In order to learn Machine Learning: Being thorough with probability and its applications A good knowledge of statistics, building on the first point Knowing linear algebra and elementary algebra An understanding of trigonometry Working knowledge of calculus Dexterity with a programming language

If you do not know any of the above, make it a point to learn them thoroughly so that you do not face any roadblocks while going through any given course in 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.

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.

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.

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.

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

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.

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.

Building on probability and combining it with the good old mean, median, mode, variance, standard deviation etc (summarized aptly as “measures of central tendency”), knowing the types of distributions is needed at the outset.

The normal distribution and the Student’s T are the ones that greet novices. One gets to work with these in detail, while building a clear intuition of how data is represented and found to work. Hypothesis testing is an essential, which naturally leads on to z-score, t-score etc and confidence intervals.

Anyone with a background in science or engineering will tell you that calculus is one tool that forms a mainstay of many of the theoretical aspects of their work. The same goes for machine learning - in general - and deep learning, in particular.

It is used scantily at the basic level; knowing *gradients* and *partial derivatives* allows you to make sense of backpropagation. While you can make do without knowing it well (Andrew Ng says so!), knowing it can help build a deeper understanding of what happens under the hood.

Machine learning has flourished mainly because of the capabilities provided by powerful programming languages like** Python**. While you can go with any language you deem fit, Python is the número uno choice. It is used prodigiously for its inclusion of a large number of libraries/modules - as reflected by its *batteries-included* motto.

While you do not need to be a coding whiz, you definitely need to be able to understand the basics of variables, data types, functions and using libraries. Eventually, you’ll be working with everything ranging from insightful plots to deep nets, which is to say that you’ll need to be open to learning along the way.

About AuthorPickl.ai is the educational vertical of TransOrg Analytics, which is a leader in working out Big Data and Machine Learning solutions for transforming businesses. Pickl’s goal is to democratize education in data science and machine learning for all.

We recognize that a *one size fits all* approach doesn’t work for the unique requirements of learners. Thus, we provide curated courses for high-schoolers, college goers, graduates and working professionals. Learning is supplemented with interactive assignments, live classes and project formulation.

Placement assistance is also provided in the form of mock interviews and resume-building sessions. To top it all, the instructors are seasoned data veterans, who have decades worth of experience in grappling with the challenges of the fast-changing domain.

Check out our flagship course here and begin your machine learning career today!

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