Go Over the 5 Elite Machine Learning Platforms And Importance of its Learning

Technology is an intricate topic that comprises enormous interlinked data and applications. For example, when you watch a video on YouTube, you will get relatable suggestions based on that video afterward. Have you ever wondered how well a machine knows? The answer is Machine learning. Not just that, numerous machine learning platforms are also being designed nowadays to meet the learner’s convenience.

Over the multiple sections, machine learning is a flux for many industries, proving to be a significant asset now and in the future. In 2019, the summation of the global machine learning market was $8 billion. That is expected to rise by $117 billion by 2027 at a CAGR of 39%. This surges the rise in demand for machine learning platforms amongst the learners to thrive in the most emerging field.

Source: (Inkwood Research)

Industries have been adapting machine learning to utilize these platforms, getting needful data that is used to save loads of investments. As a result, machine learning platforms are designed, each with its own beneficial ways of learning. Moreover, there are different types of machine learning formulated to derive different outcomes in practical applications.

Machine learning is no longer a trending topic in the software industry, but yes, it has been a hot topic for the last few years. But what is machine learning, what are different types of machine learning, and what platforms can be used to learn machine learning? Keep reading this post to find answers to your questions.

What is Machine Learning?

Machine Learning means ML is a branch of Artificial Intelligence that enables applications to offer more accurate results using previous data.

Let’s take real-life examples where machine learning is used to deliver a personalized customer experience.

• Smartphones detecting faces or fingers while taking pictures without unlocking the phone
• Facebook and Instagram showing you content based on your preferences
• Amazon recommends you products based on your browsing history
• Financial Institutions and insurance companies using ML techniques to prevent fraudulent activities in real-time.

Now before jumping to the machine learning platforms, let’s shadow some light on its different types.

3 Poplar Types of Machine Learning

Using machine learning and its algorithms, businesses can make informed decisions quickly, and these decisions are more accurate as there is no scope of guesswork. This technique is cost-effective and can analyze a huge volume of data in no time. Now, let’s go through different types of machine learning.

• Supervised machine learning
• Unsupervised machine learning
• Reinforcement machine learning

Machine learning types are divided into these three broad classes; now, let’s go through each type in detail.

Supervised Machine Learning

When you have previous data with results and want to predict future results, you should use supervised learning algorithms. Supervised learning can be divided into two major parts such as

• Classification problem
• And Regression problem

In the classification model, you need to classify results into different classes. Say, for example, whether the floor needs mopping is a classification problem. You also know the result of this problem, it can fall into one of the classes-Yes or No.

Whereas in the Regression model, you need to give an answer in how much format. Let’s carry forward the above example; the floor looks messy, and how much cleaning to be done is called a regression problem.

Unsupervised Learning

Sometimes you don’t want to predict the result. You just want to perform segmentation. For instance, a bank would want to divide customers to understand their preferences. This behavior comes to fall into unsupervised learning as banks are not predicting any outcomes here.

Reinforcement Learning

This is probably the hardest type of machine learning, where the agent learns continually from the environment by interacting with it. It can also be said that reinforcement learning is solely based on assumptions where you might get rewards based on your actions. Let’s understand this with a real-life example.

Let’s take the example of bank loans. A reinforcement machine learning algorithm checks all the previous data of the customer and decides whether they can pay the loan or not. Now, here one can expect two results, i.e., positive or negative. Both aspects help the agent to understand the situation better and thus allow them to make an informed decision.

This is all about machine learning types; now, let’s have a look at machine learning platforms.

Top Machine Learning Platforms That Are a Trend in 2021

Machine learning has become a popular technology across the world as it has the potential to predict user behavior from past experiences and patterns. Today, to survive in this crowded business world, almost all well-known brands such as Google, Amazon, Netflix, and some other large and medium scale enterprises harness the power of machine learning to understand their user’s preferences.

We have gone through the meaning and types of machine learning; now, let’s have a look at machine learning trends that will dominate in 2021 and beyond: Knime, Rapid Miner, Tensor Flow, Shogun, and Pytorch.

1. KNIME

Platform: KNIME Analytics Platform
Plan Details: Free
Official Website: https://www.knime.com/

More than 100,000 people globally are currently using this analytics platform for their machine learning requirements. Based in Zurich, Switzerland, the beauty of this platform is that it is an open-source platform and requires zero codings. It can integrate with popular programming languages such as Python, Java, JavaScript, and all versions of C and C++. If you want to access the cloud version of this platform, it is available on Microsoft Azure and AWS.

2. Rapid Miner

Platform: RapidMiner Studio
Plan Details: Free, Small plan starts with $2500 per year
Official Website: https://rapidminer.com/

Based in Boston, RapidMiner is another popular platform that allows data scientists to build and operate AI solutions. Rapid Miner provides a platform for data preparation, text mining, and predictive analysis, along with machine learning. RapidMiner Radoop, an extension of this platform, comes with capabilities to perform well in a Hadoop environment. If you are building an educational app for research purposes, this platform can help you.

3. Tensor Flow

Platform: End-to-end Open Source Platform
Plan Details: Free
Official Website: https://www.tensorflow.org/

Take Google, the best machine learning example. As we all know how Google leverages machine learning for all its products to improve search engine, translation, and image recognition. TensorFlow does the same that uses dataflow and various programming to conduct different tasks focused on NLP. Developers use this platform to build applications using various readymade tools and libraries.

4. Shogun

Platform: Open Source Machine Learning Platform
Plan Details: Free
Official Website: http://shogun-toolbox.org/

Shogun offers an extensive range of libraries for various ML tasks; it is written in C++ language and provides an interface in Ruby, Python, and Java. It is not popular among professionals, but most students use this platform as it offers ready-made APIs that are easy to manage and work with.

5. Pytorch

Platform: Open Source Machine Learning Library
Plan Details: Free
Official Website: https://pytorch.org/

Developed by Facebook four years ago, Pytorch is a great machine learning platform and framework for Deep Learning and NLP. It works best when you build, test, and run your own research prototype. Many brands these days use Pytroch but the best example you can consider here is Uber’s probabilistic programming language which is built using PyTorch.

How You Can Be Benefited From Machine Learning

When you are googling something and misspell a word while searching and hit enter, Google understands that you misspell a word and comes up with relevant results on your screen. What is it? It is possible because of machine learning. Let’s take another example when you place an order on Amazon, next time without searching; the site comes up with results based on your preferences. This is another case of machine learning.

In short, either directly or indirectly, machine learning has been taking center stage in the business world and influencing our personal chores. From healthcare to marketing to entertainment to manufacturing, everyone is harnessing machine learning capabilities as it reduces operational costs and delivers the best user experience.

In the same way, marketers leverage machine learning technology to build robust digital marketing strategies that engage customers and allow them to maximize ROI. currently, machine learning has spread its wings across all the sectors as it has offered certain advantages such as

• Helps to make an informed business decision
• Automation replaces human efforts
• Reduce operational cost
• Simplifies marketing strategy
• Recommend right products
• Prevent fraudulent activities

Machine learning helps businesses to expand their reach with the audience, improve sales and make informed decisions for long-term business success. By implementing emerging technologies such as AI and ML, brands can earn better, save money and take their business to another level.

Conclusive Thoughts

Machine learning has come into the “spotlight” suddenly, but it is one of the top value digital innovations that escalates a company’s growth process and enables them to build business models that drive personalization. Here we have discussed the meaning, types, platforms, and usefulness of machine learning in the business sector; we hope all this information helps you run a business successfully in this internet world.