Before pursuing machine learning, it is essential to follow a map that will help you in your career path. You can consider the following:

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**5 Prerequisites to Learn Machine Learning**

While machine learning certification courses do not necessarily require you to have prior skills in the domain, it eventually gets down to how well you can perform and work with **programming languages**, statistical means, variables, linear equations, histograms, etc. So, you need to be well prepared to pursue machine learning. Here is a short list of machine learning prerequisites to get you started.

**Statistics**

Statistics, as a discipline, is concerned mainly with data collection, sorting, analysis, interpretation, and presentation. Some of you might have already guessed how statistics is of value to Machine Learning. Data is, of course, a huge part of any technology today. Let us talk about how statistics fit into all this.

There are two kinds of statistics; one is **descriptive statistics **and the other is inferential statistics. Descriptive statistics, as its name suggests, is numbers that describe a certain data set, i.e., descriptive statistics summarizes the data set at hand into something more meaningful. Inferential statistics conclude from a sample instead of the entire data set.

A machine learning expert will have to be familiar with:

- Mean
- Median
- Standard deviation
- Outliers
- Histogram

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**Probability**

Probability describes how likely it is for an event to occur. All data-driven decisions stem from the foundation of probability. In machine learning, you will be dealing with:

- Notation
- Probability distribution, joint and conditional
- Different rules of probability—Bayes theorem, sum rule, and product or chain rule
- Independence
- Continuous random variables

These are only a few of the concepts; machine learning aspirants will be working with a lot more.

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**Linear Algebra**

While linear algebra is integral to machine learning, the dynamics between the two are a little vague and are only explicable through abstract concepts of vector spaces and matrix operations. Linear algebra, in machine learning, covers concepts such as:

- Algorithms in code
- Linear transforms
- Notations
- Matrix multiplication
- Tensor and tensor rank

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**Calculus**

Calculus is crucial to building a machine learning model. An integral part of several **Machine Learning algorithms**, calculus is another way for you to aim for a machine learning career. As an aspirant, you can familiarize yourself with:

- Basic knowledge of integration and differentiation
- Partial derivatives
- Gradient or slope
- Chain rule—for training neural networks

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**Programming Languages**

It is good to have a sound foundation in programming as machine learning algorithms are put into effect with code. While you can get away as a novice programmer and focus on the mathematics front, it is advised to pick up at least one programming language as it will truly help your understanding of the internal mechanisms of machine learning. However, you need to pick up a programming language that will make it easy to implement machine learning algorithms.

**Here are a few popular programming languages:**

**Python:**

Python’s easy syntax,** built-in functions**, and wide package support make it popular for machine learning, especially for beginners. It has the most-supported libraries. Through the Python Package Index (PyPI,) you can access over 235,000 packages. There is also great community support to **learn Python**.

**In Python, you will learn:**

- NumPy for mathematical operations
- TensorFlow for
**Deep Learning** - PyTorch package for deep learning
- OpenCV and Dlib for computer vision
- scikit-learn for classification and regression algorithms
- pandas for file operations
- Matplotlib for data visualization and more

Python is, however, relatively slower than other languages and also faces multithreading struggles.

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**R:**

**R** is another one of the AI and machine learning prerequisites that are as widely used as Python. Nowadays, various machine learning applications are implemented through R. It comes with good library support and graphs.

**Here are a few of the key packages that are supported by R:**

- Kernlab and Caret for regression and classification-based operations
- DataExplorer for data exploration
- Rpart and SuperML for machine learning
- Mlr3 for machine learning workflows
- Plotly and ggplot for data visualization

R is also relatively slower than C++ and can be difficult for beginners, unlike Python.

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**C++:**

Due to its portability feature, C++ is known to be majorly employed in games and large systems. It establishes a good understanding of logic building and is the go-to programming language for building libraries. As one of the prerequisites for machine learning, C++ supports:

- TensorFlow and Microsoft Cognitive Toolkit (CNTK) for deep learning
- OpenCV for computer vision
- Shogun and mlpack for machine learning
- OpenNN, FANN, and DyNet for neural networks

C++ has its shortcomings due to its syntax-oriented approach, which can be difficult for beginners. It also does not have good library support.

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**MATLAB:**

MATLAB or matrix laboratory supports machine learning operations and is used in applications and computer vision.

MATLAB has several predefined functions in addition to GUI. MATLAB is not syntax-oriented. The MATLAB compiler helps to share programs as independent apps and web apps. MATLAB uniquely supports machine learning. MATLAB provides:

- Optimized and reduced coded models using AutoML
- Sensor analytics using automatic code generation and much more

Despite all of its pros, MATLAB is not readily accessible or free; the compiler is rather costly to buy. So, the majority of MATLAB’s target audience is solely in the researcher community.

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**Choosing the Right Programming Language**

As you have already seen, every programming language has its pros and cons. So, which one should you be learning as a prerequisite for machine learning? It comes down to your area of interest. If you want to get into game development, C++ is the language that you should consider mastering. You can also go for C++ if you want to develop packages. A research-oriented professional, on the other hand, will do well with MATLAB.

In terms of machine learning, Python and R go neck to neck. As far as the learning path is concerned, both of these programming languages come with terrific support, especially online. Out of the two, however, Python is more preferred by those who are new to coding. Machine learning scientists who work on sentiment analysis prioritize Python (44%) and R (11%,) according to Developer Economics.

*Also, have a look at this blog on Machine Learning Syllabus to get an overview of Machine Learning topics.*

**Conclusion**

Along with knowing the important prerequisites for machine learning, you should also know how to work with data. It is an essential skill if you want to pursue machine learning seriously. In this blog, we covered the essential prerequisites of machine learning, along with the pros and cons of some of the most preferred programming languages for the same. In short, machine learning requires statistics, probability, calculus, linear algebra, and knowledge of programming. It is up to you to define your machine learning path. Test the waters to see which modules are more up your alley, and start there!

*If you are curious to learn more, raise a question in our **Machine Learning Community**.*

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