Is Machine Learning Math Heavy?


As a branch of artificial intelligence, machine learning involves building applications that allow computers to learn automatically. The algorithms in machine learning analyze a massive amount of data to find patterns. With so much data involved, the question remains, is machine learning math heavy?

Machine learning is a math-heavy subject depending on how deep you’re willing to go. The initial stages of the course don’t call for too much math. However, understanding how the algorithms really work requires a solid foundation in linear algebra, statistics, and optimization.

The rest of the article will explore topics related to the question, including what is machine learning, its application, some of the helpful mathematics courses, and some study tips to help you excel in machine learning.

Important Sidenote: We interviewed 100+ data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!

What Is Machine Learning?

Machine learning is a branch of artificial intelligence that allows computer systems to mimic the human ability to learn and understand data. These applications can access data and use it to learn without the need for explicit programming or human intervention.

The learning process starts with data or observation, such as instructions or direct experience, to form a baseline. The application uses data patterns from the baseline to learn and improve its decision-making abilities in the future.

Primarily, the aim of machine learning is to allow computer applications to learn and adjust their actions without the need for human intervention. It uses algorithms to find patterns in vast amounts of data. In this case, data refers to words, numbers, images, clicks, or anything that can be stored digitally.

Practically, machine learning is the power behind many of the popular internet services such as: 

  • The recommendation systems on streaming services on YouTube, Spotify, and Netflix
  • Voice assistants such as Alexa and Siri
  • Search engines such as Google
  • Social media feeds on Facebook and Twitter

In each case, the platform collects as much data about the user as possible, including the links you’re clicking, your preferred movie genre, and the content you like. It then uses machine learning to make highly educated predictions of what you might like next. Voice assistants match words within their database with your voice commands.

Typically, machine learning applies deep learning to find a pattern, then using the pattern.

What Is Deep Learning?

For simplicity, deep learning is an advanced form of machine learning since it uses techniques that give computers the ability to access and amplify even the tiniest patterns. It uses a deep neural network modeled after the inner workings of the human brain.

Much like neurons in the human brain, the deep neural network syncs vast layers of computational nodes to sift through the data and make a final prediction.

Types of Machine Learning Algorithms

Typically, there are four types of machine learning algorithms – supervised, unsupervised, semi-supervised, and reinforcement.

  • Supervised machine learning. It’s the most common, and it entails explicitly labeling the data to let the algorithm know what patterns to seek out. These are popular with streaming services such as YouTube and Netflix. Once you choose a show, you’re instructing the system to find similar shows, and they pop up on the recommendation list.
  • Unsupervised learning algorithms. In this case, the data has no labels and is unclassified, so the algo is left to uncover any hidden patterns. Since the input is unlabeled, machine studies how to discover hidden trends from available information. Therefore, the aim here isn’t to arrive at the correct output but to make inferences and describe the hidden structure in unlabeled data. It is highly applicable in cybersecurity.
  • Semi-supervised machine learning algorithms. As the name suggests, these are hybrid algos that use both labeled and unlabeled data. Typically, it entails using a small amount of labeled data as a baseline to analyze and interpret a huge amount of unlabeled data. Semi-supervised learning comes into play when the labeled data call for skilled resources to uncover the patterns.
  • Reinforcement machine learning. It’s the latest frontier in this field where the algorithms learn by trial and error to achieve a set objective. The machine experiments with numerous strategies and is rewarded or penalized for the results achieved. That allows the algo to determine the ideal steps within a specific context to improve performance.

Some Helpful Mathematical Courses

Machine learning has a broad scope of application and is used to solve various problems. The applications range from simple to highly complex, and that determines the amount of mathematical knowledge required.

Simple machine learning methods are based on powerful algorithms such as GLM, K-means, and SVM, which have been refined to a high degree. That makes it easy to use and apply the algos with a cursory knowledge of math.

On the other hand, complex machine learning projects require that you understand the subject at a conceptual level, calling more profound mathematical knowledge.

Strong math skills improve theoretical machine learning while making it easier to understand publications, implement new strategies, and gives you a granular understanding of the frameworks.

While you can use the readily available libraries in Python or R to build a model, you’ll be a better machine learning engineer if you have these math skills:

  • Linear Algebra. It’s a systematic representation of information in a format easily understood by a computer since all the operations are systematic rules. It’s useful to work with massive datasets with multiple variables and play a crucial role in unsupervised PCA techniques.
  • Multivariate Calculus. Also known as partial differentiation, it’s crucial for the mathematical optimization of a particular function.
  • Probability. Probability concepts, including Gaussian and Bernoulli distribution, cumulative density, and probability density functions to carry out hypothesis testing.
  • Statistics. Statistics is crucial in machine learning because it arms you with the tools necessary to extract information and uncover patterns from a given dataset. Some of the popular concepts include data spread, distributions, measures of central tendencies, and hypothesis testing.

Helpful Machine Learning Tips

  • Improve your mathematical knowledge. While you don’t need to be a maths whizz to process data in machine learning, having a statistical ground is beneficial. Statistical knowledge improves your ability to manipulate and analyze data effectively.
  • Polish your programming skills. Python and R are the most popular programming languages in machine learning. Mastering either of them improves your ability to apply data in machine learning.
  • Set specific goals. Machine learning is a broad subject that’s spread between math, code, data, and algorithms. Self-learning can be intimidating if you don’t set and follow a systematic path.
  • Set your learning environment. Having a specific study area that’s free from distractions improves your progress. It makes it easier to get into the learning frame of mind, which is great for comprehension.
  • Improve your learning skills. Rote learning has no place when learning a highly technical subject, such as machine learning. It would help if you had a deep understanding of the concepts so you can apply them.
  • Complete a data project. Applying the machine learning concepts as you learn them helps to gauge your skills and measure your progress. Start small and work your way to bigger projects as your skills grow.

Author’s Recommendations: Top Data Science Resources To Consider

Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.

  • DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
  • MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
  • Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.

Conclusion

To a large extent, machine learning is a math-heavy subject, but the level at which you dabble in math depends entirely on your interests.

Programming solutions such as R and Python can let you breeze through machine learning with a cursory knowledge of mathematics. But that will somewhat limit the scope and abilities.

A solid mathematical foundation is crucial if you wish to delve deep into machine learning and build complex projects. It helps you understand how algorithms work and function, relate to each other, and how to improve them if need be.

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed 100+ data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.

  1. Algorithms | Computer science | Computing | Khan Academy. (n.d.). Khan Academy. https://www.khanacademy.org/computing/computer-science/algorithms
  2. Artificial intelligence. (2001, October 8). Wikipedia, the free encyclopedia. Retrieved December 20, 2020, from https://en.wikipedia.org/wiki/Artificial_intelligence
  3. Deep learning. (2011, July 20). Wikipedia, the free encyclopedia. Retrieved December 20, 2020, from https://en.wikipedia.org/wiki/Deep_learning
  4. Generalized linear model. (2004, June 23). Wikipedia, the free encyclopedia. Retrieved December 20, 2020, from https://en.wikipedia.org/wiki/Generalized_linear_model
  5. K-means clustering. (2020, November 11). Wikipedia, the free encyclopedia. Retrieved December 20, 2020, from https://en.wikipedia.org/wiki/K-means_clustering
  6. Learning how to learn. (n.d.). OpenLearn. https://www.open.edu/openlearn/education-development/learning-how-learn/content-section-0
  7. Machine learning. (n.d.). IBM – United States. https://www.ibm.com/analytics/machine-learning
  8. Mathematics behind machine learning – The core concepts you need to know. (2020, April 22). Analytics Vidhya. https://www.analyticsvidhya.com/blog/2019/10/mathematics-behind-machine-learning/
  9. Meserole, C. (2019, October 25). What is machine learning? Brookings. https://www.brookings.edu/research/what-is-machine-learning/
  10. Principal component Analysis — Unsupervised learning model. (n.d.). Hacker Noon. https://hackernoon.com/principal-component-analysis-unsupervised-learning-model-8f18c7683262
  11. (n.d.). Python.org. https://www.python.org/
  12. Quora. (2019, February 15). Do you need to be good at math to Excel at machine learning? Forbes. https://www.forbes.com/sites/quora/2019/02/15/do-you-need-to-be-good-at-math-to-excel-at-machine-learning/
  13. (n.d.). R: The R Project for Statistical Computing. https://www.r-project.org/
  14. Rote learning vs. meaningful learning. (2017, March 23). Oxford Learning. https://www.oxfordlearning.com/difference-rote-learning-meaningful-learning/
  15. Support vector machine. (2002, July 27). Wikipedia, the free encyclopedia. Retrieved December 20, 2020, from https://en.wikipedia.org/wiki/Support_vector_machine
  16. What is machine learning? A definition – Expert system. (2020, October 20). Expert.ai. https://www.expert.ai/blog/machine-learning-definition/
  17. What is machine learning? (2020, June 26). I School Online – UC Berkeley School of Information. https://ischoolonline.berkeley.edu/blog/what-is-machine-learning/

Affiliate Disclosure: We participate in several affiliate programs and may be compensated if you make a purchase using our referral link, at no additional cost to you. You can, however, trust the integrity of our recommendation. Affiliate programs exist even for products that we are not recommending. We only choose to recommend you the products that we actually believe in.

Daisy

Daisy is the founder of DataScienceNerd.com. Passionate for the field of Data Science, she shares her learnings and experiences in this domain, with the hope to help other Data Science enthusiasts in their path down this incredible discipline.

Recent Posts