In the last few months, we have had several aspirants contact us about their intense interest in venturing into the world of AI and machine learning to build flawless data-driven products and solutions. However, we have observed that most of them actually are not sure of the foundational skills and education required to pursue a career in machine learning.

*I am a die-hard coder and know the basics of mathematical modeling. Can I become a machine learning engineer?*

*I love number crunching but do not have a Ph.D. Can I pursue a career in machine learning?*

*I have a high affinity towards statistical analysis but don’t have a master’s degree? Can I learn machine learning?*

These are some of the many questions counselors at Springboard get asked when counseling aspirants on machine learning careers. This article is for all those wondering where to start learning and understanding the prerequisites for machine learning. Jumpstarting your machine learning journey is an uphill battle but not to forget machine learning engineer is the KING so building a solid foundation is crucial to foray into a machine learning career.

**Educational Prerequisites for Machine Learning**

**Do I need a Master’s/Ph.D. to become a Machine Learning Engineer?**

This is the most common question asked by most of the aspirants with regards to understanding the educational qualification prerequisite for machine learning. The answer is a big NO. Your machine learning career will not be limited by not having a Ph.D. though having a Ph.D. might have benefits of its own based on the machine learning job role. There are countless talented and successful machine learning engineers who do not have a Ph.D. Any educational degree, be it Master’s or Ph.D. is just a certification of your skills to showcase that you can deep dive into a concept in a scientific way. For instance, someone who completes graduation and goes into an entry-level machine learning job will have 4 to 6 years of industry experience by the time their peers finish their Ph.D. Even if you do not tick the educational qualification checkboxes that organizations ask for in a machine learning engineer, you can still make that transition into machine learning.

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**Skills Pre-requisites for Machine Learning**

Though it is not mandatory, however, it is helpful if you have knowledge of the following concepts to grasp machine learning faster. However, if you are unfamiliar with these concepts, learning from books, online resources, blogs, video tutorials, or free courses is the solution to get hold of these topics and move forward on your journey to becoming a machine learning engineer.

Here is a quick refresher on machine learning prerequisites for people starting from scratch –

**Machine Learning Prerequisite #1 – Maths**

If you’re interested in pursuing a career as a machine learning engineer, you don’t require to have an in-depth understanding of a lot of advanced mathematics to get started with machine learning. However, you’re are not completely off the hook. Math concepts are still prerequisites for machine learning and so as for the data science. A thorough understanding of mathematical concepts like linear algebra, calculus, probability theory and statistics is necessary to gain a solid understanding of the internal working of the algorithms. For instance, math concepts like differentiability and continuity are widely used in ML algorithms. Strong hold of math concepts helps select the right ML algorithm which includes consideration of training time, number of features, number of parameters, model complexity, and accuracy.

A fundamental understanding of math concepts definitely helps to reason with machine learning more productively but is not a mandate for entry-level ML practitioners. In most cases particularly if you’re getting started with an entry-level machine learning engineer position, you don’t need to do the hard math. There are many machine learning libraries in Python (SciKit Learn) and R (caret package) that deal with hard math like calculus and linear algebra to get the algorithms to work.

**Machine Learning Prerequisite #2 – Statistics**

Statistics and machine learning are two tightly coupled fields. Most of the machine learning techniques and algorithms are either completely borrowed from or depend on various theories from statistics making it an essential prerequisite for machine learning. Statistical methods help transform data into information and find answers to questions about samples of data. Here’s why you need to know Statistics for Machine Learning –

- Basic understanding of descriptive statistics and data distributions helps identify the right methods for data preparation.
- Statistical hypothesis tests help choose the right model for any kind of a predictive modeling problem.
- Data sampling, resampling, and experimental design techniques help evaluate a machine learning model and understand the rationale as to why a specific method is required or not.

**Machine Learning Prerequisite #3 – Programming**

Programming is an integral part of machine learning but there is a lot more to it than just programming. A little bit of programming is enough like knowledge of object-oriented concepts, memory management, data structures, and algorithms. Programmers usually like to implement the algorithms by themselves so that they can make the most from an algorithm by customizing it according to a given problem and business requirements. The amount of coding required depends on whether you are planning to use ML or develop new ML algorithms and the question you are trying to answer using machine learning. Using existing codes one might get away with minimal coding but if you are developing a model from scratch then you might have to do a lot of coding right from making the basic assumptions to running the algorithms.

So, what if you are a non-programmer? Even non-programmers can also get a long way without even writing a single line of code – all thanks to the fantastic graphical and scripting machine learning environments like Weka, BigML, Orange, Scikit-Learn, and Waffles. These platforms help with data preparation, data pre-processing, configuring algorithms, running them, and reviewing results. You don’t need to be a skilled programmer to make progress in the field of machine learning However, having a solid mathematical foundation will undoubtedly offer you an edge while pursuing a job as a Machine Learning Engineer or Data Scientist.

As a beginner, these are the prerequisites for machine learning that you have to have to get started with machine learning. If you put together and develop these machine learning prerequisites, the rest should follow as you transition to a career in machine learning. Take the next step and check out Springboard’s machine learning program that helps you think and explore the broader and practical aspects of machine learning.

**Since you’re here…**Are you interested in this career track? Investigate with our free guide to what a data professional *actually* does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!