How C Programming Contributes To Machine Learning and its Algorithms

Machine Learning has reasonably changed the way we use the web and the numerous apps used. Machine Learning has made everyone’s life easier and better. This whole art of Machine Learning is still booming as each and every business is using it for business analytics.

Well, Machine Learning is something which helps the computer program to learn and teach itself to make the experience better. These programs learn by themselves with the help of machine learning and they also learn how to access the data.  Now, the most obvious question comes, which programming language should we use or learn for Machine Learning? Which one is most effective to learn?

Since everyone is quite familiar to the C in the initial years of their college, it becomes very easy for them to understand the algorithms of Machine Learning. The Machine Learning Certification Training Course imparts the C programming lessons with its detailed algorithm.
Here are some examples of algorithms and libraries which are written in C that shows how it contributes in Machine Learning:

1. LIBSVM

This is C based library that is mostly used to support vector machine (SVM) learning. This library is easy to understand and implement SVM. It is considered that SVM makes easy to implement AI as compared to the neural network.

It works efficiently to support vector machine learning with different classifications and regressions such as nu-SVM classification, C-SVM classification, one-class-SVM, nu-SVM regression, and epsilon-SVM regression. Sequential Minimal Optimization (SMO) algorithm for SVM based on kernels that are supporting classification and regression can be easily implemented with the help of this Library.  Below is the code of LIBSVM in C language:

LIBSVM machine learning algorithm
LIBSVM machine learning algorithm, image Source: GitHub

2. LIBLINEAR

It is another C based Library that utilizes to support vector machines. It is almost similar to LIBSVM but with some advanced features such as general purpose SVM solvers, Classification of SVM in multi-classes such as Crammer and Singer, one-vs-the-rest etc, Model Evaluation Cross-validation, probability estimates under logistic regression only, automatic parameter selection, balancing huge and unordered data, interfaces for others languages such as MATLAB, Octave, Python, Java, Ruby etc. It is generally used when huge mapped or unmapped data set is available for the SVM and available at the non-kernel platform. The diagram below shows the LIBLINEAR workflow:

LIBLINEAR machine learning algorithm
LIBLINEAR machine learning algorithm, image source: Research Gate

3. Recommender

Recommender is C library that utilizes in the machine learning systems. This is basically for recommendations or suggestions functions in machine learning systems. This C library does its job using the collaborative filtering.  The code in C language for recommender appears as shown below:

Recommender machine learning algorithm
Recommender machine learning algorithm, image source Medium

First, this recommender analyses the whole situation like the feedback of the users. It analyses what they are looking for and what their preferences are. Once it analyses, it learns by itself to predict what the user may like and starts finding similar products for the user.

Some of its features are collaborative filtering, not depending on external sources, an amazing running time (80 seconds for 10 million ratings). The memory footprint is around 160 MB. The following image depicts how recommender helps in recommending relevant items.

Recommender system machine learning algorithm
Recommender system machine learning algorithm, image source Kaggle

4. Neonrvm

Neonrvm is another machine learning library based on C which is experimental open source library. This used the popular RVM technique. The thing about this library is it is written in C programming language and also it includes a bit of binding of Python Programming Language. So basically, it is a mix of C and Python programming.  

This neonrvm makes use of expectation maximization which is one kind of a fitting method. This library of the general purpose machine learning is not a full-fledged or full features machine learning network. This mainly helps in the core training and the prediction functions. You have to use this library in accordance with other kits and tools.

You can manually include neonrvm.h or neonrvm.c in your programs in Machine learning and this would do. The compiler for this framework is the C99 compiler. The use of CMake would also be better.

5. Darknet

This is an out and out open source framework written in C and CUDA. The best thing about Darknet is it is fast and easy to install. This supports the basic functions like CPU computation.

You can install it in two methods: One is through openCV and the other one in CUDA. You need to first clone the repository and then try running the program. Now there are other cool things you can do with Darknet like DarkGO, Tiny Darknet etc.

6. Hybrid Recommender

Hybrid Recommendation System is yet another library which uses C programming. The main goal of this library is to give a bunch of scripts to different recommendation libraries like CF, CBF or the hybrid and train them with no coding at all!

This library supports the user- user collaborative filtering which is helpful in generating useful recommendations for users. It also supports other methods and techniques like grouping the algorithms, ranking, and regression etc. You can also combine one or more techniques to achieve the result. This system supports the KNN which is also known as lazy learning. This is user-user collaborative filtering.

7. KNN

K-Nearest Neighbors (KNN) is an essential classification algorithm that makes machine learning implementation easiest and efficient. The applications of this algorithm include data mining, intrusion detection, and pattern recognition. Because of its non-parametric feature, this is easy to implement real-life scenarios. Non-parametric is stand to the feature that assumes the distribution of data in different scenarios. The following is the pseudo code of KNN.

KNN machine learning algorithm
KNN machine learning algorithm, image source Research Gate

8. VLFeat

VLFeat is an algorithm that supports image standards comparisons along with local features extraction and comparison. This algorithm has codes for various features and process such as VLAD, SIFTS, Fisher Vector, Hierarchical k-means, SLIC superpixels, agglomerative information bottleneck, large-scale SVM training, and quick shift super-pixels. This algorithm supports MATLAB and different operating systems such as Linux, Windows, and Mac OS X.

Conclusion

Thus these are some of the libraries how C programming language is used in the general machine learning and its codes that help solve different purposes. You can step up into the machine learning world if you know how to code in C.

In fact, a lot of data scientists still use C to date for implementing a few operations. Also, you can learn all these properly if you take a machine learning certification training course. They teach you to make keep you focused and learn all these techniques.

This great article is contributed by Danish Wadhwa. He lives at simplilearn.com. He is a doyen of governing the digital content to assemble good relationships for enterprises or individuals. He is specialized in digital marketing, cloud computing, web designing and offers other valuable IT services for organizations, eventually enhancing their shape by delivering stupendous solutions to their business problems. Feel free to drop your queries and suggestions below in comment box.

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