“[M]achine learning will bring about not just a new era of civilization, but a new stage in the evolution of life on earth.”― Pedro Domingos
Communication is the basic phenomena in the nature. All species communicate to each other. The languages are nothing but a medium to represent the thoughts but as technology evolves people can talk to machine also. For example Siri or Alexa, Facebook friend suggestions, Gmail spam filters, traffic congestion predictions are some common examples where one talks to a machine and all this is machine learning.
Machine Learning (ML) is emerging as one of the hottest fields today.“Machine learning is the study of computer algorithms that allows computer programs to automatically improve through experience” as defined by Computer Scientist and machine learning expert Tom M. Mitchell.
The scientific field of machine learning (ML) is a branch of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed by relying on patterns and inference instead.
How does machine learning work?
Machine learning utilizes a variety of techniques to intelligently handle large and complex amounts of information to make decisions and/or predictions. It describes computer algorithms trained with real-world data to build predictive models. An algorithm can be thought of as a set of rules/instructions that a computer programmer specifies which a computer is able to process. Machine learning algorithms are learnt by experience, similar to as humans do. For example, after having seen multiple examples of an object, a compute-employing machine learning algorithm can become able to recognize that object in new, previously unseen scenarios.
In practice, the patterns that a computer (machine learning system) learns can be very complicated and difficult to explain. Once the machine learning model has been trained, we can give different images as input to see if it can correctly differentiate between them.
Types of Machine Learning
There are different types of machine learning for different kinds of problems. There are generally two categories: supervised and unsupervised – but sometimes combination of both is also used.
Supervised Machine Learning
In Supervised Machine Learning our training data contains known, correct answers for the thing we’re trying to predict. It’s called supervised because we can easily evaluate how good our model is while it is being trained by comparing it to known correct answers. Most machine learning algorithms fall into the supervised learning category including regression, decision trees, XGBoost, and many more.
In the field of machine learning, the thing we try to predict is the label. So, supervised machine learning deals with labeled training data.
Unsupervised Machine Learning
Sometimes, we try to find hidden patterns in the data we have. The goal in unsupervised learning is generally to cluster the data into characteristically different groups. Unsupervised machine learning is more challenging than supervised learning due to the absence of labels. The unknown attributes are called latent features. Techniques such as K-means clustering, principal component analysis, latent Dirichlet allocation, and K-nearest-neighbors can be used to uncover these latent features.
Here as we don’t know the correct answers, unsupervised algorithms use unlabeled training data.
Real-world projects aren’t always so definite.
In this type of learning, the algorithm is trained upon a combination of labeled and unlabeled data. Typically, this combination will contain a very small amount of labeled data and a very large amount of unlabeled data. The basic procedure involved is that first, the programmer will cluster similar data using an unsupervised learning algorithm and then use the existing labeled data to label the rest of the unlabeled data.
Supervised learning is used to train a model that assigns labels to unlabeled data, based on the human-generated labels it receives. With some practice we can compare the labels produced by the supervised algorithm to the labels produced by humans. As they start to agree, we can use the supervised model to label our training data instead of humans in cases where the model has high confidence. Those machine-generated labels are called pseudo-labels. Since our training data now contains a mixture of known labels assigned by humans and data that was inferred by a model, these models are called semi-supervised.
Areas where ML is employed
Machine learning technology has immense advantages in the industries which are working with large amounts of data. It has been observed that the organizations working with ML are able to work more efficient and be ahead their competitors.
The two key purposes to use machine learning technology in banks and other businesses in the financial industry are to identify important insights in data, and prevent fraud. It can also identify investment opportunities, or help investors know when to trade. Data mining can look for clients with high-risk profiles, or use cyber surveillance to prevent from any fraud.
Government agencies such as public safety and utilities utilize machine learning by collecting data through sensors from various inputs to fetch desired results. Machine learning can also help detect fraud and minimize identity theft.
Machine learning is growing at a fast pace in the health care industry, with the addition of wearable devices and sensors that can use data to assess a patient’s health in real time. The technology can help the medical field to analyze data to identify trends that may lead to better diagnoses and treatment.
When we online shopping on any website it provide us recommendations for the items we might like based on our previous purchases using machine learning. Retailers use machine learning to capture data, analyze it and use it to personalize a shopping experience, implement a marketing campaign, and merchandise supply planning.
Machine learning can be used to finding new energy sources, analyzing minerals in the ground, predicting refinery sensor failure and streamlining oil distribution to make it more efficient and cost-effective. The number of machine learning use cases for this industry is vast – and still expanding.
The data analysis and modeling aspects of machine learning are of great importance to delivery companies, public transportation and other transportation organizations. Machine learning is utilized to analyze data to identify patterns and trends in the transportation industry, which helps in making routes more efficient.
Career Opportunities in ML
There are some of the best engineering colleges in Delhi NCR which offer 100% Placement .
Job opportunities in Machine Learning
Machine Learning Engineer – They are sophisticated programmers who develop the systems and machines that learn and apply knowledge without having any specific lead or direction.
Deep Learning Engineer – They are specialized in using deep learning platforms to develop tasks related to artificial intelligence.
Data Scientist – They extract meaning from data and analyze and interpret it. It requires methods, statistics and tools.
Computer Vision Engineer – They are software developers who create vision algorithms for recognizing patterns in images.
Machine learning has already and will change the course of the world in the coming decade.
Therefore, there is a huge scope of Machine Learning in India, as well as in other parts of the world, in comparison to other career fields when it comes to job opportunities. According to Gartner, there will be 2.3 million jobs in the field of Artificial Intelligence and Machine Learning by 2022. Also, the salary of a Machine Learning Engineer is much higher than the salaries offered to other job profiles.According to Forbes, the average salary of a Machine Learning Engineer in the United States is US$99,007. In India, it is ₹865,257.Thus, the future belongs to the Machine Learning, and one has a bright future if he/she becomes an ML professional.
“Machine learning will automate jobs that most people thought could only be done by people.” ~Dave Waters