What is machine learning?
The standard definition of machine learning says, “It is a subset of artificial intelligence that offers a machine the ability to learn and act like humans without being programmed manually.”
Let’s explain this by taking a real-life example-
While shopping online on any e-commerce store, you must have seen “recommended for you” or “similar products” option on a product page. The website recommending or showing you products similar to the one you are currently viewing is by the help of machine learning. Yes, no human is observing your activity or sitting on the other side to show you similar products. It’s the machine learning that do this job.
Well, this technology is actually helping millions of businesses to drive their sales, boost productivity, and achieve their business goals.
What are the features of machine learning?
Let’s walk you through the characteristics of machine learning:
- Automate data visualization– Machine learning has the potential to visualize structured and unstructured data by applying rich snippets provided by its different tools. With the help of ML, businesses can get valuable insights to take their business to the new heights.
- Automate repetitive tasks– ML has the potential to perform repetitive tasks that require a lot of time and effort. For instance, it can go through hundreds of documents and categorize them according to the filters define.
- Better customer engagement-Customer support plays a crucial role in any business; at any point, if you fail to answer your customers’ queries, then you might lose them. But ML can efficiently do the same task by understanding the intent of the query and provide the resolution from the pre-defined answers.
- Data analysis– Machine learning can analyze a massive volume of data and provide accurate results. To do the same, ML uses well-designed algorithms and data-driven models.
Benefits of machine learning
Machine learning offers a plethora of benefits to healthcare, marketing, finance, e-commerce, travel, hospitality, and other industries. Check out some of them below:
- Simplifies product marketing
- Helps in sales forecast
- Helps in medical diagnosis and facilitate precise prediction
- Assists in looking into customers’ behavior to gain actionable insights
- Simplifies data analysis and data mining
- Assists in detecting fraud and spam
- Improves customer engagement
How does machine learning work?
Machine learning works with its algorithms; therefore, you have to train the algorithm with a training data set to generate a model. Whenever the algorithm receives new data input it makes prediction according to the saved model.
Now, this prediction is examined for precision, and if the result is acceptable, the algorithm is delivered. In the other case, the algorithm is trained over and over again, until the desired level of precision is achieved. This is just an overview of how ML works. To know more about the working or how it used for app development, reach out to a machine learning company or machine learning developer.
What are the types of machine learning?
Shedding some light over the types of machine learning:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Let’s read them in detail:
In this type of machine learning, the data set trains the model or the machine, which then thinks and acts like a human being. After the training finishes, the trained machine can individually take decisions or make predictions when the new data is fed to it.
In this type of ML, the data model leverages observations to figure out the particular structures in the data. When the model receives a dataset, it looks out for that structure or pattern by building clusters in it. However, unsupervised machine learning cannot give a name to these clusters, but it will do the prime task of segregating or categorizing data based on the observations.
Reinforcement learning follows the hit and trial method. This type of ML is concerned with how software agents will respond or interact in an environment. Based on the responses, the agent is given positive and negative points. All the positive points then help the data model to train itself and examine the new data entered, accordingly.
These types of machine learning have been further categorized into different categories. To know more about them and how they help an ML-based application to take decisions or forecast, contact a reliable machine learning development company.
Use cases of machine learning
Machine learning is an emerging technology that is being used globally by startups and enterprises to improve their processes using face detection, voice recognition, recommendation system, and other ML techniques.
Machine learning has redefined the way businesses use to work; therefore, it is no wrong to say that the technology is expanding at a rapid pace throughout the world. The immense benefit it renders is the reason why businesses are eager to opt it in their existing processes or want to develop new ML-based applications. But, before getting into the integration of ML in the current applications/software or development of a new app, it is necessary to think about how to leverage this technology to get the most out of it for your business. You can analyze the same on your own or reach out to a machine learning app development company to get the guidance by experts.