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 means.
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. It provides computers with the capability to learn without being programmed.
Machine learning aims at building a system that is automated and improves with experience, and that can perform without relying on coding. The machines are fed with good quality data and are trained using different algorithms. The type of algorithm to be selected depends upon the kind of data that needs to be automated.
Now that we have understood what machine learning is let us dive deep into the different types of machine learning algorithms and their features.
Machine learning is broadly classified into four types:
- Supervised Machine Learning
- Unsupervised Machine Learning
- Semi-supervised Machine Learning
- Reinforcement Machine Learning
Let us go through each one of them in detail.
Supervised learning is applied when past learnings are applied to the new data set to predict future events. In this type of learning, the algorithm is getting trained 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
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:
- Anomaly detection
- Association mining
- Dimensionality reduction
In semi-supervised machine learning, the algorithm is trained with a small amount of labeled data and a large amount of unlabelled data. So, there is 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 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:
- Video gameplay
- Resource management
To understand the effectiveness of machine learning, let us learn about its characteristics. Explained below are some features of ML with examples:
- 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.
- 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.
- 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.
- Accurate data analysis
While working with large sets of data, it sometimes becomes impossible to analyze the data correctly. Machine learning is the best solution for making this task easier with perfection. It can generate accurate analysis and results for massive volumes of data.
Example: Upstox, a platform for online share trading, uses ML to predict the ups and downs of the market and shows its users exact data, which helps correctly investing money.
- 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.
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.
With algorithms, machines are trained to make predictions and take decisions that impact business growth. As the demand for big data is booming, the market for data scientists is also expanding, bringing in opportunities as they are required to assist in identifying the most relevant business questions and, subsequently, the data to answer them.
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