The Role of Annotated Data in Supervised Machine Learning

Modern organizations rely on machine learning so that they can analyze different types of data. Machine learning models need to be exposed to different types of settings to make them reliable. Machines cannot do things on their own. Humans are still needed so that quality data can be analyzed and used.

Machine Learning Models – Supervised and Unsupervised Machine Learning

Supervised and unsupervised machine learning are two different types of machine learning models. The various models can be trained depending on the conditions that are needed. They have differences that will make companies choose one over the other. 

Supervised Machine Learning

This type of model requires the help of people who can provide data annotation machine learning services. The training data will be properly labeled by a data scientist and will be prepared before it will be introduced to the machine. The machine needs to learn the correlation between the input and output data so that it will be more accurate. The time will come when it will come across some datasets that it has not encountered yet. A properly trained machine will be able to classify the data based on the labels and predict potential outcomes.

This requires the oversight of humans. Supervised machine learning, particularly when it involves complex task like heatmaps video annotation, would not be possible without the contributions of professionals. The data that will be prepared are usually unlabeled and it is up to humans to label them before feeding them to the machine.

Unsupervised Machine Learning

Unsupervised learning in machine learning is the opposite of supervised machine learning. The machine will be exposed to raw and unlabeled data because its goal is to check the patterns in the raw datasets. The machine can start clustering different types of data depending on the trend or the pattern that it sees. There is no need to label the data before it will be checked by the machine.

The approach here is not hands-on. There are just some unsupervised algorithms in machine learning that need to be set. Once this happens, the machine will work its magic in trying to group the data according to the similarities of the data it receives.

Some Differences Between the Two

  • Supervised machine learning will rely on labeled input and output training data. Unsupervised machine learning will rely on unlabeled data.
  • Labeled training data is going to be more time-consuming and will require more resources to create as compared to unlabeled training data.
  • Supervised machine learning is better for trying to predict possible outcomes depending on the labeled data that was received. Unsupervised machine learning is better for clustering different types of data to see if they are correlated with each other. This is also better in trying to see if there are inherent trends that can be used in the future.
  • It’s going to be difficult to create the right levels of being able to explain the data in unsupervised machine learning because the data was placed without much human supervision.

Knowing the similarities and differences between the two types of machine learning can help companies decipher which one they need. 

Labeling machine learning is very useful when you want to know the possible outcome of the project that you are doing based on the trends. You can learn the chances of selling more products depending on how you will market your products. Unsupervised machine learning will help you see outliers that are stopping you from reaching your full potential. Other details about machine learning can be found right here.

When to Use Supervised vs Unsupervised Machine Learning

labeling machine learning

You want to know when you should use supervised machine learning and unsupervised machine learning for reference. It’s quite hard when you are learning all the details all at once which is why you are recommended to hire professionals who know how to do machine learning annotation. 

Use supervised machine learning for the following:

  • Predictive texts
  • Spam detection
  • Face and object detection

Use unsupervised machine learning for the following:

  • For classification of data
  • For finding possible anomalies
  • Discovering the hidden structure of the data presented to the machine

Ways to Outsource Data Annotation Specialists

Whether you choose to use supervised or unsupervised machine learning, you still need professionals who are familiar with similarity score machine learning and the use of annotation tools for machine learning. 

You can choose to hire an in-house employee but this can cost more money in the long run. Outsourcing is great because you can find people who can provide data annotation machine learning services and great skills from different parts of the world.

Start Hiring Freelancers

Some companies like hiring freelancers because they can be hired for a specific project to test their skills. If you like how they do data labeling machine learning, you can hire them again for another project. There is no need to keep the employee for a long time especially if the person is not performing up to your standards.

Pros:

  • This is more cost-effective. You can spend the money on other things that your business needs.
  • They require less supervision as long as you give them proper instructions.
  • Risks are reduced.

Cons:

  • The outcome will not always be great.
  • The time difference can be a problem especially if you need to communicate.
supervised machine learning

Getting a Company that Provides Managed Labeling Services

Some companies would like to make themselves more useful by providing some specialized services. If you want to hire a team that can work on speech recognition or similarity machine learning, they can provide that for you.

Pros:

  • You will get access to high-quality materials.
  • You do not have to worry about training people to improve their labeling machine learning skills.
  • There is no need to self-manage the technologies you need.

Cons:

  • They will have other clients aside from you.
  • Some of the services are not specialized according to your needs.

Hiring a Dedicated Labeler from a BPO Company

You want to hire a specialist who has a lot of experience in doing similarity learning. You do not have to worry that the person does not know anything about it. He will know what to do. Assign the project and the specialist will begin working on it.

Pros:

  • You are hiring a specialist who can give the output that you want. For example, you have specifically looked for a text categorization specialist. Expect that he will provide the type of work that you are looking for.
  • The specialist knows everything about the job and the project inside and out.
  • The specialist already follows a specific workflow so that deadlines can be reached.

Cons:

  • Costs can be higher.
  • They may be inflexible if you need them to do other things.

You should consider the pros and cons of each before you make the best choice.

Learn More About Machine Learning Services and How They Can Benefit You

You deserve to know how machine learning can provide the information that you need to improve your business. You can get in touch with us so that we can connect you to specialists who can provide high-quality machine learning services now. 

Svitlana Orlenko
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