In today’s technology-driven world, machine learning and data science have become desirable career options as everyone right from college students to professionals want to switch their careers and join the data science bandwagon. The first prerequisite that comes to our minds when we talk about making our way into the machine learning or data science career is being able to delve into heavy-loaded mathematics. If you are someone who is weak in Mathematics, does it mean that you must lose all hope and stop dreaming about machine learning? No, of course not. You can still get into the field of data science. But with a mathematical understanding, you will be able to grasp the inner workings of the algorithms better to obtain good results.
In this article, we will look at the real picture and analyze if it is mandatory to learn mathematics for a successful career or machine learning, or if we can even do without it.
What is Machine Learning?
This refers to machines being able to learn by themselves without being explicitly programmed. It is an application of AI which enables systems to learn and improve from experience automatically. While working with machine learning, various sets of algorithms are required. These algorithms use a set of training data to enable computers to learn.
Why is Mathematics a Prerequisite For Machine Learning?
- Machine learning deals with learning and grasping things fast. Using rigorous mathematics, data scientists can pick up techniques and intuitions very fast and accurately. They will have an easier learning curve.
- Machine learning experts are essentially statisticians, and most of them have graduate-level knowledge of statistics and Maths. It is needed for applying algorithms and hypothesis testing.
- Standard tools like decision trees, confidence intervals, or logistic regression require heavy mathematics. A majority of the employers use standard tools. Due to this, hiring managers look for candidates who have a strong mathematics background, especially for historical reasons.
- The academic training that is given to data scientists is heavy in mathematical concepts.
Role of Mathematics in Machine Learning
Some of the reasons for why the mathematics of machine learning is important are:
- Choosing parameter settings and validation strategies.
- Estimating the correct confidence level and uncertainty.
- Identifying overfitting and underfitting by understanding the Bias/Variance tradeoff.
- Selecting the right algorithm, and this includes giving considerations to training time, accuracy, number of parameters, number of features, and model complexity.
You can find many machine learning courses online, but there are only a few that will be up to the mark and offer you the perfect knowledge and skill needed to excel in a machine learning job. One such renowned online learning platform that offers comprehensive training in machine learning is the Global Tech Council. Quality is one factor to which Global Tech Council gives utmost importance. It is this determination to deliver superior quality, which has transformed Global Tech into a world leader of technical certifications. These certifications are accepted across the globe. They offer certifications in Artificial Intelligence (AI), Cyber Security, Internet of Things (IoT), Big Data, augmented reality, machine learning, virtual reality, and much more.
The Certified Machine Learning Expert course offered by them will provide you with profound knowledge in concepts such as introduction to Python and its libraries, supervised learning with respect to regression, clustering, and classification, machine learning, and time series analysis.
The amount of Mathematics that is necessary to excel in machine learning is multi-dimensional and greatly depends on the experience and interest of the individual. Some of the mathematical concepts a machine learning professional must be aware of are probability theory and statistics, linear algebra, multivariate calculus, and algorithms and complex optimizations. Some of the other maths topics that you can learn apart from these four are real and complex analysis, information theory, and function spaces and manifolds.
Conclusion
Bottom line, it is possible to be a functional data scientist without being a mathematical wizard. The reality is that, in the industry, data scientists spend a huge amount of their time in data cleaning, data exploration, and data gathering. The truth is that 80% of them are engaged in data visualization and data wrangling.
On a general note, machine learning enthusiasts will, in most cases, be a novice in Maths but the fact is that, if you are a beginner, you will not require a high amount of mathematics while starting your career in machine learning. You can become a data scientist even if you are not very skilled in mathematics, but the fact is that, in the long-term, one will certainly struggle to be an effective practitioner without having a certain level of concrete mathematical literacy. Hence, a thorough knowledge of mathematics is needed if you aim to work in the machine learning sphere for the rest of your life.
To enroll in machine learning certifications and become a machine learning expert, check out Global Tech Council.
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