Applications and Types of Machine Learning

What are the applications of machine learning? Type of machine learning?

According to Wikipedia: Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so.

Application of Machine Learning

1. Machine Learning Application in Retails

Machine Learning has opened a new vista of marketing and business process optimization in the retail sector. To understand the application of Machine Learning for retail, let us have a look at the various contexts in which this technology is used for retail.

• To offer retail customer truly personalized product recommendations.
• Offering a better price to boost sales by real-time and dynamic adjustment of prices.
• Making better inventory planning and ensuring better maintenance with the right predictions.
• Offering faster and more efficient delivery based upon past customer data and customer behavior.
• Better prediction of sales and customer service based upon earlier customer behavior data.
• Perfecting app user experience and optimizing website content based upon in-app and on-web customer behavior and interactions.
• Better segmentation of customers on the basis of previous customer behavior.

2. Machine Learning Applications in Travel

Data Science is showing the way of how we will travel in the future. If you are looking for new ideas on how to make good use of the huge data your business activity yields, here are some Machine Learning applications in the travel industry.

• Smart travel assistant for searching the cheapest offers, booking flights and making hotel bookings, planning complete trips, and improving your general customer experience through useful information.
• Better recommendation platforms for car rental offers, alternative travel dates or routes, new travel destinations according to user preferences, or even some recommended local attractions.
• Flight rates and hotel prices vary constantly according to the supplier and the anticipation of the purchase. So with the help of machine learning smart tools that monitor and send timely alerts with interesting offers are in great demand in the travel industry.
• Analyzing customer behavior through the use of machine learning profiles and technologies can help prevent and detect illegal transactions.

3. Machine Learning Applications in Healthcare

The increasingly growing number of applications of machine learning in healthcare allows us to glimpse at a future where data, analysis, and innovation work hand-in-hand to help countless patients without them even realizing it.

1. Identifying Diseases and Diagnosis

2. Drug Discovery and Manufacturing

3. Personalized Medicine

4. Machine Learning-based Behavioral Modification

5. Smart Health Records

6. Clinical Trial and Research

7. Outbreak Prediction

4. Machine Learning Applications in Finance

• Fraud Detection: Identifying and preventing fraudulent transactions requires sophisticated solutions that can analyze high-volume data.
• Investment Predictions: Fund managers can identify market changes earlier than is possible with traditional investment models.
• Customer Service: Machine learning puts a new spin on virtual assistants by enabling them to learn.
• Loan Underwriting: Financial institutions that offer insurance products to their clients yield the same benefits from ML as insurance companies.
• Process Automation: ML can do more than automate back-office and client-facing processes. It can interpret documents, analyze data, and propose or execute intelligent responses.

5. Machine Learning Applications in Media

Machine Learning applications for social media marketing highly rely on such AI subfields as natural language processing and optical character recognition.

• Text recognition: can analyze the text in any image file that you upload, and then convert the text from the image into the text which is helpful in handwriting recognition.
• Image recognition: The most controversial technology of the Facebook AI suite is the famous DeepFace network for face recognition.
• Recommendations platform: Personalized content recommendations are to blame for the addiction-forming nature of social media. A great example is the Instagram Explore tab, which analyzes what posts capture your interest to pack your Explore tab with similar pictures.

Types of machine learning

Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are three basic approaches: supervised learning, unsupervised learning, and reinforcement learning. The type of algorithm a data scientist chooses to use depends on what type of data they want to predict.

Supervised learning

In this type of machine learning, data scientists supply algorithms with labeled training data and define the variables they want the algorithm to assess for correlations. Both the input and the output of the algorithm is specified.

Let us consider the example of a school, where a teacher asked to check her student’s performance on the basis of a random test. The teacher already has test answers with her, plus the answers which the student gave. Now the teacher will check the student answers with the actual answers to get the performance, this is called accuracy. In the same way, supervise learning works, There would be 2 outputs, one which is given by the machine and the actual output.

Supervised learning problems can be further grouped into regression and classification problems.

Classification: A classification problem is when the output variable is a category, such as red or blue or disease and no disease.

Regression: A regression problem is when the output variable is a real value, such as dollars or weight.

Unsupervised Machine Learning

This type of machine learning involves algorithms that train on unlabeled data. The algorithm scans through data sets looking for any meaningful connection. Both the data algorithms train on and the predictions or recommendations they output are predetermined.

Let’s, take the case of a baby and her family dog. The baby knows and identifies her dog. A few weeks later a family friend brings along a dog and tries to play with the baby. Baby has not seen this dog earlier. But it recognizes many features (2 ears, eyes, walking on 4 legs) are like her pet dog. She identifies the new animal as a dog. This is unsupervised learning, where you are not taught but you learn from the data (in this case data about a dog.) Had this been supervised learning, the family friend would have told the baby that it’s a dog.

Unsupervised learning problems can be further grouped into clustering and association problems.

Clustering: It mainly deals with finding a structure or pattern in a collection of uncategorized data. Clustering algorithms will process your data and find natural clusters(groups) if they exist in the data.

Association: This unsupervised technique is about discovering interesting relationships between variables in large databases. For example, people that buy a new home most likely to buy new furniture.

Reinforcement learning

Reinforcement learning is typically used to teach a machine to complete a multi-step process for which there are clearly defined rules. Reinforcement learning is all about making decisions sequentially. In simple words, we can say that the output depends on the state of the current input and the next input depends on the output of the previous input.

Let us consider an example of you playing a game. You can reach the final level in one go. You 1st play the game, lose it multiple times, learn from your mistakes, and replay it. Reinforcement learning works in the same way.

I hope you have understood the applications and types of machine learning.

References

[1]. “Machine Learning textbook”. www.cs.cmu.edu. Retrieved 2020–05–28

[2] 􀀁Book: Master machine learning algorithms by Jason Brownlee

[3]. Wikipedia link: https://en.wikipedia.org/wiki/Machine_learning

[4]. https://data-flair.training/blogs/machine-learning-tutorial