Top Real-World Machine Learning Examples in Modern Times

What is Machine Learning?

Machine Learning is a sub-field of computer science concerned with building algorithms that, to be helpful, rely on a collection of examples of some phenomenon. These examples can come from nature, be handcrafted by humans, or be generated by another algorithm. Machine Learning can also be defined as the process of solving a practical problem by:

• Collecting a dataset
• Algorithmically training a statistical model based on that dataset.

Why Pursue Machine Learning? 

Machine Learning has become a significant competitive differentiator for many companies. Machine Learning significantly reduces efforts, saves time, and is a cost-effective tool that replaces multiple teams analyzing, processing, and performing regression testing on the data. Many of today's leading companies, such as Facebook, Google and Uber, make Machine Learning a central part of their operations. 

Careervira.com helps fresh graduates and early career professionals (0-5 years' experience) looking to build their career in Data Science, Analytics, and Machine Learning – the most in-demand employment skill. Here a candidate learns practical skills in python, deep learning and neural networks, NLP, R modelling, scikit, data visualization, and predictive analytics. It has been rightly pointed out by Datacamp that Machine Learning is for everyone.

The Machine Learning project lifecycle consists of the following stages: goal definition, data collection and preparation, feature engineering, model training, evaluation, deployment, serving, monitoring, and maintenance. At most stages, data leakage may arise. The analyst must be able to anticipate and prevent it. After data preparation, feature engineering is the second most crucial stage. For some data such as natural language text, features may be generated in bulk using bag-of-words techniques. 

Good features have high predictive power, can be computed fast, and are reliable and uncorrelated. They are unitary and easy to understand and maintain. Feature extraction code is one of the most critical parts of a Machine Learning system. It must be extensively and systematically tested. Best practices are to scale features, store and document them in schema files or feature stores, and keep code, model, and training data in sync. 

Real-world Contexts: 

• Device malfunctioning
  When detecting device malfunction, a good context contains vibration and noise levels, the task executed by the device, the user ID, the firmware version, the time passed since manufacturing and the last maintenance, and the number of uses since manufacturing and the last maintenance.
• Emergency room hospitalization 
  To decide whether the new patient should be admitted to an intensive care unit, a good context would include age, blood pressure, temperature, heart rate, pulse oximetry, complete blood count, chemistry profile, arterial blood gas test, blood alcohol level, medical history, and pregnancy.

Credit Risk Assessment 

To make an approval or rejection decision for a credit card application, a good context would include age, education, employment status, country residency status, annual salary, family status, outstanding debts, or availability of other credit cards, whether the person is a homeowner or tenant, whether the person has declared bankruptcy, and whether and how many times the person missed past credit payments. Machine Learning can simplify this process.

Advertisement Display 

To decide whether a specific advertisement should be displayed to a website user, a good context would include the webpage title, the user's position on the web page, the screen resolution, the text on the webpage, the text visible to the user, how the user reached the webpage, and the time spent on it. The context might include the browser version, operating system version, connection information, and date and time for logging and debugging purposes.

However, to practically apply these real-time examples and see results, a person has to take a degree course such as an MDP programme in Machine learning applications from FORE School of Management. Also, one should be wary of how to select and go about selecting courses on Machine Learning since applications, theory, or workable knowledge of Machine Learning makes all the difference. To check out more than 150+ courses and their contents, Click here.

Supervised Learning 

In supervised learning, if your examples are email messages and your problem is spam detection, you have two classes: spam and not_spam. In supervised learning, the problem of predicting a class is called classification, while the problem of predicting an actual number is called regression. The value that has to be predicted by a supervised model is called a target. An example of regression is the problem of predicting an employee's salary given their work experience and knowledge. An example of classification is when a doctor enters the characteristics of a patient into a software application, and the application returns the diagnosis. 

Unsupervised Learning 

In unsupervised learning, the dataset is a collection of unlabeled examples {x1, x2, ...., xN }. An unsupervised learning algorithm aims to create a model that takes a feature vector x as input and either transforms it into another vector or into a value that can be used to solve a practical problem. 

Clustering is useful for finding similar objects in a large collection of objects, such as images or text documents. Outlier detection helps solve a network intrusion problem (by detecting abnormal network packets that are different from a typical packet in "normal" traffic) or detecting novelty (such as a document different from the existing documents in a collection). 

Semi-Supervised Learning 

In semi-supervised learning, the dataset contains both labeled and u examples. Usually, the quantity of unlabeled examples is much higher than the number of labeled examples. The goal of a semi-supervised learning algorithm is the same as the goal of the supervised learning algorithm. The hope here is that, by using many unlabeled examples, a learning algorithm can find (we might say "produce" or "compute") a better model.

Reinforcement Learning 

Reinforcement learning is a subfield of Machine Learning where the machine (called an agent) "lives" in an environment and can perceive the state of that environment as a vector of features. The machine can execute actions in non-terminal states. Different actions bring different rewards and could also move the machine to another state of the environment. A common goal of a reinforcement learning algorithm is to learn an optimal policy. Reinforcement learning solves a problem where decision making is sequential, and the goal is long-term, such as game playing, robotics, resource management, or logistics. 

You can learn more about these principles here: Link.

Machine Learning texts, online tutorials, and courses are devoted to explaining how Machine Learning algorithms work and how to apply them to a dataset. 

Various Machine Learning courses/programs can help you with this, such as: 

• Super 100 machine learning bundle by Eduonix
• Reinforcement Learning from Edureka!
• Applied machine learning by AI Patasala and Pluralsight
• Machine learning for everyone by Datacamp

Your success will also depend on other factors. What data you can get and whether you can get enough of it, how you prepare it for learning, what features you engineer, whether your solution is scalable, maintainable, cannot be manipulated by attackers, and does not make costly errors — these factors are much more important for an applied Machine Learning project. 

Nevertheless, most modern Machine Learning books and courses often leave these aspects for self-study despite their magnitude. Some provide only partial coverage, with just an application to solving a specific illustrative problem. It is a significant knowledge gap, and Careervira can help you figure this out with an ML course from Pluralsight (Click here).

We also recommend a hands-on approach to this field in association with IBM and certified by DataTrained Link.

To get more information regarding Machine Learning and Deep Learning Courses from top learning institutes, Click here.