# What Are Prerequisites to Start Learning Machine Learning?

There are few fields in technology which have risen as much as machine learning and data science have, in the past few years. The demand for professionals well versed in data science has more than tripled, while the field is also now one of the most lucrative profession options for any interested person.

Machine learning does require the user to have a modicum of understanding over mathematical concepts. Apart from the requisite programming skills, you will need to know some basic mathematical concepts in order to understand how various algorithms function in the backdrop. Here are some of the main topics that you need to know before you get into machine learning.

• **Basic Maths** The importance of mathematics in machine learning cannot be overstated, but the extent to which it is used depends upon the project at hand, really. Entry-level users may not need to understand a lot, because you may only have to learn how to implement the algorithms well using the tools at hand.

However, you would not understand the deeper workings of algorithms or libraries without knowledge about linear algebra or multivariable calculus. If you are serious about machine learning and want to explore **how to start learning Machine Learning**, there is no doubt that you will have to customize and build your own algorithms as you progress. This means that mathematics, especially linear algebra, and multivariable calculus is important.

• **Statistics and Probability**

Machine learning algorithms are all based on statistics and probability, at heart. Therefore, you would definitely have to have a deep understanding of statistical theory, like Bayes rule, independence, and the likes. Analysis models and distributions in statistics should also be covered, and you will have to be comfortable working with them for a long time.

Bayesian concepts to be covered while covering the basics include maximum likelihood, priors, posteriors and the entire concept of conditional probability. The frequentist way of thinking commonly used with datasets are discarded in this case – the statistical model is followed. You need to have statistical knowledge if you are planning to make a long, successful career in this.

• **Data Modeling** Data modeling refers to the process of estimating the structure of a data set, and this is done so that you can find out any variations or patterns within this. A lot of machine learning is also based on predictive modeling, so you would have to know how to predict the various properties of the data you have at hand. Iterative learning algorithms may result in errors being magnified in the set and the model, so a deep understanding of how data modeling functions is also a necessity.

If all of this seems intimidating in your quest to getting a machine learning certification India, make sure to remember that becoming a machine learning professional is not an overnight thing – it would require a certain amount of practice and experience. If you want to know more about how to learn machine learning, check out the

**machine learning courses**available on Imarticus Learning!

However, you would not understand the deeper workings of algorithms or libraries without knowledge about linear algebra or multivariable calculus. If you are serious about machine learning and want to make a **career in machine learning**, there is no doubt that you will have to customize and build your own algorithms as you progress. This means that mathematics, especially linear algebra, and multivariable calculus is important.

• **Statistics and Probability**

Prerequisites for Machine learning are all based on statistics and probability, at heart. Therefore, you would definitely have to have a deep understanding of statistical theory, like Bayes rule, independence, and the likes. Analysis models and distributions in statistics should also be covered, and you will have to be comfortable working with them for a long time.

Bayesian concepts to be covered while covering the basics include maximum likelihood, priors, posteriors and the entire concept of conditional probability. The frequentist way of thinking commonly used with datasets are discarded in this case – the statistical model is followed. You need to have statistical knowledge if you are planning to make a long, successful career in this.

• **Data Modeling** Data modeling refers to the process of estimating the structure of a data set, and this is done so that you can find out any variations or patterns within this. A lot of machine learning is also based on predictive modeling, so you would have to know how to predict the various properties of the data you have at hand. Iterative learning algorithms may result in errors being magnified in the set and the model, so a deep understanding of how data modeling functions is also a necessity.

If all of this seems intimidating in your quest to getting a machine learning certification India, make sure to remember that becoming a machine learning professional is not an overnight thing – it would require a certain amount of practice and experience. If you want to know more about how to learn machine learning, check out the

**machine learning courses**available on Imarticus Learning!