Machine Learning Fundamentals

Machine Learning Fundamentals

In this article, I am going to discuss Machine Learning Fundamentals with Examples. Please read our previous article where we discussed Feature Selection and Data Preprocessing with Examples.

Machine Learning Fundamentals

“The research that provides computers the ability to learn without being explicitly taught,” wrote Arthur Samuel, a computer scientist who pioneered the science of artificial intelligence, in 1959.

Alan Turing’s key study (Turing, 1950) established a criterion for demonstrating machine intelligence, requiring a machine to be intelligent and responsive in a way that cannot be distinguished from that of a human.

Machine Learning is an artificial intelligence application in which a computer/machine learns from past experiences (input data) and predicts the future. The system’s performance should be at least human-level.

Supervised learning, unsupervised learning, and reinforcement learning are the three types of machine learning.

Machine Learning Use Cases

In fact, the use of Machine Learning is not confined to the six industries listed below. Machine Learning can be used everywhere that has at least a little amount of data.

Making a profit, decreasing expenses, optimizing production, caring for the environment, and delivering the greatest service to clients are all benefits of machine learning in business. Let’s look at several distinct ML use cases in various industries –

Retail –

Let’s start with the Machine Learning applications that are currently gaining traction in retail.

  1. Online Fitting Rooms – Virtual fitting room applications are currently available. This is the case when Augmented Reality and Machine Learning are combined. The app’s main feature is that it allows you to submit your photos and begin putting on clothes and create a more intelligent shopping list. Furthermore, smart programmers can provide you with recommendations based on information about you, as well as check the model’s availability in the nearby store. This is an excellent user experience.
  2. Voice and Visual Search – Machine Learning and Artificial Intelligence are used in both voice and visual search technology. Another strategy to increase retail sales is to optimize your site for voice and visual search.
  3. Price optimization and demand forecasting – Machine Learning can accommodate for tiny swings in anticipating demand because it works with a large amount of data. For the same reason, this technology can predict what price purchasers are willing to pay for a specific product over time.
  4. Mood Recognition – Face recognition technology is no longer startling; just look at how your iPhone unlocks. However, technology that recognizes intentions and moods can provide significant benefits to retail. When you know why a certain customer came into the store, you can provide an even more enticing and personalized offer.
  5. Management of Food Wastage – A third of all manufactured food is thrown away. ML can assist in the development of more complex food sales tactics to ensure that all food is sold and not spoiled and thrown away.
Marketing –
  1. Recommendation Engines – This is a great illustration of how Machine Learning can help. By the way, Amazon sales jumped by 35% thanks to the recommendation engine.
  2. Churn Prediction – Machine learning’s strength is predictive analytics. When it comes to anticipating intentions, Churn prediction is simply one of the Machine Learning use cases.
  3. In reality, the technology just monitors the user’s behavior and looks for signals that indicate someone is planning to switch to a competitor
  4. Personalized Offers for Users – Personalizing the content is insufficient. Personalizing your marketing offers is also crucial. By studying each user’s behavior on the site and complementing this knowledge with information from social networks, for example, ML can create the most accurate estimates about what each user would like.
Finance –
  1. Credit Card Fraud Prevention – Credit card fraud is the most serious threat to the banking sector. By analyzing behavior, detecting anomalies, and studying data in real-time, machine learning gives a plethora of solutions to this problem.
  2. ATM Fraud Prevention – Face recognition technology, as previously discussed, can help you provide higher security by instantaneously identifying the cardholder’s face at the ATM. It becomes hard to cash out even if the card and password are stolen.
  3. Loan and Investing Risks Evaluation – We desire to win and profit by investing. Machine Learning can assist in determining the feasibility and profitability of an investment using data that is free of emotional bias. It is possible to determine if a credit transaction will be profitable or risky by examining genuine solvency and forecasting customer intentions.
Healthcare –
  1. Self-Diagnosis – Smartphone apps combine Machine Learning, Artificial Intelligence, wearable devices, and fast data transfer technologies to create powerful self-diagnosing apps. Of course, this saves doctors and clinic owners a tremendous amount of time and energy.
  2. Medicine Management –
  3. The capacity to spot irregularities can help a hospital keep a closer eye on narcotics trafficking. You may be unaware that your doctor, nurse, pharmacist, and the patient began a plot to launder medications for resale. ML can follow data trails and reach a conclusion.
Hospitality –
  1. Chatbots and Virtual Assistants – These are already well-known technologies for improving user experience, and they may be used on both your hotel and restaurant’s websites, adding to the already excellent service.

Supervised and Unsupervised Machine Learning Techniques

Supervised Machine Learning –

You’ve probably come across the term ‘labeled data,’ which is also known as data annotation in data science. This is the process of marking your data to show the outcome or output that your model should produce.

We call supervised learning the process of using labeled data to train a machine learning model to deliver the appropriate level of accuracy and quality output.

When a machine learning model reaches the target level of output accuracy, it has finished training. Because the quality of data is so important in training algorithms, preprocessing data (cleaning, labeling, etc.) can be a time-consuming and costly operation, especially if the volume of data is large. The following categories or classes commonly apply to supervised learning algorithms:

Regression Algorithms:

These help in predicting a continuous value. These algorithms include –

  1. Simple Linear Regression
  2. Multiple Linear Regression
Classification Algorithms:

These are algorithms that forecast the category. These specific algorithms include the following –

  1. Logistic Regression
  2. Support Vector Machine
  3. Decision Tree
  4. K-Nearest Neighbors
  5. Random Forest
  6. Naive Bayes
Unsupervised Machine Learning –

After defining what “labeling data” means, it’s easier to explain that in the case of unsupervised learning algorithms, it’s up to them to find features and patterns in the data they’re given.

There is a widespread belief that supervised learning algorithms are easier to learn than unsupervised learning algorithms. It’s also worth noting that unsupervised learning algorithms come in useful if you don’t want to deal with the hassle of labeling data. The following subfields are divided into unsupervised learning algorithms –

Clustering:

Clustering is the most widely used approach for grouping datasets with similar properties into groups or clusters. The K-Means method A prominent example is clustering algorithms, which consider the distance between a centroid and a data point and assign a data point to the cluster with the shortest distance. Customer segmentation and analysis are the common use of these algorithms. Mixture models, hierarchical clustering, and dbscan are among the others.

Association

Association is a crucial strategy for locating goods that mostly correlate and occur together. If a person buys milk, he or she is likely to buy bread or sugar, for example.

Market basket analysis and identifying links between data in transactional databases are common applications, as are product arrangements in hypermarkets, supermarkets, and even online businesses. The Apriori algorithm is an example of Association algorithms.

In the next article, I am going to discuss Linear Regression in Machine Learning with Examples. Here, in this article, I try to explain Machine Learning Fundamentals with Examples. I hope you enjoy this Machine Learning Fundamentals with Examples article.

Leave a Reply

Your email address will not be published.