Let me talk about myself. I have been running away from math’s since my childhood. It’s not that I didn’t liked it but because no matter how much I have worked hard. I always got less marks. But it was the same when I started perusing engineering – Frankly speaking I thought now math’s will not follow me because I have completed my 12th grade. It followed again and again in engineering semesters and I got excellent marks in my engineering career in mathematics and that was the time i realized it was not me. It is how it has been taught to you or how you analyze things. Here comes the change in my life when i became a coding enthusiast with math’s on top notch.

Math has become part of our day-to-day life. From the time we wake up to the time we go to bed, we use math in every aspect of our life. But you may wonder about the importance of math in Machine learning and whether and how it can be used to solve any real-world business problems.

Why do I need to Learn Math?

Whatever may be your goal, whether to be a Data Scientist, Data Analyst, or Machine Learning Engineer, your primary area of focus should be on “Mathematics”. When it comes to real-world scenario, it’s is the basic building block to solve all the Business and Data driven applications.

Starting from analyzing company transactions to understanding how to grow in the day-to-day market or making future stock predictions of the company to predicting future sales, Math is used in almost every area of business. Math’s is all over and is applied in many Industries like Retail, Manufacturing, IT to bring out the company overview in terms of sales, production, goods intake, wage paid, prediction of their level in the present market and what not.

Viewers – Okay I got it, but the question is still answered – where is math’s used in Machine Learning?

There are 4 pillars are machine learning and not surprisingly they are the ones with were the most challenging and scoring area’s in mathematics in your school grades or college degree

Statistics:

Statistics is mostly used in machine learning for drawing conclusions from data. It deals with statistical methods of collecting, presenting, analyzing and interpreting the Numerical data. In machine learning all most every time you have to deal with a large amount of data and statistics s a key factor behind growth and development of an organization.

In machine learning – The data that is collected contains noise, improper data, null values, outliers etc. which need to be cleaned and then transform it into a meaningful observations.

The data should be represented in a suitable and concise manner. It is one of the most crucial steps as it helps to understand the insights and is used as the foundation for further analysis of data.
Analysis of data includes Condensation, Summarization, Conclusion etc., through the means of central tendencies, dispersion, skewness, Kurtosis, co-relation, regression and other methods.
The Interpretation step includes drawing conclusions from the data collected as the figures don’t speak for themselves. These all comes under stats and mathematics.

Data Analysts role in any organization is to draw conclusions from the data, and for this he/she requires Statistics and is dependent on it.

Probability:

In ML, probability plays a crucial role in decisions making based on uncertain or incomplete information. Now, imagine you have a coding or a program that predicts whether an email is spam or not. This program can use probability to determine the likelihood that certain words or phrases in an email indicate spam.

For example, if the word “free” appears in an email, the program might assign a higher probability that the email is spam. On the other hand, if the word “work” appears, it might assign a lower probability of spam. By using probabilities, the program can make more accurate predictions and classify emails more effectively.

Calculus:

It is like a tool that helps us understand how things change. In machine learning, it is used to find the best way to adjust your models based on the data you have. This adjustment process is really crucial because it will help your models make better predictions.

Let us take an example to understand it better

Differential Calculus splits the given data into small pieces to know how it changes.

Real-life Example: Imagine you have a simple model that predicts the price of a house based on its size. Calculus comes into play when we want to improve the accuracy of this model. It helps us figure out the smallest change we can make to the model to better predict house prices. This process of fine-tuning the model is essential for making accurate predictions in real estate.

Inferential Calculus combines (joins) the small pieces to find how much there is.

Data Scientists usually use calculus in building many Deep Learning and Machine Learning Models. They are involved in optimizing the data and bringing out better outputs of data, by drawing intelligent insights hidden in them.

Linear Algebra:

Focusing more on computation, linear algebra plays a crucial role in understanding the background theory behind Machine learning and is also used for Deep Learning. It gives us better insights into how the algorithms really work in day-to-day life, and enables us to take better decisions. It mostly deals with Vectors and Matrices.

Real-life Example: Let’s say you have thousands of images, and each image is made up of thousands of pixels. Linear algebra helps us organize and manipulate these pixels effectively. For instance, when training a machine learning model to recognize objects in images, linear algebra helps us represent and process these images as numerical data, making it easier for the model to learn and make accurate predictions.

How to Python Corelates to this mathematical functions above ?

Python functions and mathematical functions are closely related, especially in the context of machine learning. While Python functions manipulate data in the form of characters, strings, numbers, etc., the underlying mathematical functions help in processing and extracting insights from this data. Libraries like NumPy, SciPy, and Pandas provide pre-defined functions that enable us to apply mathematical operations on our dataset.

These mathematical functions play a crucial role in preprocessing the data by handling garbage values, noise, and null values, thereby making the dataset more suitable for machine learning algorithms. Once the data is preprocessed, these functions help us in visualizing the data and selecting the best algorithm for our model.

In machine learning algorithms, mathematical functions are embedded within the Python code, helping us solve various problems such as Boolean or matrix problems (e.g., identifying objects in images). These functions also assist in evaluating the performance of our model by using metrics like correlation, F1 score, accuracy, etc., and detecting overfitting or underfitting.

Python’s flexibility and extensive libraries make it an ideal choice for implementing mathematical strategies in machine learning algorithms, enabling us to solve real-world problems effectively in fields like ML and Data Science.