Prerequisite for Machine Learning

Nowadays, we are seeing many applications developed using machine learning which has led to the rapid growth and demand for machine learning. It is one of the most popular technologies in the current era. Most organizations are now utilizing machine learning for the development of applications. If you are one of the aspirants looking for a career in machine learning, then it is essential that you have an idea about the prerequisites for machine learning. In this blog, we will discuss machine learning and its prerequisites, applications, usaceases, etc. Let's get started!

What is machine learning?

Machine learning refers to the branch of computer science and artificial intelligence, primarily focusing on how the algorithms and data are used, allowing the systems to improve from the experience without explicit programming. The primary goal of machine learning is to develop computer programs capable of accessing the data and using them for learning purposes. A Machine learning system is responsible for learning based on the historical data, capable of building the production models and delivering the output based on the inputs.

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Prerequisite for Machine Learning

Are you looking to advance your career through machine learning Technology? If yes, are you aware of the prerequisites for machine learning? Maybe not! It is essential to understand the prerequisites for machine learning, which will help you identify the skills that are required to be developed before getting into machine learning training. 

You need to have an idea on the below set of concepts: 

a. Statistics: 

Statistics and machine learning are tightly coupled  with each other, in which the machine learning concepts are either derived or taken from statistics or either dependent on it. All the algorithms and techniques in machine learning are based on the concepts related to statistics and its theories. Hence statistics is considered one of the important prerequisites for machine learning. 

What is statistics? Statistics is one of the fields in mathematics that helps in deriving conclusions from the available data. Machine learning algorithms include distribution, logistic regression, hypothesis testing, etc. An aspirant should have knowledge of the concept related to statistics to perform the following tasks:

a. Statistics includes multiple tools from which you can derive the outcomes based on the information and data available. 

b. Statistics also helps in transforming the sample observations to obtain meaningful insights. 

c. Statistics help find the impurities as the raw data may not always be perfect, and it could be either incomplete or corrupted. 

d. Through statistics, you can also obtain the information to some questions based on the data. 

e. Through Statistical hypothesis, you will also be able to identify and select the best model to perform the predictions.

Below listed are the fundamental statistical concepts that are required for machine learning: 

  • Axioms 
  • Random variables 
  • Combinatorics 
  • Joint and conditional distributions 
  • Bayes Theorem 
  • Expectation and variance 

B. Linear algebra: 

Linear algebra is a study of matrices, linear transformations, and vectors. Linear algebra plays a crucial role in machine learning as it helps in performing the transformations and operations on a given data set. Linear algebra is integral to machine learning which helps in performing data optimization. 

Linear algebra is used to perform the PCA (principal component analysis) for the dimensionality reduction process. It has a vast  set of applications in the field of machine learning. Linear algebra is also used in the data science field. 

C. Calculus: 

Calculus plays an integral role in the field of machine learning and is also considered an important branch of mathematics. Calculus is used for building the machine learning model. A knowledge of basic concepts in Calculus is enough. Calculus helps in solving the optimization problems. There is no need to solve the complex derivatives manually in calculus. It is important to gain an understanding on how differentiation works and how it is applied for vector calculus. Multivariate calculus is used for gradient Descent but not for algorithm training. Below listed are some of the concepts included in the multivariate calculus. 

a. Divergence 

b. Quadratic approximation 

c. Derivatives

d. Directional gradient 

e. curvature 

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D. Probability: 

Probability usually refers to the concept of how the behavior varies based on the same input. Probability usually measures the uncertainty of an event that occurs. It is a crucial concept in mathematics which helps in identifying the chances that the event might occur. It is possible to model the elements using the probability in machine learning. In traditional programming, the output does not change and remains static with respect to uncertainty whereas the output differs based on the same input through probability in machine learning. Statistics and probability are the scenes that are related to each other. Probability deals with future events, whereas statistics deals with the past event analysis. 

Some of the commonly used concepts and probability are listed below: 

a. Regression 

b. Marginal, conditional and joint probabilities 

c. Discrete and continuous probability distributions 

d. Bayes Theorem 

e. Density estimation, etc.

E. Programming languages: 

It is important to have knowledge and programming languages as machine learning algorithms are based on coding. If you are an aspirant who has mathematics knowledge, then you need to learn at least one programming language, so that you can gain an understanding on the internal mechanism of machine learning. A programming language plays a key role in machine learning, it is vital to gain in-depth knowledge in at least one programming language. Some of the popular programming languages are: 

a. C++: 

C++ programming language is used in the development of gaming applications and large system based applications. You need to gain understanding on the logic building concepts and includes multiple libraries like 

a. Shogun 

b. Microsoft cognitive Toolkit 

c. Tensorflow 

b. Python: 

Python is a programming language which includes any syntax, consisting of built-in functions providing its extensive support for machine learning. There are almost 235000 packages that can be accessed in the Python package index. You will gain understanding of TensorFlow, PyTorch for deep learning, NumPy for performing the mathematical operations, pandas for performing the operations on the files, etc. 

c. R: 

R is one of the popular programming languages that forms a prerequisite for Artificial Intelligence and machine learning. Most of the machine learning applications are now being implemented using the R programming language. It includes a derived set of libraries that provide its support via graphs. 

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Applications of Machine Learning

1. Image recognition:

Image recognition being one of the most popular applications of machine learning, helps in identifying the places, objects, persons, images, etc. Facebook includes a feature called Auto friend tagging suggestion. Whenever the user uploads the photograph in Facebook, it will automatically suggest you to tag a person with the name based on the face detection. This takes place with the help of face detection and recognition algorithms. 

2. Medical diagnosis:

in the field of Medical Science, machine learning plays a crucial role in identifying the diagnosis of diseases. Machine learning is used in Medical technology, which also hel[s in  building 3D models that are capable of predicting the position of lesions in the brain. It helps in identifying and determining the brain tumors and helps in the diagnosis process. 

3. Finance sector:

Machine learning is being implemented in the finance sector as well. One of the major applications that is used in the finance industry is algorithmic trading, which includes employment of the algorithms to make precise decisions about the trading. The traders will be capable of building the mathematical models which help in monitoring the news and trade related activities on a real time basis.

Understanding machine learning with a use case

Machine learning is technology or a practice that is responsible for creating an algorithm using which you can learn from the data to make a production about the future events. Some of the examples are recommendation engines, spam filtering, etc. 
As machine learning consists of mathematics as well, it is important to have knowledge in math and it will be fun. Let's discuss one of the use cases of predicting the prices of houses. You have to read a set which includes all the different features along with its prices. Every house includes some set of areas which will be represented in the format of square feet and prices.

Understanding machine learning with a use case
It is expected to have a correlation between the two sets of variables. To find out the correlation we need to start building a model that helps in predicting the prices of the houses. 

The graph is represented below 


The x-axis presents the price per square feet of the house, and the y axis the price of the house. Once all the data points are plotted, we will be able to identify a scatter plot which is represented in the form of a line in the above figure. If an input is given, the output will be predicted. Our ultimate goal is to identify the line that intersects the maximum data points. 

The line can be termed as below 

Y= MX + c where X is referred to as the independent variable, Y as a dependent variable,  c as y-intercept, m as the slope of the line. 

Here, we are calculating the linear relationship between the dependent variable and independent variable which is called linear regression. 

X being the independent variable, we have the values for it. Hence we need to identify the values for m and c to calculate the value of Y. 

How can you find these values? 

You need to try a bunch of values and identify the line which will be intersecting the maximum number of data points. 

The below equation is used to represent the least square error function. 

 least square error functionIf we consider the different terminologies and factors that are included when the pricing is decided for an apartment, then the equation will be represented as 

Y = b0 + b1x1 + b2x2 + …..+bnxn + c

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Among the different technologies, machine learning has gained a lot of value and demand in the current era. To prosper in the machine learning career, you must take a chance to learn all of the above prerequisites before getting into it. I hope the above information is helpful to you. In-depth knowledge and expertise in machine learning will help you attain the best job opportunity.

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Saritha Reddy
Saritha Reddy
Research Analyst
A technical lead content writer in HKR Trainings with an expertise in delivering content on the market demanding technologies like Networking, Storage & Virtualization,Cyber Security & SIEM Tools, Server Administration, Operating System & Administration, IAM Tools, Cloud Computing, etc. She does a great job in creating wonderful content for the users and always keeps updated with the latest trends in the market. To know more information connect her on Linkedin, Twitter, and Facebook.

Yes, coding is required for machine learning. As machine learning works on algorithms, one must know how to code to monitor and understand how the code has been implemented in the application. 

To use Machine learning, one must have basic knowledge of Python, and it is recommended to learn Python before getting into machine learning. Python is one of the programming languages that provide extensive support on all operating systems like Linux and Windows and offers a complete package for machine learning purposes. 

As machine learning deals with algorithms that are hard to understand for beginners, it is considered to be a bit hard for beginners. Each algorithm will include a set of components that must be learned before you apply them with an application. Hence, you will become proficient when you start working on real-time data and projects. 

Yes, machine learning is a good career with increased growth and demand. It is expected to open up multiple opportunities for the aspirants who are looking to pursue a career in the machine learning field. The salaries offered to a machine learning professional are high compared to the other technologies.