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A subset of artificial intelligence (AI) is machine learning (ML) enables computers to “self-learn” from training data and get better over time without having to be specifically programmed.   Detecting patterns in data and learning from them allows machine learning algorithms to develop their own predictions. In short, algorithms and models for machine learning gain knowledge through experience.

Machine learning shouldn’t be viewed as a conditional statement, where a specific consequence results if this is done or a different one if that isn’t the case. In contrast, machine learning is an autonomous process that allows computers to solve issues with little to no human interference and to execute actions based on prior observations. It is much more than that.

Although the terms “artificial intelligence” and “machine learning” are frequently used synonymously, they are actually two distinct ideas. AI is a broader notion that encompasses robots that make decisions akin to those made by humans, gain new abilities, and make decisions by analyzing the situation and taking into account various factors or scenarios. For instance, the humanoid robot Sophia considers its emotions and cognition when making decisions. Definitely, there’s much more to AI than just robots.  While machine learning refers to expert machines that learn new data on their own.

As we move further into this blog, let’s take a look at a few of these machine-learning ideas and their applications in solving real-world problems.

Many different issues can be resolved with machine learning, including regression analysis, classification, clustering, forecasting, and many more.

In general, machine learning may be classified into three categories: supervised learning, unsupervised learning, and reinforcement learning. We will shortly dig into each of these in more detail.

• Supervised machine learning
• Unsupervised machine learning
• Semi-supervised machine learning
• Reinforcement learning

Supervised learning

Supervised machine learning is a form of machine learning in which the algorithms are fed with a labeled training dataset, and the data scientists need to define the variables in the algorithm to assess the correlations. Both the input and output of the algorithm need to be specified in the supervised machine learning model. Labeled data is a type of data that is already tagged with the correct output. A supervised machine learning model can be thought of as a model which is trained by a supervisor and trains the model to predict the output correctly. It is similar to the concept of a student learning under the supervision of their teacher.

The motive of the supervised machine learning model is to map the input variable with the output variable by finding a mapping function that involves a process of providing input data along with the correct output data to the model. The supervised machine learning model can be implemented to deal with real-world tasks such as risk assessment, spam filtering, fraud detection, image classification, etc.

How does supervised machine learning work?

Since now we know what supervised machine learning is and what its requirements are, let us now discuss how it works. The models in a supervised machine learning technique are trained using a labeled dataset, and the models learn about the data through the inputs and outputs provided in the dataset. Once training of the models is done, testing is performed based on the testing data where the output is predicted by the model. Let us understand the working of the supervised machine learning model through an example.

Let us suppose that we have a dataset that consists of images of dogs, cats, and foxes, and the dataset is labeled, meaning that the names of the animals are mentioned as per the image. The first step in the supervised machine learning model is to train the model for each animal type based on their physical features, color, and how they look.

Once the training is done, we need to test the model using the test data set where the task of the model is to identify the type of animal. In this dataset, the output is not provided, and the model will be evaluated based on the number of correct predictions that it makes. Finally, the model will classify the animals into three types and predicts the output.

Steps to execute supervised machine learning:

• Determine the type or subject of the training dataset and collect the labeled dataset based on the requirements.
• Split the dataset into two or three parts training dataset, testing dataset, and validation dataset.
• Determine the input features using the training dataset that has enough knowledge required by the model for making the prediction.
• Decide a suitable model for modeling, such as a decision tree, random forest, logistic regression, etc.
• Train the model using the training dataset, where the validation dataset may be used for controlling parameters.
• Finally, evaluate the model by using the evaluation parameters and the test dataset to know how accurately the model performs.

Examples of supervised machine learning

The supervised learning model can further be classified into two types based on the type of problems that need to be handled:

1. Classification: Classification models are those algorithms that work by classifying one type of data from the other and are used when the output variable is of a categorical type. Categorical form means that the output consists of only two classes, yes-no, true-false, etc. Some of the classification algorithms used widely are:
   • Decision trees
   • Random forest
   • Support vector machines
   • Logistic regression

• Regression: regression algorithms are implemented when we can find a relationship between the input and the output variable and are used for the prediction of continuous variables like market trends, weather forecasting, etc. Some of the regression algorithms which lie in the supervised learning area are:
  • Regression trees
  • Linear regression
  • Polynomial regression

Supervised machine learning algorithms are suitable for application based on the following tasks:

• Regression modeling: It predicts the continuous values.
• Ensemble modeling: It combines several machine learning models and their predictions to obtain a much more accurate prediction.
• Binary classification: It divides the data into two categories.
• Multi-class classification: it works by choosing between more than two types of answers.

Unsupervised machine learning

In the supervised machine learning models, we discussed that labeled data is required to make the prediction, but there are several fields in which labeled data is hard to obtain. So what do we have to implement in this condition? The supervised machine learning models fail in this situation. To deal with this kind of situation, we have unsupervised machine learning techniques.

Unsupervised machine learning is a machine learning technique in which the models are not being supervised or guided using the training dataset as the dataset is not labeled. To deal with this situation where the dataset is not labeled, the models find insights and hidden patterns from the given data, similar to how thinking and pattern matching happen in the human brain. Unsupervised learning can be thought of as a technique in which the models are trained using an unlabelled dataset and allowed to act without any guidance or supervision.

Where to implement the unsupervised learning technique?

Some of the main reasons to implement unsupervised learning are:

• It works on unlabelled data and data which is categorized.
• It is similar to how humans think and learns through their own experience.
• It is used for finding insights from the dataset.
• Sometimes the dataset may not have input, so we need unsupervised learning to perform the learning.

How does unsupervised machine learning work?

Supervised machine learning works on the unlabelled dataset, which is not categorized, and the output is not present for the corresponding inputs. When the unlabelled data is fed to the machine learning model, it will interpret the data and find the hidden patterns and apply algorithms suitable for unsupervised learning. Finally, the data is processed, and the data is divided into various parts as per the category of particular data.

Suppose that we have raw data that is unlabelled, consisting of images of dogs and cats which needs to be separated from each other. Here we implement the unsupervised learning technique, which will interpret the data, apply the algorithms, and processes the dataset to obtain the categorized data that contains two groups with images of cats and dogs separated from each other.

Examples of unsupervised machine learning

Unsupervised machine learning can be further categorized based on how it performs the operations into two types:

1. Clustering: It is a technique in which the objects are grouped into clusters. The objects which are similar to each other are placed in one cluster, and the objects with no or fewer similarities are placed in other clusters.
2. Association: It works by finding the relationship between variables in a huge database and determines those sets of items that are occurring together in the dataset. It can be implemented in marketing strategy to make it more effective.

Some of the well-known unsupervised machine learning algorithms are:

• K means clustering
• Neural networks
• Anomaly detection
• Hierarchical clustering
• Apriori algorithm

Semi-supervised machine learning

As we have discussed how the supervised and unsupervised machine learning techniques work, let us see one more technique that is based on the combination of both these techniques. It is called the semi-supervised learning technique, which uses the combination of labeled and unlabelled datasets as a part of the training period.

Semi-supervised learning has been introduced to cope with the disadvantages of supervised and unsupervised learning techniques. Supervised learning requires labeling of data which is done by ML specialists by hand and also requires high processing cost, whereas unsupervised learning has a limited spectrum of applications. Therefore, semi-supervised learning can work with both labeled and unlabelled data, which covers the disadvantages of both these techniques.

Assumptions to be followed while working with semi-supervised learning technique

The semi-supervised learning technique works mostly with the unlabelled dataset; therefore, it requires a relationship between objects which can be understood using the following assumptions:

• Continuity assumption: This assumption tells that the objects nearby each other tend to share the same group or label. In this assumption, the decision boundaries will get added using the smoothness assumption.
• Manifold assumption: In this assumption, the data lies on a manifold with fewer dimensions as compared to the input space and helps us in using the distances and densities.
• Cluster assumption: The data in this assumption are divided into different discrete clusters, and the points in the same cluster will share the output label.
• The process with less degree of freedom will create dimensional data, which may be hard to model directly.

How does semi-supervised machine learning work?

The pseudo-labeling method is used by semi-supervised learning to train the model and combines various training ways along with the neural networks. The working of semi-supervised learning can be explained using the following steps:

• The model is trained with less amount of labeled training data, and the training continues until accurate results are obtained.
• In the next step, the model uses unlabelled data with pseudo labels, which may produce results that are not accurate.
• The linking of labeled training data and pseudo-label data is done.
• The input data present in labeled and unlabelled training data is linked.
• Finally, the model has been trained again with new combined input similar to that of the first step, which reduces the errors and improves the accuracy of the model.

Reinforcement learning

Reinforcement learning is a form of machine learning technique based on a feedback mechanism in which the agent learns to behave in an environment by performing certain actions and looking at the results of the performed action. For each good action performed by the agent, it receives positive feedback or positive reward points and performing each bad action or wrong move results in negative feedback or a penalty to the agent. The agent in reinforcement learning learns automatically using unlabelled data, and therefore, it is bound to learn through experience, similar to humans.

Reinforcement learning can be implemented to solve problems where decision-making is required, and a long-term goal is present such as in robotics, game playing, etc. The primary goal of the agent is to gain experience by exploring the environment all by itself. The agent performs several actions in reinforcement learning, such as taking action, changing state, remaining in the same state, and getting the reward or feedback.

How does reinforcement learning work?

To understand the working of reinforcement learning, we need to understand a few terms used in reinforcement learning:

• Agent(): The entity which explores and acts to get the rewards.
• Environment(): The situation being faced or explored by the agent.
• Action(): Moves implemented by the agent in the environment.
• State(): Situation returned by environment once the agent makes any move.
• Reward(): The feedback that is returned to the agent.
• Policy(): The strategy applied by the agent to explore the environment.

Some of the key features of reinforcement learning which need to be understood before understanding how it works are:

• RL is based on the hit-and-trial method.
• The agent does not have any instructions specific to the environment and what actions need to be performed.
• The agent may receive the reward at a later point in time.
• The environment is stochastic, and to get maximum rewards, the agent must explore the whole environment.
• The agent performs the next action on the basis of the rewards that it gets in the previous state.

There are three approaches through which reinforcement learning can be implemented:

1. Policy-based approach: It is used for getting the optimal policy for obtaining the maximum rewards without the value function. It consists of two types of approaches which are deterministic and stochastic.
2. Model-based approach: In this approach, a virtual model is created, and the agent explores the environment to learn from it. The model representation for each environment varies in this approach.
3. Value-based approach: This approach is all about finding the optimal value function, which is the maximum value at a state under any policy. Thus, the agent expects a long-term return at the states.

Examples of reinforcement learning

Some of the most popularly known reinforcement learning algorithms are:

1. Q-Learning: It is an RL algorithm used for temporal difference learning and is an off-policy algorithm used for comparing temporally successive predictions.
2. State action reward stares action (SARSA): It is a temporal difference learning method based on the on-policy method, which selects the action of each state while using a specific policy.
3. Deep Q neural network (DQN): It is a Q-Learning model which is built using neural networks and is implemented for a big state space environment, which can be a challenging task to define and update a Q-table.

Who is using Machine learning?

Machine learning has a wide range of applications and is implemented in a lot of areas to provide assistance where a large human force is required to perform the task. Machine learning implementation can make these tasks easier as it performs the actions of a recommendation engine to power several businesses and firms. Some of the common applications of machine learning are presented here:

• Business Intelligence: Machine learning is very common and most popularly used for boosting the performance of several businesses and is also used to measure the other parameters required to grow the business. Business intelligence has become a subset of machine learning, which is able to handle business statistics much faster and more accurately than a team of human beings can ever do. Thus it is very good at business intelligence and analytics.
• Self-driving cars: Another useful implementation of artificial intelligence using machine learning is in autonomous vehicles, where machine learning models are used for identifying the objects and their path. Machine learning is making fast progress in the field of self-driving cars but has not mastered it efficiently due to several reasons, but in the near future, self-driving cars will be common in public.
• Image recognition: It is one of the biggest applications of machine learning algorithms where the models are trained to identify the objects based on the images. This technique can be used in a wide field and is also used for face detection and other major applications such as the detection of diseases through X-ray images or CT scan images. Thus, image recognition is a very helpful technique for machine learning models.
• Speech recognition: Speech recognition is an achievement of machine learning as it eases our tasks and helps blind people to navigate through their smartphones or other devices. We have seen the application of speech recognition in Alexa and Siri, which can do tasks as per our commands by recognizing our voice and understanding what is meant. Thus it is a very advantageous and useful advantage of machine learning.
• Spam and malware filtering: We receive various emails throughout the day, of which some of the emails that we receive are not important or may contain viruses and tricks to deceive us. These emails are called spam and must be removed before we waste our time opening and reading these emails. Thus, machine learning can filter these spam emails easily by getting the content, heading, and several keywords in the mail.

Challenges and the future of machine learning

There are several challenges involved while working with machine learning techniques. Some of these are:

1. Machine learning models can be expensive as they are implemented by data scientists who demand higher salaries, and the projects in machine learning require expensive infrastructure.
2. The problem of bias still exists in machine learning which may affect the results obtained and may harm the business. In the case of data, scientists fail to apply those steps, which will help us to obtain better data.
3. Poor quality of the dataset can lead to major problems, and inaccurate or faulty predictions will be obtained as a result. Therefore, it is necessary that a good quality dataset is collected and used for training the machine learning models.
4. Underfitting and overfitting the training dataset is another challenge faced by every data scientist or machine learning engineer, which may lead to faulty predictions. However, these issues can be resolved by implementing some necessary machine learning processes for cleaning and preparing the dataset.
5. The machine learning algorithms show imperfections in several algorithms, and the training dataset grows in size due to several reasons, such as irregularity in the dataset and other reasons. This results in making predictions that are not concise and accurate.

Machine learning will grow more in the upcoming future as more and more businesses will employ machine learning techniques, and it will obtain more popularity which will power advanced artificial intelligence applications and software. Machine learning has become one of the major platforms among the enterprise’s competitive realms, and with some of the major vendors such as Amazon, Google, IBM, Microsoft, Netflix, and others challenging each other to sig more customers for their platform services, it continues to rise above all other techniques. With the rise in customer data, the need to process these datasets will grow, and the companies will have to study and analyze the data to obtain productive results and for the growth of their businesses; thus, the use of machine learning algorithms will continue to grow.

The continued research into machine learning, deep learning, and artificial intelligence has increased the focus on these technologies, and due to their amazing achievements, the research in these fields also continues to grow. However, there are some researchers who are trying to explore ways to make more flexible models which will allow the machine to apply the context learned from a task to different tasks in a much easier manner.

Conclusion

Machine learning is a niche technology that is very efficiently and popularly used as a recommendation engine and takes data as an input to produce an output. The output mainly consists of predictions and recommendations and is also used for classifying several types of data or images from each other using the algorithms. The machine learning algorithms are basically divided into four types depending upon the type of dataset as labeled or unlabeled dataset and on the basis of their use cases. Supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning are four types of machine learning algorithms implemented based on the situations.

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