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Top 5 Emerging Machine Learning Trends in 2021

Top 5 Emerging Machine Learning Trends in 2021
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Machine Learning is one technology that has grown in popularity over time! Whether you are connected to the tech industry or not, Machine Learning and Artificial Intelligence are no more undetermined to the world.

Machine Learning is being used by an increasing number of companies, including Google, Netflix, etc. In fact, smaller companies are also using ML algorithms to extract insights from data. According to market research, the global machine learning market will grow from $7.3 billion in 2020 to $30.6 billion in 2024. It also indicates that this technology will be on the rise in 2021.

According to a study, 77 percent of the devices we currently use have ML. Artificial intelligence services are promulgating avant-garde, innovative solutions for organizations and regular day-to-day existences. There are numerous innovations and Machine Learning Trends expected to emerge in 2021. There are already many applications of Machine Learning in the industry, including its integration with the Internet of Things, as well as its more widespread use in industries such as cybersecurity, health, finance, and so on.

Machine Learning and Artificial Intelligence are improving the customer engagement experience, which clearly demonstrates that ML as technology is gaining traction. Here in this post, we will look at the key Machine Learning trends for 2021 that could change the world for the better.

Take a look…..

1. The Intersection of ML and IoT

IoT is already a well-established technology in which multiple devices or "things" are linked across a network and can communicate with one another. These devices are becoming increasingly common.

According to research, there will be more than 64 billion IoT devices by 2025. All of these devices gather data, which is then analyzed and studied to gain useful insights. That is where Machine Learning comes into play! Machine Learning algorithms is used to transform data collected by IoT devices into actionable results.

Several industries, including manufacturing, smart homes, automotive, oil and gas, and so on, are implementing IoT-based systems to provide better services to people while also improving processes and their efficiency. Machine learning is crucial in the development of Internet of Things solutions in this regard. When it comes to IoT, all of the machine learning takes place in the cloud. Concepts such as predictive engines, also known as rule-based engines, make it possible.

2. Artificial Intelligence & Machine Learning

AI engineering is on the all-time rise today! This is a significant development because the integration of Artificial Intelligence and Machine Learning in the industry has been ad hoc. Artificial intelligence (AI) began as a subfield of computer science with the goal of solving tasks that humans can perform but computers cannot (for instance, image recognition). AI can be approached in a variety of ways, such as by writing a computer programme that implements a set of rules devised by domain experts. Hand-crafting rules is now a time-consuming and laborious task.

The field of machine learning – originally considered a subfield of AI – was concerned with the development of algorithms that allow computers to learn (predictive) models from data automatically.

AI Engineering strategy for a company ensures that a machine learning algorithm provides great performance, reliability, and scalability, ensuring a return on investment in AI.

3. Automated Machine Learning

Automated Machine Learning is the next stage of Machine Learning development! Using Automated Machine Learning, these data scientists can create Machine Learning models with greater efficiency and productivity while maintaining high quality.

AutoML is capable of training high-quality custom machine learning models for classification, regression, and clustering. It can easily deliver the right amount of customization without requiring a detailed understanding of Machine Learning's complex workflow.

It can also aid in the use of best practices in machine learning while saving time and resources. One example of AutoML is Microsoft Azure's automated machine learning, which can be used to build and deploy predictive models.

4. Reinforcement Learning

Reinforced Learning (RL) will be widely used by businesses in the coming years. It is a one-of-a-kind deep learning application that uses its own experiences to improve the effectiveness of captured data.

AI programming in reinforcement learning is set up with various conditions that characterize what type of activity the software will perform. Based on previous actions and results, the software self-learns actions to take in order to achieve the ultimate ideal goal.

A chatbot that handles simple user queries such as greetings, order booking, and consultation calls is an excellent example of reinforcement learning. Machine Learning Development Firms can utilize

Reinforcement Learning to make the chatbot more creative by adding sequential conditions to it. Identifying prospective customers and routing calls to the appropriate service agent is one of the examples of

RL. Other applications of RL include industrial automation, business strategy planning with robotics, robot motion control, and aircraft control.

5. Machine Learning in Cyber Security

One of the most popular industries where Machine Learning is used is the Cyber Security industry. Machine Learning has numerous applications in cyber security, including identifying cyber threats, improving available antivirus software, combating cybercrime, etc.

Apart from this, Machine Learning is also used to create Smart Antivirus software, which can identify any virus or malware. As a result, the Smart Antivirus can identify older threats from previously encountered viruses as well as new threats from recently created viruses by analyzing their auspicious behavior. Many companies are incorporating Machine Learning in Cyber security, with the most common examples being CloudMinds (focused on developing cloud intelligence-based applications to support cloud-connected smart machines), Chronicle, a cyber-security company managed by Alphabet (Google's parent company), etc.

Conclusion

These are the Machine learning trends in 2021. With more and more companies integrating the Machine Learning trend is expected to witness significant growth in the coming years.

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