Why You Should Use Machine Learning Within Your Website for Customers

August 14, 2018 | Sakshi Sharma

Machine learning may sound like an alien word to people, however it’s used in many appliances that we utilize. For example, Facebook uses machine learning to allow users to find new types of content and avoid spam. Self driving cars, Amazon’s Alexa, cancer detection, and numerous other appliances use machine learning as a way for computers to “learn” data and do the jobs they need to do.

This type of artificial intelligence utilizes different algorithms to interpret and display data, allowing companies to find strategies and opportunities in revenue in order to improve their customers’ experience on their website.

There are many types of algorithms used in machine learning. One algorithm doesn’t fit every problem. The machine learning algorithm best suited for your website is always dependent on the available data, the intended usage of the data, and the data scientist’s experience with the subject. Comprehending how and why the algorithms are different ensures that every predictive model built creates reliable results.

What is a predictive model? A predictive model stems from the function Y = f(x), which serves as a foundation for the creation of algorithms. In this function, Y represents the predictions that could be made given X, the input variables. This function is used when we don’t have an idea of what the function looks like. (If this was known, then machine learning algorithms wouldn’t be necessary to interpret the data since you could use the function directly). The predictive model, also known as predictive analytics, is formed from mapping Y = f(X), which can be used to make the most accurate predictions.

As mentioned earlier, there are many types of machine learning algorithms like the Internet of Things devices and how they predict the likes and wants of people. It can be confusing to try and navigate them all. However, we’ve narrowed down the top 3 most popular machine learning algorithms to help you get started navigating the world of AI.

1. Logistic Regression

Logistic Regression is a simpler type of machine learning algorithm, which can be easily implemented for a variety of tasks. Logistic regression is commonly used for problems with two class values. This type of problem, known as the binary classification problem, produces two outcomes that are affected by one or more variables.

Unlike other algorithms, logistic regression enables the effect of each specific data to feature to be interpreted. For this reason, logistic regression is used when the data needs to be collected with as much accuracy as possible. A type of real-world example of a logistic regression problem is cancer detection, where the logistic regression algorithm would be used to detect if a patient has a type of cancer-based on a screening picture that serves as the input.

2. Neural Networks

Neural Networks is another type of machine learning that functions similarly to the brain. For example, let’s say you unexpectedly bump into a coworker at the supermarket. Your brain needs to work to recognize the face of the person and associate the name and your relationship with the person all in an instant.

This is similar to how artificial neural networks function. Artificial neural networks consist of the input layer, the hidden layer, and the output layer, which all pass information to each other. The hidden layers are composed of interconnected neurons, the output layers of predictions, and the input layers of raw features. While information is being fed into the machine, the connections between the network produce highly accurate predictions.

This is one of the most complex types of neural networks, which functions in areas of high-dimensional AI problems like natural language processing, image segmentation, and object and speed recognition. Without specific tools, interpreting data from neural networks can be extremely difficult. This type of machine learning is used when the data needs to be analyzed with extreme accuracy.

3. Random Forest

A random forest is a collection of “decision trees”. Decision trees consist of nodes on a graph symbolizing a question about the data and branches that branch off from each node representing each questions’ potential answers. Random forests are highly accurate and are very popular, starting at a relatively low computation expenditure. Random forests are applicable to many types of problems. It runs on large databases with efficiency, can be built without a great deal of tuning, and is generally fast.

How Do I Use Machine Learning In My Website?

Using specific types of machine learning can greatly alter the way your company forms marketing strategies and takes advantage of any revenue opportunities that arise. As seen with major companies like Facebook, Apple, Amazon, and health and tech companies, machine learning is benefiting the customer experience and improving companies’ revenue. There are also multiple ways AI can help your website with its predictive capabilities.

You may be wondering which one would work best for your website. That answer is dependent on many factors which a development company like SDI can help you determine. With the right Machine Learning technology your company could see drastic positive changes.

Are you interested in using machine learning on your website? With the help of software development professionals from SDI, your company can utilize this technology to enhance the experience of the customer and increase conversion rates. If you want to implement machine learning in your websites, contact Sakshi at sakshi@sdi.la or 408.621.8481 for more information on how to get started.

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