2. Logistic Regression. This algorithm estimates discrete variables from a given set of independent variables. For instance, it's helpful when predicting the likelihood of patients developing medical diseases.
3. K Nearest Neighbor. KNN (K Nearest Neighbor) solves regression or classification problems. Fun fact, it's also known as the lazy learner algorithm. This name is because it doesn't learn from given training sets. Instead, it stores it and only performs an action when it's time to classify the data. Among its uses are medical diagnosis, gaming, and credit score checking.
4. Decision Trees. Like KNN, a Decision Tree algorithm solves classification and regression problems. It consists of a classifier that has a tree structure. Here, internal nodes represent attributes, while branches represent decisions. Finally, the outcomes are in their leaves. Decision Trees are primarily used in marketing, customer retention, and fraud detection. Yet it also applies to medical diagnosis.
5. Random Forest. This is another algorithm used to solve regression and classification problems. Yet, it's beneficial for solving complex problems or improving model performances. A Random Forest has several decision trees and subsets of the given dataset. This algorithm is used in eCommerce, finance, and healthcare.
6. K-Means Clustering. This is the only item on this list that belongs to Unsupervised Learning. K-Means Clustering solves, well, clustering problems. It does so by grouping unlabeled datasets into different clusters. Some of its uses are fraud detection, customer segmentation, and identifying crime-prone areas.
7. Naïve Bayes. This algorithm comes from the Bayes Theorem that solves classification problems. ML models built with it make quick predictions with better accuracy and performance. Naïve Bayes is used for credit scoring and medical data classification.
8. Support Vector Machines. SVM algorithms create the best decision boundary to segregate n-dimensional space in classes. In general, Support Vector Machines algorithms apply to classification problems. But they also serve to solve regression problems. Some of its applications include image classification, handwriting recognition, and face detection.
What can Machine Learning (ML) do?
Machine Learning has countless potential uses. We couldn’t cover them all, but we’ll give you a handful of examples. The truth is that Machine Learning can apply in pretty much every industry. This list includes manufacturing, retail, banking, cybersecurity, customer service, and sales. But also healthcare and transportation. Popular ML uses include Predictive Analysis, Recommendation Engines, and Speech Recognition. Also, there are image and video processing and Sentiment Analysis. Below, we’ll go over some real-life Machine Learning examples.
1. Image and Speech Recognition. These two Machine Learning applications are quite famous and widespread today. Image Recognition usage encompasses labeling x-rays, tagging photos, and recognizing handwriting. Meanwhile, Speech Recognition includes control appliances, voice dialing, and voice search.
2. Self-Driving Cars. Tesla and Tata are only two of the companies working toward making self-driving cars a reality for all of us. Vehicles get trained to detect people and objects while driving through supervised ML.
3. Medical Diagnosis. Doctors can analyze patient data through Machine Learning to help make diagnoses. Machine Learning is even used in oncology to detect cancerous tissue!
4. Traffic Prediction. Machine Learning can predict the shortest route to your destination. As well it can tell whether there is any traffic congestion. This is all thanks to the Google Maps app and sensor.
5. Automatic Translation. Automatic or Machine Translation is another popular application of Machine Learning. The most known example, Google Translate, has evolved quite a bit over the last few years. Although mostly accompanied by human review, it's excellent for the demand increments in translation.
6. Product Recommendations. You are probably familiar with Netflix and Spotify’s recommendation features. We have these thanks to Machine Learning!
7. Virtual Assistants. You might also be familiar with Siri and Google Assistants. They can play music, call people, or search for things on the internet through voice commands.
8. Spam Filtering. Machine Learning is also behind spam filtering for your email account. The classification that Gmail now boasts (Main, Promotions, and Social) also fall here.
Machine Learning (ML)Limitations
Like any new technology, Machine Learning has issues and has raised some concerns. Here are some of the most relevant ones.
Ethical Concerns
Algorithms are developed and trained by humans. Humans are often biased. Thus, it's virtually impossible to remove all bias from algorithms. Further, these biases can influence Machine Learning results. So, avoiding prejudices and ensuing ethical issues is critical. As a result, ventures need to invest in control frameworks and techniques to identify bias in ML.
Moreover, Machine Learning is all about data. This aspect has some experts wondering. Are we relying too much on data and leaving our judgment and logic aside? Of course, this leads us to the question of who is to blame if something goes wrong. Let's take a self-driving car as an example. Would we blame the driver, the manufacturer, or the developer in case of a traffic accident?
Lack of (Good) Data
Again, ML, particularly Neural Networks, takes a colossal amount of training data, particularly Neural Networks. As ML grows, it requires more and more data. Increasing the fed data for Machine Learning systems can be either costly or illegal. Since we have limited data and it's not advisable to reuse it, there might be data shortages.
Furthermore, the quality of Machine Learning systems depends on the data fed to them. Due to this, it's essential to consider what data is actually provided. Using "bad" or low-quality data can result in highly biased systems.
Deterministic Problems
Machine Learning algorithms can process data to make specific predictions. Yet, these can't understand things like physics laws or weather systems. Since Machine Learning doesn't recognize cause-and-effect relations, it's unsuitable for weather forecasting. While ML can connect two pieces of data, it doesn't know where the connection lies or why it exists. Many researchers are working on a way to bypass this. For now, ML being stochastic and not deterministic is a considerable limitation.
Why is Machine Learning (ML) important?
Machine Learning has a myriad of applications that serve not only businesses but each one of us. Further, ML helps automate tasks in a way that frees up our time so we can devote it to more critical tasks. Not to mention it is helping improve our quality of life.
ML is already improving cancer treatments and helping with remote patient monitoring. This is on top of its many businesses uses. These venture applications include care tasks automation, cross-channel marketing, and predictive maintenance. On top of everything, Machine Learning has the potential to take over dangerous jobs. These risk works encompass checking radiation levels or handling hazardous chemicals. In this context, another tremendous potential application is identifying environmental damage.
The truth is that Machine Learning is already making a difference in the world. Moreover, it could make a considerably larger one in the short term.
Conclusion
Machine Learning has made strides over the last decade alone. This subfield of AI has outstanding potential, and we can’t wait to see how it evolves! We hope this article gave you a comprehensive overview of ML and got you as excited for its future as we are!
