Artificial Intelligence (AI) refers to the ability of computers to think like humans and perform similar tasks as people do. While computer scientists have yet to create a near-perfect AI that genuinely feels as a human being does, there are already AI applications that help improve day-to-day activities. You may take AI virtual assistants as a prime example. These applications help you do various tasks such as setting an appointment in your calendar, set an alarm, and more. These types of programs have helped individuals and businesses achieve better efficiency and productivity.

If you’ve heard of AI, you may have also heard about ML or machine learning. It’s a subtopic of AI focused on helping computers learn the same way humans do. People primarily develop machine learning applications by creating models that will guide how the computer thinks. ML models are based on labeled or unlabelled data that the computer scientist or software engineer feeds.

If you want to further learn about machine learning, then you must understand the two main types of machine learning—supervised and unsupervised machine learning. Supervised machine learning essentially involves more human intervention, unlike unsupervised machine learning.

In this article, you’ll learn more about unsupervised machine learning.

• Algorithms

Unsupervised machine learning involves three main tasks: clustering, dimensionality reduction, and association rules. Within these tasks, specific algorithms are used according to the needs of the job or project. That said, here are brief explanations of each main task used in unsupervised machine learning:

• Clustering

Clustering is a technique used for data mining wherein unlabelled data are grouped according to their similarities or differences. To learn more about this method, you may read this guide to clustering algorithms. That said, below are some of the algorithms used to perform this task:

• K-Means Clustering

This is an instance of exclusive clustering and is often used in market segmentation and image compression. It first identifies the ‘k’ number of centroids. A centroid refers to the imaginary or actual location of the center of a cluster. From there, data points are grouped according to the closest centroid.

• Hierarchal Clustering

There’re two kinds of this algorithm: agglomerative and divisive, with the former initially isolating data points into separate groups. Then they are merged iteratively based on similarity until a cluster is achieved. Meanwhile, it’s the opposite for divisiveness, with clusters being made out of differences.

• Probabilistic Clustering

Data points are clustered based on how likely they are to be a part of a particular distribution. The Gaussian Mixture Model (GMM) is the most commonly used method.

• Dimensionality Reduction

Dimensionality reduction involves reducing the number of dimensions or features in a given dataset in the event that it’s too large. For this, the following algorithms are used:

• Principal Component Analysis (PCA)

This algorithm is used to lessen the redundancies in a dataset with a large volume using feature extraction.

• Single Value Decomposition (SVD)

On the other hand, SVD factorizes a matrix denoted with the letter ‘A’ into three lower-ranking matrices.

• Autoencoders

Autoencoders take advantage of neural networks and find a way to create a new representation for the original data’s input. In doing so, this becomes useful for compressing data.

• Association Rules

Association rules focus on finding the relationships between the variables within a given dataset. This has been found to be very helpful in marketing to understand customers’ consumption habits. However, it can be used for recommendation systems too.

For this task, the apriori algorithm is used. Online retailers use this to personalize the shopping experience of consumers online. Aside from e-commerce stores, streaming platforms also use this technology to recommend media content that the user would probably like.

This algorithm is used in transactional datasets. From that, platforms try to determine the likelihood that a user will buy a particular item based on their consumption or purchase of another product.

• Applications

Having established the difference between supervised and unsupervised machine learning, you may want to learn about the applications of unsupervised machine learning. Knowing some applications may help you decide if unsupervised machine learning is for your project.

Unsupervised machine learning is often used for projects involving large volumes of unlabeled data that help companies and developers gain new insight from the dataset. They can be used in the following:

• Recommendation systems
• Customer segmentation
• Anomaly detection
• Segmentation of products
• Preparing data for supervised machine learning as you can use unsupervised to label the data for you

Advantages and Disadvantages

Like any other method, unsupervised machine learning has its share of advantages and disadvantages.

Advantages

• Helps with handling large volumes of data
• No expertise is required initially
• It may be considered time-efficient as you don’t need to spend time labelilng the data.

Disadvantages

• Outliers may cause results to vary
• High risk for computer mislabeling data
• You’ll need an expert on the subject to interpret the results 

Concluding thoughts…

AI and ML are becoming prominent fields of study in computers and data analysis for businesses and consumers. To better understand how ML works, it is best to study its two main types first—supervised and unsupervised machine learning.

This guide has dwelled on the basics of unsupervised machine learning. As you can probably tell, you can encounter unsupervised machine learning whenever you’re shopping online or looking for new shows and movies to watch.

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Benefits of Choosing the Facebook Marketing Course

• Develop Facebook Marketing Skills

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• Practical Experience

Facebook marketing online courses provide practical experience and effective ways to analyse your data for better promotional methods.

• Increase Your Audience:

You can immediately apply your learning to different business activities and increase your audience reach. This course also develops skills to target the appropriate audience based on your product.

The course provides the proper tools, techniques, and training for advertisement guides through the social media platform.

You will also learn about the Facebook blueprint and other training guides to boost your marketing skills. 

• Increase Job Opportunities:

When you become a social media market expert, you can work as a freelancer or an employee in the marketing division for one of the big companies. Job opportunities include:

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As everything is online, the course can be completed part-time from home or anywhere else you like. 

Specific Skills Covered

By doing the Facebook marketing online courses an individual will:

Details About the Facebook Marketing Online Courses

The course provides access to experienced teachers and experts to address any questions. It covers both theoretical and practical aspects of online marketing. The classes are interactive, and everyone gets the chance to ask questions. You can complete the course at your own pace with six months of access. 

It‘s time to grab the opportunity to turn Facebook into a powerful promotion machine. The Facebook marketing online course is very important for anyone who owns a business and is looking for effective marketing strategies, or who has a Facebook page and wants to increase its reach, or anyone who simply wants to start a career in Facebook and social media marketing.

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Computational thinking is one of the core objectives that runs through the computing program of study in England from Key Stage 1 to Key Stage 4. Before computers can be used to solve a problem, computational thinking refers to understanding the problem itself and the ways in which it could be resolved. Software engineers and computer scientists for example, routinely engage in computational thinking. As a higher order thinking skill, computational thinking has applications both across and beyond the school curriculum.

There are four key techniques to computational thinking:

Abstraction – focusing on the important information only, ignoring irrelevant details
Algorithms – developing a step-by-step solution to the problem
Decomposition – breaking down the problem into smaller, more manageable parts
Logic – looking for similarities among and within problems

Learning to program is one of the best ways to develop computational thinking, as it uses each one of these techniques. My intention here is to show an example of a lesson in which computational thinking is taught at Key Stage 1 (5 to 7 years) through programming. I took the lesson plan (attached above) from The Barefoot Computing Project and I taught it to my 1st grade class last week.  It required the children to work in pairs to create step-by-step instructions through pictures.  The pairs then swapped each other’s instructions, which they used to draw the ‘crazy character’ that the other child had in mind.

This sort of lesson is ‘unplugged’ (no computer needed) and teaches children about algorithms. Although the children used instructions to make a ‘crazy character’, any set of instructions could be used to introduce algorithms.  Algorithms are a critical foundation for learning to program because without them no program would run. Since this lesson’s objective was just an introduction to algorithms, only the term ‘algorithm’ was discussed explicitly. The other techniques of computational thinking (abstraction, decomposition and logic) were not explicitly mentioned, but the ideas behind them were explained to the pupils using child-friendly language. I include here how the four key techniques to computational thinking are embedded into this lesson.

Abstraction

Abstraction captures only the most important features of the problem, filtering out the details that we do not need. The Tube map for the London Underground for instance, shows the order of the stations and the interchange between lines, typically ignoring physical distances and locations.

Algorithms

An algorithm is a set of step-by-step instructions to solve a problem. Pupils learn during this lesson that algorithms are the sets of instructions that others can follow to draw their characters.

Bugs are explained to the pupils as a mistake in the algorithm, something that does not work in the program as it was intended. Debugging then needs to be done in which pupils think what the algorithm should do, test it to find out (by showing their partner the drawing), work out where it goes wrong and then fix it. For example, when their partner draws the legs at the bottom of the body but they were supposed to be at the top, the algorithm has to be changed accordingly. In other words, debugging is likely to involve adding greater precision to the language used in one or more of the steps.

Decomposition

Decomposition is where pupils break down a problem into smaller parts. For example, pupils here are required to draw the body first, then the eyes, then the legs, and so on. This activity in particular, can help children to see the importance of looking at a problem in stages.

Logic

Logic requires pupils to look for patterns in problems. By using logic pupils are able to predict what will happen when their partner uses the algorithm to draw the character. They use previous knowledge about what has already happened for steps like this. By both creating and following more algorithms pupils will recognise that they use the same blocks of “code” (in this case, written instructions and pictures) time and time again.

To summarise, the focus on computational thinking in the new curriculum reflects a shift of emphasis from the skills of using ICT to developing an understanding of computer science. Computational thinking, which is the heart of computer science, is not only a useful skill to teach, but it’s also an important and interesting skill to learn. As I hope to have demonstrated here, the basics of computational thinking can be taught at a very young age (from 5 years) without even using a computer.  Since any set of instructions can be used to teach algorithms, the related ideas of abstraction, decomposition and logic can also be embedded into such a lesson to develop children’s computational thinking.