Present technological advancements have earned our belief that whatever we used to find fascinating in sci-fi movies is close to becoming a reality today. A decade ago, could we imagine talking to virtual assistants, watching cars driving without drivers, machines recognizing our face, or wearing smart wearables that could track our pulse? Probably not. But it is the reality today; thanks to artificial intelligence (AI) and machine learning. What’s more! These are just the initial applications of this powerful technology. We are yet to witness fully AI-powered devices and gadgets in the years to come.
The easier our tasks become through modern gadgets, the more complex is the technique working on the back end. Though we say machine learning is what works behind it, the subject is quite complicated to understand. On a basic level, machine learning is the science of allowing computers to perform human-like actions and learn through the feedback when we feed data into it. Today, machine learning training is empowering many professionals to gain expertise in the field and try out their career opportunities in this fascinating world of AI.
Read this article to know more about machine learning and how one can get a job in this field.
What is Machine Learning?
Machine learning, a subset of Artificial Intelligence, is all about making machines capable of mimicking the human brain by learning through experience. Machines are fed with some data to train them so that they can make decisions and perform tasks like humans when new data is fed into it. For example, if we want a machine to recognize different fruits, we feed a lot of images showing different kinds of fruits to train it. Through thousands of iterations, machines are trained and a new image is then fed to it. If the machine is able to recognize the fruit correctly, it has been successfully trained. If not, the training process is repeated.
Machine learning is divided into the following types:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised Learning – When the machine is fed with labeled data, i.e. an input that is mapped to the desired output, such learning is called supervised learning. When the machine is trained, the test data is then fed to it and checked if it is giving an accurate outcome. Linear regression, logistic regression, decision trees, Naive Bayes classifiers are some of the popular algorithms used for supervised learning.
Unsupervised Learning – In this type of learning, the machine is fed with unlabeled data, i.e. an input with unknown output. The data is new and when fed to a machine learning algorithm, it tries to find patterns and gives the desired result. Some of the algorithms used in unsupervised learning include fuzzy means, K-means clustering, hierarchical clustering, and partial least square.
Reinforcement Learning – This kind of learning involves an agent, environment, and action. It uses trial and error methods, i.e. the agent who is the decision-maker interacts with the environment and takes a certain action. If the outcome is as desired, the system is rewarded, else it tries another method. Over time, the system works to maximize rewards and improves.
A Career in Machine Learning
Professionals looking for an exciting career in AI can explore their opportunities to become a Machine Learning engineer. As per the insights from Dice, Machine Learning engineer was the fastest-growing job title in the world for the year 2019. Moreover, the role commanded an average annual package of $146,085 in the US, with the number of job openings growing by a whopping 344% from 2015 to 2018.
To get a job in the field of machine learning, it is better to gain some foundational knowledge to understand the ML topics clearly. You can start by brushing up your knowledge of statistics and core math concepts like calculus, linear algebra, and probability. Thorough knowledge of any one programming language like Python, Java, or R is also essential. You can then continue by mastering data science concepts like data preprocessing, cleaning, manipulating, and visualizing. When you build a strong foundation in all these areas, it would become easier for you to start with machine learning algorithms. As mentioned earlier, there are different algorithms for each type of machine learning. The more you practice applying these algorithms, the better you will understand to use them in real-world scenarios. Lastly, it is also recommended to know about big data and Hadoop as well as data visualization tools.
You will never regret once you step into the world of AI and machine learning. One of the best things about the position of an ML engineer is that the responsibilities of such professionals are still being defined. It means organizations are yet to test their credibility in many other open business problems. So the role will constantly change in the time to come and one doesn’t need to do monotonous tasks every day. In the future, machine learning engineers would need to be comfortable in three complex disciplines – data science, software engineering, and DevOps.
Could you take up this challenge and prove your worth as a Machine Learning Engineer? If yes, why not join an online training program and channelize your learning. Highly qualified mentors who design such courses allow learners to read AI and ML from beginning to advanced level with ease. Take this step today and watch your career growing in a short span of time.