Prerequisites for Machine Learning

One of the most sought-after technologies of our time is machine learning, which is unquestionably the upcoming wave. You must be aware of the machine learning prerequisites if you want to learn machine learning. You can choose a better career path with the assistance of the machine learning prerequisites.

The goal of the interdisciplinary field of mathematics and computer science known as machine learning is to teach machines to think like humans do. The process by which machines examine, or learn from, input data is referred to as "learning" in machine learning. Patterns can be automatically learned by machine learning systems from a variety of datasets, including structured data, numerical data, textual data, visual data, and so on. Understanding each concept in its proper context is crucial to success in machine learning technology.

In this article, we will talk about the perquisites for AI so you can improve your base for learning its high level ideas.

What are the prerequisites for machine learning?

To get started with machine learning, you must be aware of the below points.

• Educational Prerequisites for Machine learning Career
• Skills-based Prerequisites for Machine learning Career

1. Statistics
2. Linear Algebra
3. Calculus
4. Probability
5. Programming Languages

Educational Prerequisites for machine learning

Does Master's/Ph.D. require to become a Machine learning engineer?

Among those who want to learn machine learning and pursue a career in it, this is one of the most common inquiries regarding educational requirements for ML. The answer is NO, which means that you don't need a master's or doctoral degree to learn about and work in machine learning. There are bunches of individuals who have made a vocation in this field without having a degree. Having a master's or Ph.D., on the other hand, will undoubtedly provide you with additional advantages and smoother progression. The doctoral degree The D. certificate is a way to show off your skills, but your practical knowledge and skills will ultimately help you build a project or make a career in machine learning. In this way, on the off chance that you have sufficient opportunity and assets to have an Expert's or Ph.D. degree, you can do this, and it will unquestionably give you benefit. However, you can move into machine learning even if you don't have a degree but do have strong ML skills.

Skill-based Prerequisites for Machine learning Career

1. Statistics

Machine learning and statistics are the two tightly coupled fields, as most of the concepts of machine learning are either taken from statistics or are dependent on it. Machine learning techniques and algorithms are widely dependent on statistical concepts and theories; hence it is a crucial prerequisite for ML.

Statistics is a field of mathematics that allows to draw the logical conclusion from the data. Every machine learning enthusiast must understand the statistical concepts in order to learn the working of algorithms such as logistic Regression, distribution, hypothesis testing, etc. It helps in performing the following task:

• It contains various tools that allow us to get some outcomes from the available data and information.
• It finds outcomes from the data and transforms sample observations into meaningful information.
• Each raw data is not perfect and contains different impurities in it, such as incomplete data, corrupted data, etc. In such cases, statistical concepts help to identify these impurities.
• It helps in obtaining answers for different questions such as, who scored the maximum & minimum in the cricket tournament? Which technology is on-trend in 2021? etc.
• Statistical hypothesis tests enable in selecting the best model for any kind of predictive modeling problem.

Some fundamental concepts of Statistics needed for ML are given below:

• Combinatorics
• Axioms
• Bayes' Theorem
• Variance and Expectation
• Random Variables
• Conditional and Joint Distributions.

2. Linear Algebra

Linear algebra deals with the study of vectors & some rules of manipulating these vectors, matrices, and linear transform. It is one of the integral parts of machine learning and helps the ML algorithms to run on a huge number of datasets with multi-dimensionality.

The concepts of linear algebra are widely used in developing algorithms in machine learning. It can perform the following task:

• Linear algebra has vast application in machine learning.
• Linear algebra is essential for optimizing the data in machine learning.
• It is used in loss functions, regularisation, covariance matrices, Singular Value Decomposition (SVD), Matrix Operations, and support vector machine classification.
• Linear algebra is also used for performing Principal Component Analysis(PCA) for dimensionality reduction.
• Apart from the above applications, it is also used in neural networks and the data science field.

Although linear algebra is one of the crucial prerequisites for machine learning, it is not required to go in-depth in this, at least not for the beginner; only understanding of the basic concept is enough to start.

3. Probability

For the same input, there are numerous scenarios in the real world where the behaviour or output can vary. Likelihood has forever been a fundamental piece of Math, which estimates the vulnerability of the occasion. The more likely it is that an event will occur, the higher its probability. Probability is used in machine learning to make predictions with incomplete data. It aids in determining the likely outcomes of upcoming events. With the assistance of likelihood, we can display components of vulnerability, for example, risk in a business cycle or exchange, i.e., we can work with non-deterministic issues. We deal with deterministic problems in contrast to traditional programming; Uncertainty has no effect on output. It also helps in testing hypotheses and analyzing distributions like the probability density function and the Gaussian distribution.

Probability theory and statistics are related fields; probability deals with future events, whereas statistics deal with the analysis of past events.

Below are some commonly used Probability concepts:

• Maximum Likelihood Estimation
• Regression with Maximum
• Joint, Marginal, and Conditional Probability,
• Probability Distributions (Discrete, Continuous),
• Density Estimation
• Likelihood and Bayes Theorem, etc.

4. Calculus

Calculus is also an integral part of Machine learning, but it is not required to go in-depth of it at the beginner level; rather, only knowledge of basic concepts is enough. In machine learning, the process of getting the best parameters is known as optimization, and multivariate calculus helps in solving optimization problems in the ML model. It helps in optimization and getting good results from the model. In calculus, we don't need to solve complex derivatives manually; rather, we must understand how differentiation work and how it is applied for vector calculus. Multivariate calculus is not only used for algorithm training but also for gradient descent. Some crucial concepts of multivariate calculus are Derivatives, divergence, curvature, and quadratic approximations, Laplacian and Lagrangian Distribution, Directional Gradient, etc.

5. Programming Languages

Apart from the mathematical concepts, it is very important to have a good knowledge of a programming language and coding capabilities for machine learning. Some of the most popular programming languages for machine learning are as follows:

Python

Python is the most powerful and easy language that anyone can learn. Python was initially developed in early 1991. Most of the developers and programmers choose Python as their favorite programming language for developing Machine learning & AI solutions. The best part about Python is it is very easy to learn compare to other programming languages and also offers great carrier opportunities for programmers and data scientists.

Python provides excellent community support and an extensive set of libraries, along with the flexibility of programming languages. Python is a platform-independent language as well as it provides an extensive framework for Deep Learning and Machine Learning.

Python is also a very portable language as it can be used on different platforms including Linux, Windows, Mac OS, and UNIX.

R

R is one of the great languages for statistical processing in programming. It may not be the perfect language for machine learning, but it provides great performance while dealing with large numbers. Some inbuilt features such as built-in functional programming, object-oriented nature, and vectorial computation make it a worthwhile programming language for machine learning.

R contains several packages that are specially designed for ML, which are:

• gmodels - This package provides different tools for the model fitting task.
• TM - It is a great framework that is used for text mining applications.
• RODBC - It is an ODBC interface.
• OneR - This package is used to implement the One Rule Machine Learning classification algorithm.

Java:

Java is the most widely used programming language by all developers and programmers in the world. Java can be easily implemented on the various platform due to JVM(Java Virtual Machine). The best things about Java is once it is written and compiled on one platform, then you should not need to compile it again and again. This is known as WORA (Once Written Read/Run Anywhere) principle. Java has so many features which make Java best for use in Machine learning. These are as follows:

• Portable
• Memory manager
• Cross-platform.
• Easy to learn and use.
• Easy-to-code Algorithms.
• Built-in garbage collector.
• Swing and Standard Widget Toolkit.
• Simplified work with large-scale projects.
• Better user interaction.
• Easy to debug

Selecting the correct Programming Language

Apart from the above-mentioned programming languages, there are many other programming languages that are being used in Machine learning, such as C, C++, MATLAB, JavaScript, etc. However, choosing the best languages may become a challenging task for beginners. In machine learning, Python and R are the two most preferred languages because of their great benefits and vast libraries. However, other general-purpose languages can also be used, such as Java, C, C++, but make sure you are skilled with these languages.

In addition to the previous programming and math skills, advanced concepts can only be learned with an understanding of some fundamental machine learning concepts. The various types of machine learning?supervised, unsupervised, and reinforcement learning?as well as methods and model construction are among these ideas.