Machine Learning Prerequisites Explained With Their Importance

Machine Learning Prerequisites
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Know what you need to know before starting to learn ML + where to learn about it

Everything is an example of machine learning, from malware filtering to email spasms and YouTube’s recommendation system. But, before you venture further into this space and build systems for executive tasks beyond what is programmed, you must have a sound understanding of some areas. These are what you can understand as the machine learning prerequisites.


When you go through the various online Machine Learning courses, you may realize they are often labeled beginner-friendly classes, expecting no former skill or experience. But, it eventually boils down to how well you can work with programming languages, variables, statistical means, histograms, and linear equations.


Thus, regardless of whether the course assumes former knowledge, there are some vital basics you must know in advance. Below we will address them.


What Are Prerequisites For Machine Learning?

Even the best of courses like Machine Learning by Standford has a few prerequisites. So, let us have a look at what kind of basic background you need to go ahead with machine learning.


A. Statistics

Data is a vital aspect of every technology. If you want some outcome from your data, you use statistics. It is a discipline responsible for presentation, interpretation, analysis, sorting, and data collection. Broadly, there are two types of statistics – inferential and descriptive statistics.


The former concludes with a sample and not the entire data set, helping you derive vital information from the raw data. The former summarizes the data set at hand into something meaningful.


Since machine learning algorithms and techniques are tightly-associated with statistical theories and concepts, it is one of the vital machine learning prerequisites.


Some statistics concepts you must be well-versed with are:

  1. Mean
  2. Median
  3. Outliners
  4. Standard Deviation
  5. Histogram
  6. Outliners


If you wish to be well-versed with Statistics for Machine learning, here are some resource suggestions:

An Online Course for You

1. Statistics for Machine Learning – [Great Learning] – It is a free class and bags you a certification. It can be an excellent aid if you wish to fulfill this prerequisite for machine learning course. In this two-hour, self-paced video lesson, you will study with Dr. Abhinanda Sarkar and cover all the essential statistics concepts in Machine learning.
A Book to Combine with The Course

2. Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R 1st Edition – If you like to study the traditional way, this book can be an excellent pick to help you learn it all about statistics and its influence on Machine Learning.


B. Basic Math

It is the level of mathematics you achieve in high school. So, even though it is one of the top machine learning prerequisites for beginners, you do not have to spend too much time getting acquainted with it.


You can solve even complex math problems via the in-built programming language. As you have studied these concepts over the years, you do not need to go into detail about these algorithms. But, if you still need some preparation, we have a suggestion.


Online Course for Math’s Application in Machine Learning
One of the top course choices to help you with this machine learning course prerequisites is the Mathematics for Machine Learning Specialization – Offered by Imperial College London – [Coursera].


In this class, you will study everything needed to implement mathematical concepts with real-world data, master PCA, know-how algorithm projections work, and derive PCA from the projection perspective. The class takes four months to complete at a suggested pace of four hours per week.


C. Probability

Probability determines how likely an event is supposed to occur. Knowledge of probability can help us reason whether a particular situation may happen again or not. All data-driven decisions arise from the basic understanding of probability. Hence, probability knowledge is also one of the top prerequisites to learn machine learning. As part of ML, you will employ the following probability-related aspects:

  1. Independence
  2. Notation
  3. Joint and conditional probability distribution
  4. Continuous random variables
  5. Different rules of probability
  6. Chain or product rule, Sum rule, and Bayes Theorem
  7. And more


Course for Your Rescue
One of the best courses to help you with this prerequisite is the Udemy class Complete Math, Probability & Statistics for Machine Learning. It is a 14-hour session and covers all the above three prerequisites that you need for Machine Learning.


So, if you do not want to go through the hassle of three different sessions, this can be the best pick to fulfill machine learning prerequisites for beginners.


D. Linear Algebra

Linear Algebra appears literally everywhere. It is a necessary skill to get well-versed with the basic properties of matrix multiplication, matrices and vectors, Gauss-Jordan elimination, special matrices, and more. Study this aspect to understand the fundamentals for working with data in matrix and vector form.


Some topics you need to cover in algebra are:

  1. Projections
  2. Matrix
  3. Vector Spaces
  4. Matrix Operations
  5. Symmetric Matrices
  6. Eigenvectors and Eigenvalues
  7. Factorization
  8. QR decomposition
  9. Orthogonalization and Orthonormalization
  10. Lower-upper decomposition
  11. Singular Value Decomposition


What’s The Solution?
If you wish to study linear algebra in detail, you can opt for Professional Certificate in Introductory Linear Algebra – by Georgia Tech University – [edX]. It can help you fulfill this prerequisite machine learning needs. It is a two-month class with a suggested effort of five to six hours per week. There are two courses included in this session.


E. Calculus

Unfortunately, many of you did not fancy calculus when it was taught in school. However, it can be an absolute shock for machine learning aspirants because it is one of the most imperative prerequisites to learn machine learning. But you do not have to be a pro at it.


If you understand the principles associated with calculus, it should suffice. Calculus can help you work with machine learning practical applications and create a machine learning model.


Some calculus topics you must know to work with machine learning are:

  1. Partial derivatives
  2. Integration and differentiation
  3. Slope or Gradient
  4. Chain rule


What Should Be Your Action Plan For This?
Two of the best courses to help you learn calculus in detail are Become a Calculus 1 Master and Become a Calculus 2 Master. The former is a 12.5 hour, and the latter is a 30.5-hour of video session.


F. Programming language

For implementing the whole process associated with Machine Learning, you should understand this prerequisite for machine learning course. Knowledge of programming languages like Python and R gives you in-built libraries that make it easier for you to implement the Machine Learning algorithms.


Beyond the basic programming knowledge, you must also learn to analyze, process, and extract data.


Some Courses To Help You Master Programming Languages:
1. Learn Python: The Complete Python Programming Course – It is a 14-hour session that helps you create your own Python programs, parse the web, create your games, and become proficient in Python.

2. Complete Machine Learning with R Studio – ML for 2022 – The session has 12 hours of on-demand video that educates you on solving real-life problems with R knowledge and Machine learning techniques. In addition, you will also study the basic statistical operations and understand how to run machine learning models in R.


G. Data Modeling

Data Modeling involves the data set structure estimation and helps find the patterns or variations within, making it the key prerequisites to learn machine learning. It is based on predictive modeling. Thus, you need to know the different data properties for prediction. Iterative data modeling algorithms learning can help avoid errors in the model and data sets. Hence, it is vital to possess the data modeling function understanding.


Where To Learn It?
A popular course choice you can enroll in for acquiring data modeling understanding is the Data Modeling and Regression Analysis in Business – by the University of Illinois at Urbana-Champaign – [Coursera]. It is a popular session that helps you explore regression, data description, and statistical interference.


In addition, you will also study the statistical methods employed for prediction when the response variable is categorical. You can finish this course in 25 hours.


H. Python

Python is one of the top machine learning prerequisites. It is one of the top programming languages because of its simplicity and versatility, which dictates its dominance in Machine Learning. The code in Python is concise and easy to understand. It is flexible and possesses rapid prototyping abilities.


Moreover, Python allows seamless integration with other programming languages like Cobra, Java, C++, and C. Moreover, there is a vast ecosystem of frameworks, tools, and libraries like NumPy, Pandas, Keras, TensorFlow, Scikit-learn, and more.


What To Do To Get An Understanding Of Python?
One of the best places to help you fulfil the essential pre requisites for machine learning in Python is Machine Learning with Python – [FreeCodeCamp]


Related ReadsAWS Machine Learning Prerequisites | Prerequisite For Machine Learning and AI


Frequently Asked Questions


Ques 1. Do I have to learn Python before ML, or is it optional?

Ans. Yes, you must possess Python knowledge to implement the whole machine learning process.


Ques 2. Is machine learning a prerequisite to AI?

Ans. Machine learning is not one of the machine learning course prerequisites but rather a subset of artificial intelligence. It involves the scientific study of statistical models and algorithms employed by machines. It helps perform the given task with data interference and patterns.


Ques 3. Do you need coding experience for machine learning?

Ans. If you hope to pursue a career in artificial intelligence or machine learning, you must have some coding experience.


Skills You Need For Machine Learning

Beyond the machine learning prerequisites, there are also some skills you must possess to be a machine learning engineer. These include:

  1. Natural Language Processing
  2. Communication Skills
  3. Data Science skills
  4. Software engineering skills
  5. Practical application of prerequisites like Probability, Linear Algebra, and Math.
  6. Teamwork

Final Word

So, these are all the major prerequisite machine learning requires. Hopefully, with all the above-stated concept understanding and skills, the journey to a successful career in machine learning is guaranteed.


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