Prerequisites for Machine Learning and AI

Artificial Intelligence and Machine Learning Prerequisites
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Make artificial intelligence and machine learning easier with basic Math and programming

In the last decade, or two, Machine Learning and Artificial Intelligence used to be hot industry topics. Artificial Intelligence has a detrimental impact on the business world, if not directly in our everyday lives. Reports from 2014 suggest that over $300 million is invested in venture capital for AI startups.


It was three times more than earlier. You should train yourself and acquire the necessary knowledge to excel in this career. But, you cannot get into the field without familiarity with the prerequisite for Machine Learning and AI. So, that’s the purpose of this guide.


However, before we get to that, you need to understand that definition and scope of AI are constantly evolving. While back in the day, even simple +/- calculations on a calculator were artificial intelligence because every computation was done manually earlier. But, today powers and scope of AI are different.


AI powers home automation devices and systems like Alexa, Siri, and Google Home. Moreover, with tech giants like Facebook and Google launching new AI integrated features every week, the user experience for the people only elevates. The suggested responses and auto-reply feature in Gmail is also an example of AI, wherein the replies are taught to the machine.


Now, let us address the different prerequisites for Artificial Intelligence and Machine Learning.


What Prerequisites Do I Need For Machine Learning and AI?

Please understand even though some prerequisites for AI and Machine Learning are co-related, both require intensive foundation and learning. In addition, the former is a little more complicated. So, unlike in Machine learning, to be a valuable and bankable AI employee, you cannot rely solely on a mere class.


You will have ample alternatives to learn the different AI capabilities needed to ace the subject. But, you can pick the session depending on your experience or skill in the field.


Even if you choose a course from reputed universities like Stanford for Machine Learning, then too you need to have a little bit of basic background knowledge to do well in the course from day one.

Despite your current skill level, here are some general prerequisites for Machine Learning and AI that you need to tick out.

A. Mathematical Knowledge– You need a lot of math to progress your career in these two domains. Sadly, it is one reason that puts off many newbies. However, if you have pledged to go forward, here are some mathematical areas wherein you must have the necessary proficiency:


1. AlgebraUnderstanding algebra is fundamental to mathematics in general. Besides mathematical operations, such as BODMAS, you must also acquire an understanding of the following:

  • Radicals
  • Exponents
  • Scientific Notations
  • Summations
  • Factorials


2. Linear Algebrais  -the AI’s primary computation tool, widely employed in engineering and science. The vital AI and Machine Learning prerequisites in linear algebra are:

  • Scalars
  • Vectors
  • Matrices
  • Tensors
  • Singular Value Decomposition
  • Eigenvectors and Eigenvalues
  • Principal Component Analysis

Beyond this, you must also be well-versed with linear algebra properties like Vector product, Dot product, and the Hadamard product.


3. Calculus – considers the changes in functions, parameters, approximations, and errors. For ML and AI, you must possess a working knowledge of multi-dimensional calculus. Here are some aspects you should know in Calculus:

  • Gradient Algorithms
  • Vector or Matrix Calculus
  • Derivates


4. Information Theory Concepts – It is an amalgam of probability, statistics, and calculus and includes:

  • Cross-EntropyEntropy
  • Viterbi Algorithm
  • Encoder-Decoder
  • Kullback Liebler Divergence


5. Statistics and probabilityare – the most important AI and Machine Learning prerequisites. Thus, dedicate a substantial degree of time to acquiring this proficiency. Fortunately, they are not very challenging. So, it should not be difficult to get well-versed with them. A few concepts you need to understand in Statistics and Probability are:

  • Fundamental Statistics comprising mean, median, mode, covariance, variance, etc
  • Rules of probability – Sample spaces, events, and conditional probability.
  • Baye’s Theorem
  • Common Distributions – binomial, poisson, bernoulli, gaussian, exponential
  • Maximum Likelihood Estimation
  • Random Variables – distributions – joint or conditional, variance, expectation, discrete, and continuous.


Please understand if you wish to be an expert in AI or a machine learning engineer, you should be pro at solving real-world problems with deep learning and AI algorithms. It will take time, practice, and effort.


One of the courses that can help you with it is the Mathematical Foundation For Machine Learning and AI – [Udemy]. It is a 4.5-hour video course but is not all-inclusive. So, find the classes or books to understand the other listed topics.


B. Data Modeling – For machine learning and AI to metamorphose, it is imperative to commence with an effective data model and top-quality reliable data. The data model determines how your data is stored, organized and the relationship between the data. With a good model, it is easier for users to comprehend the working of an organization.


Here are some resources for assistance:

a. The Complete Database Design & Modeling Beginners Tutorial – [Udemy]– It is a beginner-friendly class to help you fulfill this prerequisite for Machine Learning and AI. In this 2-hour class, you will understand the meaning of data modeling and the three levels of relational database design. You will also understand the working and be able to create database relationships.


b. Alternatively, you can also refer to some books on data modeling. Here are some popular suggestions to help you start:

  1. Designing Data-Intensive Applications by Martin Kleppmann
  2. The Data Model Resource Book, Vol. 3: Universal Patterns for Data Modeling 1st Edition by Len Silverston, Paul Agnew
  3. Data Modeling Made Simple: A Practical Guide for Business and IT Professionals Second Edition by Steve Hoberman


C. Programming Skills – If you wish to be an AI or machine learning expert, you need familiarity with some vital programming languages. GitHub suggests ten programming languages under prerequisites for Artificial Intelligence and Machine Learning.


These include:

  1. C++
  2. Python
  3. C#
  4. JavaScript
  5. Shell
  6. R
  7. TypeScript
  8. Java
  9. Julia
  10. Scala 


Even though Python is one of the most prevalent languages you will use in AI and machine learning, Scala is also becoming popular, especially in interactive with big data frameworks like Apache Spark.


Related Prerequisites for: AWS Machine Learning | Machine Learning in Python


Frequently Asked Questions

Ques 1. Any course to learn AI and ML with no prerequisite?

Ans. You can take the 11-month Professional Certificate Program In AI And Machine Learning program from Purdue University to learn AI and ML sans any former knowledge.


Ques 2. Will it be difficult for me if I skip the prerequisites?

Ans. As we have seen, there are multiple prerequisites associated with AI and ML. Regardless of how beginner-friendly the session is, it always helps to acquire elementary knowledge to streamline the journey into these advanced fields.


Final word

So, these were the topics you should know beforehand for ML and AI. Knowledge of prerequisites only simplifies entry into the field. Your hard work and effort determine your journey’s conquest in the domain. So, keep at it, and success shall be yours!


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