If you’re interested in pursuing a career in machine learning, you’ll need to have a firm grasp of at least one programming language. But with so many languages to choose from, which one should you learn? Here are three of the most popular machine learning programming languages, along with a brief overview of each.
Python
Python is a programming language with many features that make it well suited for machine learning. It has a large and active community of developers who have contributed a wide variety of libraries and tools. Python’s syntax is relatively simple and easy to learn, making it a good choice for people who are new to programming. In addition, Python is free and open source, which means that anyone can use it and there are no licensing fees. Python also has strong support for scientific computing, which is important for machine learning because of the need to work with large datasets. Finally, Python integrates well with other software, making it possible to use machine learning algorithms in a range of different contexts.
The following are some of the most popular Python libraries / packages that are used for building machine learning models:
- NumPy: NumPy is a Python library that is used for scientific computing. It provides functions for working with large arrays and matrices, and it is also efficient for performing mathematical operations on those arrays. NumPy is a popular library for machine learning as it can be used to process and manipulate data.
- Pandas: Pandas is a Python library that is used for data analysis. It provides functions for reading and writing data, as well as for manipulating that data. pandas is a popular library for machine learning as it can be used to clean and prepare data for modeling.
- SciPy: SciPy is a Python library that is used for scientific computing. It builds on NumPy by providing additional functions for working with data. SciPy is also efficient at performing mathematical operations on data.
- Matplotlib: Matplotlib is a Python library that is used for plotting data. It provides functions for creating various types of plots, including line graphs, scatter plots, and histograms. matplotlib is a popular library for machine learning as it can be used to visualize data.
- Seaborn: Seaborn is a Python library that is used for statistical data visualization. It provides functions for creating more complex plots, such as heatmaps and time series plots. seaborn is a popular library for machine learning as it can be used to visualize the results of machine learning models.
- Scikit-learn: Scikit-learn is a Python library that is used for machine learning. It provides functions for training and evaluating machine learning models, as well as for preprocessing data. scikit-learn is a popular library for machine learning as it contains many useful tools for building and working with machine learning models.
- TensorFlow: TensorFlow is a Python library that is used for deep learning. It provides functions for creating complex neural network architectures, training those networks on large datasets, and deploying them in production environments. TensorFlow is a popular library for machine learning as it can be used to build powerful deep learning models.
- Keras: Keras is a Python library that is used for deep learning. It provides functions for creating simple neural network architectures, training those networks on large datasets, and deploying them in production environments. Keras is a popular library for machine learning as it can be used to quickly build prototype deep learning models
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PyTorch: PyTorch is an open-source library for deep learning that was developed by Facebook. It is widely used by researchers and developers to create a variety of different deep learning models.
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MXNet: MXNet is an open-source library for deep learning that was developed by Amazon. It is used by a number of different companies and organizations, including Amazon, Microsoft, and the University of Washington.
R
R is another popular language among data scientists and machine learning engineers. Like Python, it’s easy to learn and has a syntax that’s relatively straightforward. R is also advantageous in that it was designed specifically for statistical analysis, which makes it well-suited for tasks like data visualization.
The following are some of the most popular R libraries / packages that are used for building machine learning models:
- caret: The caret R package is one of the most widely used machine learning packages in R. The package provides a variety of functions for pre-processing data, training and evaluating machine learning models, and performing model selection.
- e1071: The e1071 R package provides functions for a variety of common machine learning tasks, such as classification and regression. The package also includes functions for working with support vector machines (SVMs), a popular type of machine learning model.
- gbm: The gbm R package provides an implementation of gradient boosting machines (GBMs), a powerful type of machine learning model. GBMs are particularly well-suited for problems where the goal is to predict a continuous target variable, such as in regression tasks.
- nnet: The nnet R package provides functions for training neural networks, a type of machine learning model that is inspired by the structure of the brain. Neural networks are particularly well-suited for tasks where the goal is to classify data into a finite number of classes, such as in classification tasks.
- randomForest: The randomForest R package provides an implementation of random forests, a type of machine learning model that is ensemble method that combines multiple decision trees to make predictions. Random forests are often used for classification and regression tasks
JavaScript
You might not expect to see JavaScript on a list of machine learning programming languages, but the fact is that JS is increasingly being used for these purposes. One reason for this is that JavaScript is able to run directly in web browsers, which makes it easy to deploy machine learning models without any additional infrastructure. Additionally, recent advancements in hardware (such as GPUs) have made JavaScript an even more attractive option for machine learning due to its speed and efficiency.
The following are some of the most popular Javascript libraries / packages that are used for building machine learning models:
- TensorFlow.js: TensorFlow.js is an open-source library that can be used for machine learning in the web browser. The library provides a variety of tools that can be used to create and train machine learning models, as well as to deploy them in the browser.
- Keras.js: Keras.js is a open-source library that provides a high-level API for creating and training machine learning models. The library can be used in conjunction with TensorFlow.js or other libraries such as NumPy.
- ml5.js: ml5.js is an open-source library that provides tools for creating machine learning applications in the web browser. The library is designed to work with the JavaScript programming language and can be used in conjunction with other libraries such as TensorFlow.js and keras.js.
- Brain.js: Brain.js is an open-source library that provides tools for creating neural networks in the web browser. The library is designed to work with the JavaScript programming language and can be used in conjunction with other libraries such as TensorFlow
Java
Java is another versatile language that’s popular among machine learning engineers. Like Python, Java has a number of libraries and frameworks that make working with data easy. However, one advantage that Java has over Python is that it’s faster to execute. This can be important when you’re working with large datasets or training complex machine learning models.
The following are some of the most popular Java libraries / packages that are used for building machine learning models:
- Weka: Weka is a Java-based machine learning library that contains a collection of algorithms for data mining tasks, such as classification, regression, and clustering. The library also includes tools for data pre-processing, visualization, and evaluation. Weka is open source software released under the GNU General Public License.
- Apache Mahout: Apache Mahout is a machine learning library that is also based on the Java programming language. The library’s algorithms are primarily designed for scalability and run on the Apache Hadoop platform. Mahout also includes a number of integrations with other Apache projects, such as Spark and HBase.
- DeepLearning4J: DL4J is a deep learning library for the Java programming language. The library includes a wide variety of algorithms for building and training neural networks, including support for convolutional and recurrent networks. DL4J can be used in conjunction with Weka for data pre-processing and visualization tasks.
- JSAT: JSAT is a machine learning library that is written in the Java programming language. The library includes implementations of many popular algorithms, such as Support Vector Machines, Linear Regression, and k-means clustering. JSAT also provides an interface to the WEKA machine learning software.
- Spark MLlib: Spark MLlib is a machine learning library that is included in the Apache Spark project. Spark MLlib provides a wide variety of algorithms, including classification, regression, clustering, and feature selection. The library is open source and released under the Apache License.
Mallet: Mallet is a machine learning package that is implemented in the Java programming language. The package includes a wide variety of algorithms for text classification, sequence labeling, and clustering. Mallet also provides an interface to the WEKA machine learning software
MATLAB
MATLAB is a commercial programming language that’s used in a variety of industries, including finance, automotive, and aerospace. MATLAB is popular among machine learning engineers because it has a number of built-in functions for working with data, matrices, and algorithms. MATLAB is also widely used in academia, so if you’re looking to pursue a career in machine learning research, MATLAB would be a good language to learn.
Octave
Octave is an open-source programming language that’s similar to MATLAB. Octave is used for numerical computation and has a syntax that’s similar to MATLAB. Octave is a good choice if you want to use a MATLAB-like language but don’t want to pay for the commercial version of MATLAB.
Scala
Scala is a relatively new language that combines features of both object-oriented and functional programming paradigms. Scala is also fully compatible with Java, which means that any Java code can be run in Scala without modification. That makes Scala a good choice for projects that need to interface with existing Java codebases. Scala is also fast and scalable, making it well-suited for big data applications like machine learning. However, Scala can be more difficult to learn than some other languages due to its complex syntax.
C++
C++ is a high-performance language that was originally designed for system programming tasks like operating system development and video game development. These days, C++ is still used for those sorts of tasks but has also found new life in applications like machine learning where speed is critical. C++ can be difficult to learn due its complex syntax and error-prone nature (e.g., memory management), but it can offer significant performance advantages over interpreted languages like Python or Java when used correctly.
Julia
Julia is a newer language that’s specifically designed for scientific computing. It combines the ease-of-use of Python with the speed of C++ to create a language that’s both powerful and easy to learn. Julia also has a number of excellent libraries for machine learning, such as Flux and Knet. However, because Julia is still relatively new, there aren’t as many resources available for those just starting out.
Conclusion
There are many different programming languages out there, but not all of them are well-suited for machine learning tasks. If you’re looking to get started in this field, your best bet is to learn one of the two most popular machine learning programming languages: Python and R. Each has its own unique advantages that make it well-suited for specific tasks related to machine learning. Whichever language you choose to learn, though, be sure to take advantage of the wealth of resources (including online courses and tutorials) that are available to help you get up to speed quickly.
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I found it very helpful. However the differences are not too understandable for me