Machine learning: Introduction, Working and Applications
An exciting branch of artificial intelligence, Machine learning is almost used in every field. Machine learning is a technical concept and is difficult to understand. But this article will help the reader in understanding what exactly machine learning is and how it works along with its advantages and disadvantages.
What is Machine Learning?
Before knowing the meaning of machine learning it is worth reading some famous definitions of machine learning that may help you better understand its essence.
According to Stanford University, "Machine learning is the science of getting computers to act without being explicitly programmed."
According to Tom M Mitchell, "Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience."
According to Carnegie Mellon University, "The field of machine learning seeks to answer the question" how can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
According to Nvidia, "Machine learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world."
Machine learning is a process of teaching a computer system how to accurate predictions when fed data. For example, spotting people crossing the road in front of a self-driving car, recognizing the speech accurately, etc. Machine learning models can effectively distinguish between the fruits because they have been trained on a large amount of data. Machine learning algorithms find and apply patterns in data and pretty much run the world. It uses the statistics to find patterns in a huge amount of data. All the recommendation systems you see on Youtube, Netflix, Google, and voice assistants like Siri, Alexa is the result of machine learning.
How does machine learning work?
The machine learning process starts with inputting the training data into the selected algorithm. The type of training data input impacts the algorithm. Then to test whether the algorithm works correctly, new input data is fed into the machine learning algorithm and the prediction is then checked. If the prediction is not as desired, the algorithm is retrained multiple times until the desired output is formed. This lets the machine learning algorithm continually learn on its own and produce the most optimal answer that will increase in accuracy over time.
Machine learning approaches
Various approaches to machine learning are as follows:
1. Supervised machine learning algorithms
In this type of algorithm, a mathematical model of a set of data is build that contains both the inputs and the desired outputs. It means that the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. It helps to optimize performance criteria with the help of experience. Supervised machine learning solves various types of real-world computation problems. Bioinformatics (Biological Information) is one of the most well-known applications of supervised learning. Another example of supervised learning is text classification problems. There are two types of supervised learning techniques which are regression and classification. Classification separates the data whereas regression fits the data.
2. Unsupervised machine learning algorithms
Unsupervised machine learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The unsupervised model, unlike the supervised model, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns of its own. It looks for previously undetected patterns in a data set with no pre-existing labels and with minimum human supervision. It is known as unsupervised because the algorithm is not guided like supervised learning algorithm. This type of machine learning algorithm is used to draw inferences from datasets consisting of input data without labeled responses. Cluster analysis is the most common unsupervised learning method which is used for exploratory data analysis to find hidden patterns or grouping in data. This approach is also used for finding fraudulent transactions.
3. Semi-supervised machine learning algorithms
A combination of supervised and unsupervised machine learning algorithms is semi-supervised learning. In semi-supervised learning, an algorithm learns from a dataset that includes both labeled and unlabeled data, usually, it uses a small amount of labeled data and a larger set of unlabeled data. Speech analysis is a classic example of semi-supervised learning models.
4. Reinforcement machine learning algorithms
In the reinforcement learning method, the agent receives a delayed reward in the next time step to evaluate its previous action. It trains an algorithm with a reward system, providing feedback when an artificial intelligence agent performs the best action in a particular situation. It was mostly used in games such as Mario, Atari with performance on par with or even exceeding humans.
Applications of machine learning
Machine learning is used in almost every field today. Some of them are given below.
- Computer networks
- Credit card fraud detection
- Data quality
- Financial market analysis
- Handwriting recognition
- Internet fraud detection
- Machine translation
- Natural language processing
- Online advertising
- Robot locomotion
- Search engines
- Sequence mining
- Software engineering
- Speech recognition
- User behavior analytics
Advantages of machine learning
Various advantages of machine learning are as follows:
1. No human intervention needed
Machine learning gives machines the ability to learn and this helps them make predictions and also improve the algorithms on their own. For example, Antivirus software, they learn to filter new threats as they are recognized.
2. Wide application
Machine learning helps to deliver a much more personal experience to customers and also targets the right customers. It is applicable in almost every field such as advertising, banking, marketing, telecommunication, etc.
3. It easily identifies trends and patterns
Machine learning can review large volumes of data and can easily discover specific trends and patterns that would not be apparent to humans. For example, Amazon understands the browsing behaviors and purchase histories of its customers to help cater to the right products and reminders relevant to them.
4. Continuous improvement
All the algorithms with time gain experience and keep on improving their accuracy and efficiency. Machine learning deals with the ever-increasing amounts of data and this can be used for better decisions or predictions in the future.
Disadvantages of machine learning
Various disadvantages of machine learning are as follows:
1. It requires time and resources to bring results
Machine learning algorithms require time to develop enough for your needs. Handling tremendous volumes of data and running computer models uses a lot of computing power which can be quite costly.
2. Interpretation of results
All the approaches are powerless unless they can be interpreted. The output must be checked for any errors and the correction operation should be followed to get the desired accuracy. Also, the algorithm should be carefully selected according to the required purpose.
3. Data acquisition
The data acquisition process comes with a massive disadvantage. Collecting data from surveys may contain a large volume of incorrect data. Sometimes an imbalance in data leads to poor accuracy of models.
4. Highly error-prone
The training data must be clean and accurate. It sometimes happens that data might be improper. This results in incorrect results or errors.