What Are The Prerequisites For Machine Learning?

Machine learning is one of the fastest growing and high paying domains. If you aim to become a machine learning engineer then this article will help you understand the main prerequisites to become one. Read on.

If you want to become a machine learning engineer and don’t know where to start, we can help you on how to start. This article talks about the prerequisites for Machine Learning and will guide you on the skills required to become a machine learning engineer.

Table of Content

• Educational Prerequisites for Machine Learning
• Is Coding required for Machine Learning?
• Skills Prerequisites for Machine Learning
• Programming Languages Prerequisites for Machine Learning
  • Python
  • R
• Can I Teach Myself Machine Learning?
• Conclusion

Educational Prerequisites for Machine Learning

To start your career as a machine learning engineer, you would need to have a Bachelor’s degree or an equivalent qualification in disciplines like Computer science, Information technology, Information Management, Mathematics or Statistics. 

Must Read – How to Become a Machine Learning Engineer?

Is Coding required for Machine Learning?

Yes. If you are a machine learning aspirant then knowledge of coding is necessary. Machine learning is implemented through coding and if you know how to code, you can leverage your knowledge and skills to monitor and optimize the algorithms. You can learn programming languages like Python, C++, Java, R, Lisp, Prolog, etc. 

Skills Prerequisites for Machine Learning 

• Probability
• Linear Algebra
• Calculus
• Statistics

Probability 

In probability, we talk about the probability of an uncertain event. Probability plays an important role in tasks associated with large-scale data collection and interpretation, making it an essential tool for machine learning. 

Topics to Learn –

• Random Variables
• Random Experiment
• Conditional Probability 
  • Bayes’ Theorem
• Probability Distributions – 
  • Uniform
  • Bernoulli 
  • Binomial 
  • Poisson 
  • Normal

Linear Algebra 

Linear Algebra appears practically everywhere. It will be necessary to understand the basic properties of vectors and matrices, matrix multiplication, special matrices, Gauss-Jordan elimination, etc. 

Linear Algebra is important to learn the fundamentals of working with data in vector and matrix form. You would need to solve systems of linear algebraic equations and find the basic matrix decompositions and their applications.

Topics to Learn – 

• Matrix
• Vector Spaces 
• Eigenvalues & Eigenvectors
• Singular Value Decomposition (SVD)
• Lower–Upper (LU) Decomposition
• Orthogonalization & Orthonormalization
• QR Decomposition/Factorization
• Symmetric Matrices
• Matrix Operations
• Projections

Calculus

Calculus plays an important role in building machine learning models. It can be considered as a set of tools to analyze relationships between functions and their inputs. As a machine learning aspirant, you should be familiar with –

• Iintegration 
• Differentiation
• Partial Derivatives
• Vector-Values Functions
• Directional Derivative
• Gradient, Hessian, Jacobian, Laplacian and Lagragian Distribution

Statistics 

You would need to have the knowledge of statistical concepts when dealing with data and drawing conclusions from it. 

Topics to Learn – 

Measures of Central Tendency

• Mean
• Median
• Mode

Measures of Dispersion

• Range
• Inter Quartile Range
• Variance

Standard Deviation

• Hypothesis Testing
• P-value

Statistical Test

• Z-test
• t-test
• Chi-square test
• ANOVA test

Other important skills that you should learn, include –

• Knowledge of Data Structures
• Dynamic Programming
• Randomized and Sublinear Algorithms
• Graphs
• Stochastic Gradients/Descendants 
• Primal-Dual Methods 

Programming Languages Prerequisites for Machine Learning

If you’re new to machine learning, you’re obviously wondering which programming language you should start with. There is a huge amount of language for you to start writing your machine learning algorithm. Each of them has its own unique feature. 

Python is without a doubt the best programming language for machine learning applications. Other programming languages ​​that can be used for Machine Learning applications are R, C++, JavaScript, Java, C#, Julia, Shell, TypeScript, and Scala.

You can choose a programming language to start with and focus more on the concepts. Let’s take a look at the few popular machine learning programming languages. Pick programming language that comes with –

• Efficient machine learning and deep learning libraries
• Good runtime performance
• Good tool holder
• A large community of programmers
• A healthy ecosystem of support packages

Python 

One of the main reasons why Python is becoming the most popular programming language in machine learning is because of its simplicity combined with great versatility. Python gives a very clear and therefore easy to understand code, flexibility, and rapid prototyping capabilities. 

Statista compares the popularity of Python against other programming languages.

Being a multi-paradigm language, Python allows integration with other languages ​​such as C, C++, Java, and Cobra, while allowing various programming styles, such as object-oriented programming (OOP), imperative programming, and functional programming. Besides, it also has a huge ecosystem of libraries, frameworks, and tools such as Pandas, NumPy, TensorFlow, Keras, Scikit-learn, and many more.

R

R is a specific language for working on data analysis and machine learning, which gives it a great advantage over other general-purpose languages. It is an open-source cross-platform language, functional, object-oriented, and easy to learn. 

R has a very popular development environment that is specific to the language (R-Studio, which also supports Python). It also has several packages, Like Python, R has an incredible number of ready-to-use packages such as RODBC, Gmodels, Class, and Tm, which help in implementing Machine Learning algorithms easily.  

Starting from data loading and processing to data visualization, nonlinear regression to neural networks, R enables multitasking. 

Can I Teach Myself Machine Learning?

Yes, it would be a tricky process but you can surely teach yourself machine learning. Below are some of the steps that can help you learn machine learning on your own –

• Learn the necessary machine learning skills like math and statistics, programming, data handling, data structures, object-oriented concepts, algorithms, etc.
• Build your profile on Kaggle and participate in competitions on Kaggle and score high on the leaderboard for a better visibility
• Create your machine learning projects on GitHub
• Participate in hackathons, bootcamps and machine learning conferences to gain the real-world experience of machine learning
• Pursue internship with a reputed organization
• Take up online machine learning courses based on your existing knowledge and skills

Must Explore – Top Machine Learning Courses

Conclusion

Machine learning is a highly lucrative and high-paying field. For someone who is looking to grow as a machine learning engineer, these prerequisites will help you get started. There is no shortcut to becoming a machine learning engineer. You would need to practice a lot on creating machine learning projects and models so that you gain real-life work experience.

All the best!

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