Different Types of Machine Learning Algorithms

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The advancement of Artificial Intelligence is moving at a fast speed in the realm of innovation. Most services that we use daily rely on machine learning. Before learning about machine learning algorithms, let us first understand what the term Machine Learning Algorithms means.

What is Machine Learning?

Machine Learning is an application of AI in computer science that gives devices the ability to focus on using data and algorithms to imitate the way humans learn and act.

Machine learning empowers computers to learn without being explicitly programmed. The goal of machine learning is to create an automated system that improves with experience and can perform without depending on coding. Different algorithms are utilized to train machines, and the selection of the algorithm depends on the type of data that requires automation. Now, let’s delve into the various types of machine learning algorithms and their features.

Different Types of Machine Learning

Machine learning is broadly classified into four types:

  1. Supervised Machine Learning
  2. Unsupervised Machine Learning
  3. Semi-supervised Machine Learning
  4. Reinforcement Machine Learning

Let us go through each one of them in detail.

Supervised Machine Learning

In supervised learning, we apply past learnings to the new dataset to predict future events. In this type of learning, the algorithm trains on a labeled dataset. It has a set of input variables (x) and an output variable (y) that identifies the mapping function. When input data is fed into the algorithm, it balances its weights until the algorithm has been fitted correctly. This happens to cross-validate and to ensure that the algorithm avoids overfitting or underfitting. This type of learning is helpful for organizations as it solves various real-world problems, like classifying spam in a separate folder from your inbox.

Examples where supervised learning works:

  • Binary classification
  • Multi-class classification
  • Regression modeling
  • Ensembling

Unsupervised Machine Learning

Unsupervised learning is when the output is unknown; there is only an input variable. Here, the algorithm is trained on unlabelled and unstructured data, so it learns itself. This algorithm is designed to observe how a system will judge the information that is not classified. The algorithms can discover hidden patterns or data grouping without the need of a human.
Examples where unsupervised learning works:

  • Clustering
  • Anomaly detection
  • Association mining
  • Dimensionality reduction

Semi-supervised Machine Learning

In semi-supervised machine learning, the algorithm trains with a small amount of labeled data and a large amount of unlabeled data. It incorporates a mix of both the learnings discussed above. This learning method uses unsupervised techniques to predict labels and then feed these labels to supervised techniques. The systems that work on this algorithm have a better learning accuracy.

Examples where semi-supervised learning works:

  • Machine translation
  • Fraud detection
  • Labeling data

Reinforcement Machine Learning

Reinforcement learning is when the data scientists train the algorithm to work on the data with a set of pre-defined rules. It learns as it goes by using trial and error. These algorithms are trained to make decisions upon the feedbacks and rewards they receive for their actions. Data scientists program an algorithm to finish a task and provide it positive or negative signal as it works out to complete a task. This is known as the reinforcement signal.
Examples where reinforcement learning works:

  • Robotics
  • Video Gameplay
  • Resource management

Features of Machine Learning

To understand the effectiveness of machine learning, let us learn about its characteristics. Explained below are some features of ML with examples:

  1. The ability to perform automated data visualization

    Businesses today deal with a massive amount of data daily. It is not at all feasible for an individual to filter out data one by one. Machine learning offers excellent benefits here; it provides rich snippets of data that can be used. Businesses can increase productivity with automated data visualization by quickly getting large data sets in a readable layout.
    Example: The algorithm learns about common data selection patterns within visualizations and their combination with other variables to create charts. Such as to create visualizations, it is common to group data by geography (country, state, sex), characteristics of individuals (citizenship status, marital status, sex), etc. Also, it learns to subset data using common ordinal fields such as responses (yes/no), sex (male/female), etc. and forms graphs for these values against other fields.

  2. Automation at its best

    One of the best gifts of machine learning is the ability to automate. It has made workflow so much easier and productive that most businesses have been using this feature of machine learning to get their paperwork sorted.
    Example: In the financial sector, there is a lot of repetitive data that comes which has to undergo the same process. ML makes these tasks faster and more accurate. Other common examples of ML that you all must have come across are chatbots addressing customer queries, managing expenses, etc.

  3. Customer engagement like never before

    All kinds of businesses must engage with their customers to retain them and convert them into loyal customers. Customers today expect a more personalized experience. Machine learning helps us understand what each customer wants and likes.
    Example: Pinterest, an image-sharing social media service, provides personalized suggestions to its users.

  4. Business intelligence at its best

    When combined with big data, machine learning can bring excellent results, using which industries can develop strategies for growth. It has become one of the most talked-about technology trends as it has drastically improved business operations.
    Example: Healthcare industries use this feature in their machines to help interventional surgeons to make better and more precise decisions for their patients.

Conclusion

These days, machine learning has become an essential component in the growing field of data science. It has gained popularity throughout the world, and it is time for business owners to aim at leveraging AI automation and ML in the right direction to benefit business needs. With innovations and advances in technology, in no time, we will be living in a world where humans and machines will work together to bring out more remarkable results. Teams need to rule out the best strategy for machine learning. It will benefit the overall performance when humans collaborate with the power of ML.

Algorithms train machines to make predictions and decisions that impact business growth. The booming demand for big data is also expanding the market for data scientists. Organizations require data scientists to actively identify the most relevant business questions and, subsequently, the data to answer them.

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