What is Machine Learning?
The ability of machines to perform complex tasks without being explicitly programmed to do so is termed as machine learning. It is a multidisciplinary domain that lies at an intersection of subjects like computer science, algebra, statistics, calculus etc. It is a subset of artificial intelligence, a field of study that has fascinated humans for decades now.
Abbreviated as ML, it encompasses various techniques and consists of a wide variety of tools for specific purposes. Regression, classification, deep learning, random forests, neural networks…, and the list goes on and on. It is said to have over 14 types even though this is contended; there is much more agreement on classifying it into three main subtypes:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Machine learning is lauded for making data useful at a large scale. While companies use it as a tool to improve services for their customers and to maximize profits and fulfill business goals, scientists use it for solving complex problems and discovering novel solutions that enrich human life.
Where is Machine Learning used?
It has come to occupy an important position in the 21st century lifestyle, with almost all the apps and technical services we use utilizing it in one way or the other. From the curated feeds on platforms like Facebook, Instagram, Quora, Reddit etc to product recommendations on websites like Amazon and Flipkart, machine learning and its use cases can be found everywhere.
Your favorite search engine relies heavily upon machine learning in bringing you credible and useful search results. Navigation services use it to provide reliable traffic predictions. Weather forecasts are fast abandoning large and cumbersome models from meteorological science and replacing them with ML-based approaches.
The so-called tools of the future like language translators, text-to-speech engines, computer vision and self-driving cars all rely upon it. All in all, machine learning pervades our everyday life and is widely recognized as a changemaker that is already enhancing our lives.
Why do people study Machine Learning?
Just like the industrial revolution and the age of the internet, machine learning has taken the job market by storm. The sheer number of applications machine learning has, has led firms to scramble for professionals who are skilled and have domain expertise.
The global Machine Learning market is expected to grow to $209.91 billion by 2029, at a compounded annual growth rate of 38.8%, considering 2022 as the base year. A lot of opportunities are opening up everyday, across tech companies, banking and financial services organizations, new-age startups and legacy giants alike.
This has led to a proliferation in degree programs and courses that claim to make anyone an exponent of machine learning. However, this is not completely true as there are various criteria one needs to meet in order to learn machine learning satisfactorily. These are listed below.
Necessary Prerequisites for Machine Learning
Being a multidisciplinary field, ML is highly technical. There are six broad areas where expertise is needed to build good fundamentals for a career in machine learning.
High-school algebra and linear algebra
Math is used prodigiously in machine learning. Thus, having a good grasp on algebra is a must. The
focus points include linear equations, logarithms, tensors, matrices and their multiplications and functions.
- Data is represented in the form of matrices/tensors;
- There is a wide usage of transformations for ensuring that models work
- Representation of relations in the form of equations is extremely common
Elementary trigonometry
While triangles (and geometry) aren’t actively related to Machine Learning, trigonometry basics are specifically required for understanding a specific kind of activation function called tanh in neural networks, which in itself is quite an advanced topic.
Nevertheless, a strong grasp of trigonometry is an indication of having sound basics, which will definitely hold a new learner in good stead.
Probability
Probabilistic models and the theory of chance is considered to be a bedrock for statistics, which in turn is fundamental for machine learning. The notion that outcomes need not be discrete goes a long way in the domain and thus, needs to be internalized.
Starting from simple probability, one needs to inculcate concepts as advanced as conditional probability and Bayes’ theorem. You’d be learning more with time but these are the ground basics that are necessary.