Machine learning is a concept that involves giving a machine the ability to make smart predictions or take actions based on some amount of data where it’s able to study and learn the patterns from. This involves the training algorithms on datasets so that they are able to learn the relationships in any dataset and identify any pattern that exists within this data. This process enables an algorithm to easily generalize to new, unseen data points that are put as an input and produce new and accurate predictions or decisions based on the patterns that are previously identified.
There are various types of machine learning techniques and algorithms that are used in the world of artificially intelligent technologies. These include the supervised learning where the learning outcome is known to the algorithm, the unsupervised learning where the learning outcome is not known and the training is strictly done to identify the similar patterns between different groupings of data points within a dataset, the semi-supervised learning where the data contains both known and unknown learning outcomes, and the reinforcement learning where an intelligent agent learns to interact with an environment and is rewarded or penalized based on some set of predefined rules.
With the potential to solve complex real-world problems with relative ease, machine learning is a concept that is frequently used in the tech world as well as in finance, healthcare, business, and more. There are different tools that can be used to power the machine learning requirements for any project. Two of the most well-adapted tools for machine learning are Python and Matlab. We will compare both of these tools and come to a calculated result as to what tool is better under what circumstance and how we can use this tool to achieve the machine learning requirements for any project.
Python
Python is an interpreted programming language with a simple and easy-to-learn syntax. It makes programming easy even for beginners which is why it is extremely popular. Despite being an easy-to-learn language, its applications which are powered by third party tools and frameworks are extremely utilitarian and powerful. Python has many libraries and frameworks which help the users to implement the machine learning algorithms. PyTorch, Tensorflow and Sklearn are three of these machine learning frameworks. They contain the popular built-in algorithms that can be run on any data just by calling a function that represents them. They also provide the ability to create custom algorithms that are able to give accurate predictions after training on the data. Moreover, both of these libraries and many others that the Python library repository have offer amazing documentation which helps the users to apply the offered functionality in the best way possible without bugs and logical errors.
Matlab
Matlab is a programming language which is geared towards technical computing, data analysis, and scientific computing. It primarily focuses on performing operations on matrices which is why it is very efficient when it comes to performing the machine learning tasks. It comes equipped with functions for linear algebra, statistics, and optimization techniques, all of which increase its utility as a machine learning tool. Matlab has built-in functions for certain machine learning algorithms like regression, classification, clustering techniques, and more. Despite being efficient for matrix arithmetic, it limits you in the things you can do. Unlike Python, it does not provide brilliant open source third party framework support which makes it limited in its scope for the number of tasks that it is able to perform.
Comparison
| Category | Python | Matlab |
| Support | Has brilliant third party library and framework support. The open source machine learning libraries are readily available to use. | Contains built-in machine learning algorithms which limit your use to a few popular algorithms that can be used. |
| Efficiency | Less efficient when it comes to building and training the algorithms that are meant to accurately predict the data outcomes. | More efficient because of its focus on matrix operations and linear algebra. |
| Ease | Easy-to-learn as a language but the third party frameworks come with a learning curve that one has to go through before one can code in Python. | The language itself is easy to learn but the implementation of machine learning algorithms is somewhat complicated and has a learning curve just like Python. |
| Tasks | The different types of tasks that Python is able to do when it comes to machine learning is significantly more as compared to Matlab. This is primarily because of the third party library support for Python. | The different types of tasks that Matlab is able to perform is limited by what the core devs developed into the language itself. It does not have Python-like amazing library support which makes it is limited in this category. |
Conclusion
The world of machine learning has different tools at their disposal. Some people use Python to implement the machine learning workflows while others use Matlab. Both of these languages have their benefits and drawbacks. Some outweigh the others while still being utilitarian and useful. Python is a well-adapted language which is known across the industry for its ease and amazing developer support, not to mention the amazing suite of third party libraries which are focused towards machine learning, AI, and data analytics based tasks. This makes Python a very good contender in this race. But there are certain tasks where Matlab absolutely takes the title and one of them, which is a very important category, is efficiency. Matlab primarily focuses on matrix arithmetic which makes it quicker than Python. When faced with tasks which require training on large datasets with more features, Matlab accomplishes such a task more quickly as compared to Python. It all boils down to your use case and what you are comfortable with. Keep these things in mind: one can make a strong case for either of these languages.
