The power of Artificial Intelligence (AI) and Machine Learning (ML) has, no doubt, been seen across the globe in diverse sectors in the last decade. Notable achievements like cost reduction, faster adaptation to evolving markets, improved problem solving, and informed decision-making mean that ML adoption across industries can only soar. Machine-learning-driven solutions have penetrated the healthcare, manufacturing, education, retail, and IT industries in many ways, with its revenue projected at $80.3billion by 2023 with a CAGR of 33.6% from 2020. At the same time, the broader AI field has grown tremendously to be worth over $422.37 billion as of 2022, with projected growth of 39.4% CAGR between 2022 and 2028, according to a forecast by Zion Market Research.
It is hard to come by enterprises that have not employed AI in their overall strategies, considering implementing it directly or outsourcing its development. Organizations that take time to adopt AI may be disadvantaged greatly and may not manage to catch up with their counterparts if they do. Therefore, as the demand for machine learning skills continues to rise, it is best to have a basic understanding of statistics and R through a machine learning with R free course if you are new in the field and still undecided about pursuing ML as a career or not.
Machine learning (ML) is the branch of AI founded on building computer algorithms that are trained to become accurate at predicting outcomes without explicitly being programmed to do so. ML algorithms are built on historical data as input and then become more accurate at predicting output as more input data is fed into the system. Today, we see the applications of ML all around us.
For instance, when you interact in real-time with a chatbot agent on a website, get personalized treatment based on your lifestyle and medical history, product recommendations on your search on Amazon, or a personalized newsfeed on Facebook, this is a machine learning technology at work. Machine learning has become a part of our lives, and we have witnessed firsthand how machine learning algorithms improve our experience. Yet, the full potential of machine learning is far from being realized. Machine learning has become core to enterprise operations.
Machine learning and R
As we have seen, ML is a data analysis technique that models algorithms that learn from data to discover patterns and make accurate predictions without human intervention. R is one of the most popular languages used for machine learning and statistical analysis that provides a range of tools, libraries, and packages for mining, exploring, modeling, analyzing, and visualizing vast volumes of data.
R is an open-source language developed by statisticians and developers to help them make statistical inferences faster and more efficiently. R excels in advanced analytics and modeling and has been adopted by several companies for their machine learning, data analysis, and data science projects.
R popular packages for machine learning include:
- Dplyr package for summarizing tabular data in machine learning through the Select, Filter, Arrange, Mutate, and Summarize functions
- Caret package for training machine learning predictive models like the classification and regression models.
- Nnet library for modeling neural networks
- ggplot2 package for visualization tasks
- randomForest package for implementing ensemble decision trees for non-linear classification tasks
- XGboost package provides a parallel boosting for implementing decision tree models used in classification, regression, and ranking tasks.
The state of machine learning with R in 2023
One thing is for sure, data explosion, as has been witnessed in the past decade, is not just about to slow down. By 2025, it is expected that the amount of data created in the world will surpass 180 zettabytes. Artificial intelligence and machine learning have proved to be the tools for crunching such vast volumes of data efficiently across different sectors. Enterprises today have their eyes on innovative tools that they can leverage to take advantage of new market trends and evolve fast.
Notable machine learning trends
Some notable trends that we can expect in the near future are:
- Data explosion will more than double
The amount of data generated will continue to increase. According to the IDC forecast, the amount of data generated by 2025 will be more than 175 zettabytes. For this reason, cloud-based tools like machine learning and RStudio will be preferred for their capability to manage such vast volumes of data. It is expected that innovations within the machine learning field will be significantly useful in the big data revolution. Larger datasets will be used to build even more accurate predictive and deep learning models.
- The emergence of embedded ML and analytics-driven embedded systems
Embedded systems are common in the fields of IoT and microcontrollers. An embedded system refers to a small computer system (processor + memory) embedded in an output device to control a specific function that is part of a larger system of the device.
In analytics-driven embedded systems, analytics is done in the cloud or within the system to enable real-time analytics. This may include predictive analytics to inform certain actions that the devices take, such as preventing downtime or crashing of the systems. ML plays a significant role in these systems as it is used to model analytic algorithms that run controls in real-time. The datasets used are usually very large as data is drawn from multiple embedded devices such as IoT devices and other non-embedded sources.
There are more than 250 billion microcontrollers in the world today, and embedded machine learning powered by statistical analysis languages like R and Python is the next big thing. We expect to see more revolutionary innovations in robotics, healthcare, IoT, automotive, manufacturing, and retail fields. Simply put, embedded machine learning empowers devices with the kind of capability required to make intelligent decisions.
- Machine learning and quantum computing
For many years, AI has been the rage. But quantum computing is now emerging as the field to watch out for. To date, computers still use binary code using bits represented only by ones and zeros for data manipulation and analytics. However, with qubits as the basic unit of information in quantum computing, the next generation of quantum computers, it is believed, will have unimaginable processing power. Quantum computers can run calculations to solve multiple problems simultaneously.
As of 2018, there were only 11 quantum computers in the world. With the massive explosion of data, and this data is expected to rise exponentially in the coming years, it is projected that there will be between 2,000 and 5,000 quantum computers by 2030, with the global quantum computing market worth $949 million by 2025.
Quantum computing is a disruptive technology. More disruptive is the combination of AI and quantum computing in what is known as quantum machine learning which integrates quantum algorithms in machine learning programs. While this is still a field under research and may take a longer time before implementation and adoption, the future is promising with quantum computing and machine learning.
The growth of data is an important key driver in the growth of the machine learning field. Going forward, the more quality data we can capture, the more accurate machine learning algorithms will be. In 2023, attention is not on volumes but on the ability to process the volumes of data into high-quality and high-integrity data for optimal data-driven decision-making. Enterprises must begin to see data as a strategic asset to realize the power of machine learning as technology and R as a data analytics tool. We cannot even begin to compare the value contained in data to the cost incurred in its management and storage. As machine learning gains traction, its computer vision and Natural Language Processing subfields are something to watch out for.