Machine learning is a subfield of computer science concerned with building algorithms that rely on a collection of examples of some phenomenon to be useful. Artificial intelligence includes machine learning.
Machine learning can also be defined as solving a practical problem by 1) gathering a dataset and 2) algorithmically building a statistical model based on that dataset. That statistical model is assumed to be used to solve the practical problem.
In this article, you will learn What is Machine Learning for Dummies, as well as the types of Machine Learning, what is Machine learning used for, and what are different Machine Learning algorithms? etc. You will learn the same as the “What is machine learning GeeksforGeeks” article teaches you. Continue reading to get the answers to all of your queries!
What is Machine Learning?
Machine Learning is the science (and art) of programming computers so they can learn from data.
Here is a slightly more general definition:
Arthur Samuel states, “Machine Learning is the field of study that allows computers to learn without being explicitly programmed.”
And a more engineering-oriented one:
According to Tom Mitchell, “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.”
For example, your spam filter is a Machine Learning algorithm that can learn to recognize spam based on samples of spam emails (e.g., marked by users) and samples of normal (nonspam, sometimes known as “ham”) emails. The models that the system employs to learn are referred to as the training set. Each training example is known as a training instance (or sample). In this situation, task T is to detect spam in new emails, experience E is the training data, and the performance measure P must be defined; for example, the ratio of correctly classified emails can be used. This type of performance metric is known as accuracy, and it is frequently employed in classification jobs.
What is the difference between Artificial Intelligence and Machine Learning?
Artificial Intelligence and Machine Learning are correlated with each other, and yet they have some differences.
| Artificial Intelligence | Machine Learning |
|---|---|
| AI stands for Artificial intelligence, where intelligence is defined as the ability to acquire and apply knowledge. | ML stands for Machine Learning which is defined as the acquisition of knowledge or skill |
| AI is the broader family consisting of ML and DL as its components. | Machine Learning is the subset of Artificial Intelligence. |
| The aim is to increase the chance of success and not accuracy. | The aim is to increase accuracy, but it does not care about; the success |
| AI is decision-making. | ML allows systems to learn new things from data. |
| It is developing a system that mimics humans to solve problems. | It involves creating self-learning algorithms. |
How does Machine Learning Work?
Machine learning is the brain where all the learning takes place. The way the machine learns is similar to the human being. Humans learn from experience. The more we know, the more easily we can predict. By analogy, when we face an unknown situation, the likelihood of success is lower than the known situation. Machines are trained the same. To make an accurate prediction, the machine sees an example. When we give the machine a similar example, it can figure out the outcome. However, like a human, if it feeds a previously unseen example, the machine has difficulties predicting.
The core objective of machine learning is learning and inference. First of all, the machine learns through the discovery of patterns. This discovery is made thanks to the data. One crucial part of the data scientist is to choose carefully which data to provide to the machine. The list of attributes used to solve a problem is called a feature vector. You can think of a feature vector as a subset of data that is used to tackle a problem.
The machine uses some fancy algorithms to simplify reality and transform this discovery into a model. Therefore, the learning stage is used to describe the data and summarize it into a model.
For instance, the machine is trying to understand the relationship between the wage of an individual and the likelihood to go to a fancy restaurant. It turns out the machine finds a positive relationship between wage and going to a high-end restaurant: This is the model.
What is Machine Learning Used for?
Machine Learning is used in almost all modern technologies and this is only going to increase in the future. In fact, there are applications of Machine Learning in various fields ranging from smartphone technology to healthcare to social media, and so on.
Smartphones use personal voice assistants like Siri, Alexa, Cortana, etc. These personal assistants are an example of ML-based speech recognition that uses Natural Language Processing to interact with the users and formulate a response accordingly. Machine Learning is also used in social media. Let’s take Facebook’s ‘People you may know’ as an example. It is mind-boggling how social media platforms can guess the people you might be familiar with in real life. And they are right most of the time!!! This is done by using Machine Learning algorithms that analyze your profile, your interests, your current friends, and also their friends and various other factors to calculate the people you might potentially know.
Machine Learning is also very important in healthcare diagnosis as it can be used to diagnose a variety of problems in the medical field. For example, Machine Learning is used in oncology to train algorithms that can identify cancerous tissue at the microscopic level with the same accuracy as trained physicians. Another famous application of Machine Learning is Google Maps. The Google Maps algorithm automatically picks the best route from one point to another by relying on the projections of different timeframes and keeping in mind various factors like traffic jams, roadblocks, etc. In this way, you can see that the applications of Machine Learning are limitless. If anything, they are only increasing and Machine Learning may one day be used in almost all fields of study!
Why is Machine Learning Important?
Machine learning is the best tool so far to analyze, understand and identify a pattern in the data. One of the main ideas behind machine learning is that the computer can be trained to automate tasks that would be exhaustive or impossible for a human being. The clear breach from the traditional analysis is that machine learning can take decisions with minimal human intervention.
Take the following example for this ML tutorial; a retail agent can estimate the price of a house based on his own experience and his knowledge of the market.
A machine can be trained to translate the knowledge of an expert into features. The features are all the characteristics of a house, neighbourhood, economic environment, etc. that make the price difference. For the expert, it took him probably some years to master the art of estimating the price of a house. His expertise is getting better and better after each sale.
For the machine, it takes millions of data, (i.e., an example) to master this art. At the very beginning of its learning, the machine makes a mistake, somehow like the junior salesman. Once the machine sees all the examples, it got enough knowledge to make its estimation. At the same time, with incredible accuracy. The machine is also able to adjust its mistake accordingly.
Most of the big companies have understood the value of machine learning and holding data. McKinsey has estimated that the value of analytics ranges from $9.5 trillion to $15.4 trillion while $5 to 7 trillion can be attributed to the most advanced AI techniques.
Types of Machine Learning
At a broad level, machine learning can be classified into three types:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised Learning
Supervised learning, as the name indicates, has the presence of a supervisor as a teacher.
Supervised learning is a type of machine learning method in which we provide sample labelled data to the machine learning system in order to train it, and on that basis, it predicts the output.
The system creates a model using labelled data to understand the datasets and learn about each data, once the training and processing are done then we test the model by providing sample data to check whether it is predicting the exact output or not.
Supervised learning can be grouped further into two categories of algorithms:
- Classification
- Regression
Unsupervised Learning
Unsupervised learning is the training of a machine using information that is neither classified nor labelled and allowing the algorithm to act on that information without guidance. Here the task of the machine is to group unsorted information according to similarities, patterns, and differences without any prior training of data.
In unsupervised learning, we don’t have a predetermined result. The machine tries to find useful insights from a huge amount of data. It can be further classifieds into two categories of algorithms:
- Clustering
- Association
Reinforcement Learning
Reinforcement learning is a feedback-based learning method, in which a learning agent gets a reward for each right action and gets a penalty for each wrong action. The agent learns automatically with this feedback and improves its performance. In reinforcement learning, the agent interacts with the environment and explores it. The goal of an agent is to get the most reward points, and hence, it improves its performance.
Advantages of ML
There are several advantages of using machine learning, including:
- Improved accuracy: Machine learning algorithms can analyze large amounts of data and identify patterns that may not be apparent to humans. This can lead to more accurate predictions and decisions.
- Automation: Machine learning models can automate tasks that would otherwise be done by humans, freeing up time and resources.
- Real-time performance: Machine learning models can analyze data in real time, allowing for quick decision-making.
- Scalability: Machine learning models can be easily scaled up or down to handle changes in the amount of data.
- Cost-effectiveness: Machine learning can reduce the need for human labour, which can lead to cost savings over time.
- Ability to learn from experience: Machine learning models can improve over time as they are exposed to more data, which enables them to learn from their mistakes and improve their performance.
- Better predictions: Machine learning models can make predictions with greater accuracy than traditional statistical models.
- Predictive Maintenance: Machine learning models can help identify patterns in sensor data that are indicative of equipment failure, allowing for preventative maintenance to be scheduled before an issue occurs.
Disadvantages of ML
While there are many advantages to using machine learning, there are also some potential disadvantages to consider, including:
- Complexity: Machine learning algorithms can be complex and difficult to understand, which can make it difficult for non-experts to use or interpret the results.
- Data requirements: Machine learning algorithms require large amounts of data to train and be accurate, which can be difficult to collect and preprocess.
- Biased data: Machine learning models are only as good as the data they are trained on, and if the data is biased, the model will also be biased.
- Overfitting: Machine learning algorithms can overfit the training data, which means they will not perform well on new, unseen data.
- Limited interpretability: Some machine learning models, particularly deep learning models, can be difficult to interpret, making it hard to understand how they reached a particular decision.
- Lack of transparency: Some machine learning models are considered black boxes, meaning it is difficult or impossible to understand how they arrived at a particular decision.
- Privacy concerns: Machine learning models can process sensitive data that could be used to discriminate or make privacy-intrusive decisions if not used responsibly.
- Requirements of experts: Machine learning requires experts such as data scientists, engineers, and statisticians who can develop, train and deploy models which can be costly.
Which Cloud Computing Platforms Offer Machine Learning?
There are many Cloud Computing Platforms that offer Machine Learning services to other companies. The most popular among them are:
1. Amazon Web Services (AWS)
Some of the products that Amazon Web Services provides include Amazon SageMaker for creating and training machine learning models, Amazon Forecast to increase forecast accuracy, Amazon Translate for language translation using natural language processing, Amazon Polly for converting text into life-like speech, etc.
2. Microsoft Azure
Some of the products that Microsoft Azure provides include Microsoft Azure Machine Learning for creating and deploying machine learning models, Cognitive Service for providing smart cognitive services, Databricks for Apache Spark-based analytics, Bot Service for smart and intelligent bot services, etc.
3. Google Cloud
Some of the products that Google Cloud provides include Google Cloud AutoML for training an AutoML machine learning model, Vision AI for cloud vision, Speech-to-Text for transmitting from speech to text, Text-to-Speech for transmitting from text to speech, Natural Language for natural language processing, etc.
4. IBM Watson Cloud
Some of the products that IBM Watson Cloud provides include IBM Watson Studio for building machine learning and artificial intelligence models, Speech-to-Text for transmitting from speech to text, Text-to-Speech for transmitting from text to speech, Assistant for creating and managing virtual assistants, Natural Language Understanding for natural language processing, etc.
