It was in the year 2015, one morning, while trying to plan my higher education, I came across an article in a magazine ‘Computerworld’ titled “Can Machine Learn”. I found the topic very interesting and started reading more about it. Artificial Intelligence and machine learning since then, have become two terms which are trending but these two term are also much confusing.
As Herbert Simon has defined Artificial Intelligence (AI) as –
“We call programs intelligent if they exhibit behaviors that would be regarded as intelligent if they were exhibited by human beings.”
Then how can we describe machine learning because the term is in close relation to Artificial Intelligence. Let us dig deep into this
- What is machine learning?
- How it is related to Artificial Intelligence?
- What are the basic if someone needed to start machine learning?
- What are the career aspects in machine learning?
Machine Learning: What it is?
Lots of books have defined this term is many ways. The famous book “Machine Learning” written by Tom M. Mitchell [3] defines
“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”
The definition itself explain many terminologies like Experience denoted by E, Class of tasks denoted by T and performance measure denoted by P which may not be understood by a laymen.
Hence as a layman we can say machine learning is a concept which makes a machine able to learn.
For making a machine able to learn we need to feed the machine with some past data based on some specific tasks and measure how it is performing when new specific task are assigned to it. If same behavior exists in past then only the machine can predict if the same task will happen again. Prediction mainly depends on past cases.
How Machine Learning & Artificial Intelligence are related?
Nowadays, Artificial Intelligence (AI) is becoming a major tool to map thought process with computational model. Artificial Intelligence (AI) captures, shares, develops and transform knowledge as per the desired format of organization. It originates from Greek mythology of artificial beings with thinking ability. This terminology is introduced by John McCarthy almost 30 years ago. But the journey started much before that. Vanner Bush put forward a system which turn up and understand human’s mind. Many computational models have been proposed using software tools to capture intrinsic knowledge associated with application domains.
Artificial Intelligence is a domain or field that can be handled by several sub-fields like vision, robotics, speech processing, expert systems, natural language processing, machine learning. Machine learning are mainly some algorithms that recapitulate over large datasets. They are mainly handling the task of prediction, classification and clustering. They analyze patterns in data and can help to produce reliable result from history. Nowadays as a result, numerous sectors are being associated with machine learning like automobiles, health, entertainment, cooking, e-commerce, etc.
Day by day as artificial intelligence is replicating human though process in terms of analyzing, learning and decision making. Machine learning systems are helping human race in many ways
- Less error prone than humans, if coded properly
- Able to do repetitive and tiresome tasks without getting tired or bored, since machines do not have any feelings or emotions.
- More organized than humans
- Memorizing capability is exceptionally higher
- High capability in detecting frauds, assisting human and integrating power with other technologies
Not compulsory but requirement will be helpful
Statistics
Machine learning techniques and algorithms are mostly borrowed from or dependent on statistical theories. Statistical distributions help to understand the variations in data. Some knowledge of descriptive and inferential statistics is helpful to extract and gather some meaningful information from data.
Linear Algebra
The subjects include many topics like vectors, matrices, transformations to understand the internal working of the algorithms. But no harder mathematics are required. Nowadays libraries like scikit-learn in Python and caret in R is dealing with hard core mathematics which makes implementation of machine learning algorithms easy. [1]
Calculus
To gain deeper insights about advanced machine learning algorithms like deep learning, neural network, knowledge of calculus like gradient descent, nonlinear functions are helpful.
Probability
Probability itself is a huge area, but it acts as a pillar in the field of machine learning. Some concepts like maximum likelihood, Bayesian probability, probabilistic graphical models includes some skills of probability.
Programming Language
Learners of machine learning have a big question regarding what programming language is best?
R or Python. To answer this debatable question I always suggest the choosing of the language depends on the particular task. For what purpose you are going to implement machine learning according to that you have to choose your coding language. Beside R or Python environments like Weka, Scikit-Learn are mostly used by non-coders who want to implements machine learning algorithms to perform their task.
Career Aspects in Machine Learning
Start-ups and big tech giants are tending towards artificial intelligence and machine learning for decision making and predictive projects. Pay scale of machine learning engineer in the United States is $100,956 per year as reported by the top American website that provides salary and compensation information about different companies [2] The salary of a data scientist with machine learning skills in India is around 9 lacs and whereas in the US it is around $92,000[4].
“According to a Tractica Report, AI driven services were worth $1.9 billion in 2016 and are anticipated to rise to $2.7 billion by end of 2017 of which 23% of the revenue comes through machine learning technology.
A report from TMR mentions that MLaaS (Machine learning as a Service) is expected to grow from $1.07 billion in 2016 to $19.9 billion by end of 2025.”[5]
Conclusion
Lastly I want to conclude that this digital era i.e machine learning is going to create a boom in job opportunities for upcoming professionals. Applications in sector of cyber security, recognition, recommenders etc are looking for machine learning experts for better delivery in services in form of prediction or decision making. Besides all these, sectors like healthcare, manufacturing, production, human resource, marketing etc have good opportunities to develop forefront applications. We have to understand that in order to make machine learn in a much efficient way, we human beings have to understand the subject in best possible way.
References
[1] Sakshi Gupta. (2019, October) Springboard blog. [Online]. https://in.springboard.com/blog/prerequisites-for-machine-learning-to-get-started/
[2] PayScale. [Online]. https://www.payscale.com/
[3] Tom M. Mitchell, Machine Learning.: Mc Graw-Hill education.
[4] (2019, May) Edureka. [Online]. https://www.edureka.co/blog/machine-learning-career/
[5] (2017, November) ProjectPro. [Online]. https://www.dezyre.com/article/why-you-should-learn-machine-learning/362
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