Machine Learning: An overview, trends and careers

If you’re looking for an exciting, challenging, secure, and successful future, then you can consider choosing a machine learning career. And if you don’t already know, it was recently declared one of the most sought-after AI jobs, with rapid digitalization and a greater focus on advanced fields like machine learning and artificial intelligence. 

In the past four years, machine learning and AI jobs have increased by a whopping 75% and are continuously growing. This indicates that working in a machine learning job can help you secure your future and earn your dream salary by serving an industry that is increasing in demand. In this article, we will discuss what machine learning is, and how you can build a successful career in this field. Read on! 

What is Machine Learning?

In simplest terms, machine learning is a field of AI. It helps in building self-learning automatic systems that can enhance their performance with their experience and without any human intervention. This allows machines to make data-centric decisions. Whatever a machine learns from its experience and available data, it can be used for making predictions. For instance, if you have used Google maps, then you might have noticed that sometimes it shows the fastest route with less congestion and traffic. It is able to do so with the help of machine learning algorithms. 

Machine learning engineers build these algorithms in such a way that they get used to experiencing and exploring fresh data for making predictions. This quality of machine learning provides businesses and organizations with the ability to make better strategies and optimize their operations to grow and succeed in their respective industry. 

What Does a Machine Learning Career Path Look Like? 

A career in machine learning usually begins with a post of machine learning engineer. A machine learning engineer builds solutions and applications that automate tasks that are previously handled manually. Most of these are repetitive tasks that are based on specific action pairs and conditions — which a machine can perform efficiently without any error. 

Once you earn a promotion as a Machine Learning engineer, you become a machine learning architect. Individuals in this job are responsible for creating applications and prototypes that need development. Some other job roles available in the field are ML software engineer, ML data scientist, ML senior architect, and so on. 

What Skills Do You Need for Machine Learning? 

Given below are some ML areas as well as skills required by the professionals:

Statistics and Probability 

A majority of machine learning algorithms have their base in mathematical concepts like Markov models, Bayes theorem, and a few other areas of probability. Furthermore, concepts like mean, deviation, median, and Poisson distribution are also very important in ML. 

System Design

Machine learning solutions are not generally standalone in nature. Instead, they are an important part of an integrated technical economy. This is the reason machine learning professionals have a good knowledge and understanding of software design. 

ML Algorithms and Libraries

ML professionals swear by models like Bagging, Linear Regression, Boosting, and Genetic Algorithms. 

Data Modeling

As a machine learning enthusiast, you should be able to evaluate the structure of datasets in order to identify patterns, correlations, and clusters. Further, data modeling is also necessary to make sure that data models are on point. Moreover, ML practitioners should also know how to test data for its accuracy and integrity. 

Programming Languages

Finally, if you want to pursue a machine learning career then you should have great proficiency in programming languages. Note that Python is the most important programming language for machine learning. Besides, technologies like Apache Spark, SAS, and AWS, are also crucial in ML. 

Know that this isn’t the list of only skills you need to acquire to have a career in machine learning. There are several other concepts and modules that you should learn to become an ML professional. The best way to begin your career in this field is to learn machine learning by enrolling in the right machine learning course. Further, you can also consider taking a machine learning certification program from a reputed institute. 

Scope and Salary Trends of Machine Learning 

As compared to other career options, the scope and demand for Machine Learning are very high in India as well as abroad. According to a Gartner survey, there will be more than 2.3 million jobs in the field of AI and ML by the end of 2022. 

Moreover, ML jobs offer a much higher salary than other jobs. As per the Forbes report, the average salary of an entry-level ML professional is $99,007 in the US. This converts to INR 865,257 in India. 

As you can see, machine learning is a booming career field that can help you build a successful and secure future. So, don’t wait anymore and start your career in this lucrative field by taking the right machine learning course! 

Machine Learning has been a buzzword in recent years, possibly due to the large quantity of data produced by applications, the rise in processing power, and the development of better algorithms.

Machine Learning is employed everywhere, from automating monotonous operations to providing sophisticated insights; companies in every area are attempting to capitalize on it. You could already be using a gadget that makes use of it. 

Introduction to the machine learning world

You may apply machine learning in prediction systems as well. Supervised learning, reinforcement learning, and unsupervised learning are the three categories of machine learning. The computer is given a set of training data together with the intended output and subsequently learns how to produce the desired output using the training data. This process is known as supervised learning. 

When the computer gets a collection of data but not the intended result, it must learn to recognize the structure in the data on its own through unsupervised learning. Reinforcement learning is where the computer gets a set of data and a reward function, and the computer has to learn to produce the desired output to maximize the reward.

Machine learning is a powerful tool that may apply to many tasks, including predictions, voice recognition, and face and facial expression detection. 

Application of Machine Learning

 Speech recognition is the procedure of converting spoken words into text. Speech dialing, call routing, and appliance control are all examples of voice user interfaces. You may also use it for simple data entering and structuring papers.

One of the popular uses of machine learning is image recognition. It recognizes items, people, places, digital photos, etc. Facebook has an auto friend tagging recommendation option. When we submit a picture with our Facebook friends, we instantly get a tagging recommendation with their names, powered by machine learning’s face identification and recognition algorithm.

Machine learning techniques are currently widely employed in various voice recognition applications. Google Assistant, Siri, Cortana, and Alexa use speech recognition technologies to respond to spoken commands.

Machine learning is a subtype of AI that allows machines to learn from data, improve performance based on previous experiences, and make predictions. 

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