Machine Learning is an application of Artificial Intelligence. Also, it allows software applications to become accurate in predicting outcomes. Moreover, machine learning focuses on the development of computer programs. The primary aim is to allow the computer to learn automatically without an human intervention.

Types of Machine Learning

1) Supervised Machine Learning Algorithms

In Supervised Learning, the dataset on which we train our model is labeled. There is a clear and distinct mapping of input and output. Based on the example inputs, the model is able to get trained in the instances. An example of supervised learning is spam filtering.

2) Unsupervised Machine Learning Algorithms

In Unsupervised Learning, there is no labeled data. The algorithm identifies the patterns within the dataset and learns them. The algorithm groups the data into various clusters based on their density. Using it, one can perform visualization on high dimensional data.

3) Reinforcement Machine Learning Algorithms

In Reinforcement Learning is an emerging and most popular type of Machine Learning Algorithm. It is used in various autonomous systems like cars and industrial robotics. The aim of this algorithm is to reach a goal in a dynamic environment. It can reach this goal based on several rewards that are provided to it by the system.

Machine Learning Applications

Machine Learning can used in various domain as follows.

1) Machine Learning in Education

Teachers can use machine learning to check how much of lessons students are able to consume, how they are coping with the lessons taught and whether they are finding it too much to consume. Of course, this allows the teachers to help their students to grasp the lessons. Also, it prevent the students from falling behind or even worst, dropping out.

2) Machine Learning in Digital Marketing

ML is being implemented in digital marketing departments around the globe. Its implications involve utilizing data, content, and online channels to increase productivity and help digital marketers understand their target audience better. But how, exactly, are ML tools being used in digital marketing strategies today? The experts at Smart Insights have compiled a few examples of how ML can make its way into your digital strategy, including:

Content marketing:

In recent years, digital marketers, bloggers, and businesses of all sizes have been busy creating content of all types to engage their target audience. ML tools can be a beneficial part of helping digital marketers uncover and understand this data better. By tracking consumer trends and producing actionable insights, ML tools allow you to spend time streamlining your tasks to reach more leads with your content.

Pay per click campaigns:

Gone are the days of marketers trying to analyze data sets to measure the effectiveness of pay per click (PPC) campaigns. ML tools can help you level-up your PPC campaigns by providing information that demonstrates: • The metrics you need to help drive your business forward • How you can make better, strategic decisions based on the top performance drivers • Overcome the struggles that keep you from meeting PPC goals.

Search engines:

Search engines rely on machine learning to improve their services is no secret today. Implementing these Google has introduced some amazing services. Such as voice recognition, image search and many more. How they come up with more interesting features is what time will tell us.

Content management:

To drive brand awareness and build engagement, digital marketers must create meaningful relationships with leads, prospects, and customers alike. As you attempt to optimize your dialogue and develop engagement across multiple online platforms. ML tools will be immensely helpful in analyzing what type of content, keywords, and phrases are most relevant to your desired audience.

3) Machine Learning in Health Care

Machine learning in medicine has recently made headlines. Google has developed a machine learning algorithm to help identify cancerous, tumours or mammograms. Stanford is using a deep learning algorithm to identify skin cancer. A recent JAMA article reported the results of a deep machine-learning algorithm was able to diagnose diabetic retinopathy in retinal images. It’s clear that machine learning puts another arrow in the quiver of clinical decision making. Machine learning can offer an objective opinion to improve efficiency, reliability & accuracy.