- Artificial Intelligence
- Machine learning
Table of Contents
Machine learning is one of the most fast-growing markets. With a 38.6% CAGR and 91% of American wealthiest companies showing interest in investing in machine learning solutions, the market’s value is projected to hit $152.2 billion by 2028.
Modeling, data management, and data analysis are only a part of a rich spectrum of machine learning software possibilities. Programmers love their machine learning development tools because they increase the accuracy of the models, automate mundane tasks and reduce the need for unnecessary toil of manual rummaging.
Every tool has its unique features and peculiarities. In today’s article, we’ll be looking at the 12 best tools in machine learning, so you could figure out what language or which machine learning library to use for your objectives. We will discuss the specific application of each tool, as well as their benefits. Let’s start with Python-based solutions.
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Python-based popular machine learning tools
Python programming solutions dominate the machine learning market. According to the 2021 Kaggle Survey, more than 80% of data scientists pick Scikit-learn as their framework of choice. The language is versatile and lets developers create almost anything they want. Here are six out of the top 10 Python machine learning libraries, frameworks, and other tools.
| Tool | Specific use | Benefits |
|---|---|---|
| 1. Scikit-learn | It’s a widely-used library for statistical modeling. Scikit-learn is well-known for its data regression classification, preprocessing, and regression clustering algorithms. It also enables users to access other libraries written in Python. | – An open-source library that enables users to redirect and modify the code; – Well-organized API documentation; – Large community makes navigating and mastering Scikit easier. |
| 2. TensorFlow | This is an end-to-end open-source platform for creating ML models. It offers robust machine learning production without any language limitations. With TensorFlow, users can build State-of-the-Art models, conduct intuitive debugging, and do immediate iterations without sacrificing performance or speed. The platform can handle sizable machine learning tasks like neural networks development and distributed training thanks to its Distribution Strategy API. | – Compatible with languages like Python, JavaScript, C++, C#, Swift, and Ruby; – Frequent new releases with new features and quick updates backed by Google; – Automatically adjusts to different hardware configurations; – Pipelining and debugging. |
| 3. PyTorch | Python open-source software library that helps implement Natural Language Processing, cross-validation algorithms, and build models for computer vision. Based on the Torch library, it supports fast prototyping, a wide range of deep learning models, and helps write more readable code. The library is mostly developed and maintained by the Meta AI lab. | – Supports parallel processing, GPU, CPU, and distributed training; – Uses debugging tools (ipdb and pdb); – The fast path from prototyping to production. |
| 4. NLTK | A python-based platform for natural language processing (NLP). The NLTK library contains various tools for tagging, tokenization, semantic reasoning, parsing, and stemming. Users can check if product reviews are positive or negative, build chatbots, and perform sentiment analysis through its text classifier tools. | – Gives quick access to numerous datasets, word vectors, and text corpora; – Supports multiple languages and GPU acceleration. |
| 5. Keras | It’s one of the deep learning frameworks in machine learning with APIs for reducing cognitive load. It minimizes the number of user actions required for common use cases and provides clear feedback upon any error. | – Supports multiple back-end neural computation engines; – User-friendly interface; – Modular and extensible architecture. |
| 6. NumPy | This library supports large, multidimensional arrays and matrices and has a lot of high-level functions to run on these arrays. NumPy’s chief purpose is to increase the speed of the Python list. It does so through ndarray — an array object that improves the traditional list’s speed by 50-fold. | – Robust computing capabilities; – Reduces memory usage to store data. |
From one-person startups to multi-billion organizations — Python-based tools and libraries are the go-to solutions for various businesses. For instance, Netflix, a company with over 214 million paying subscribers, uses Python to build machine learning models for content delivery networks, test algorithms, clean up data, and conduct information analysis. Python-based solutions like PyTorch, Keras, and TensorFlow are intrinsic parts of SDKs Netflix’s development teams use.
Popular machine learning tools for C++
The next machine learning tools and frameworks we’ll examine are for C++. C++ programming language is ideal for adaptive caching and developing sizable data libraries — ML-related solutions that need to be fast-paced are written with C++ and Python’s bindings. Google’s MapReduce, MongoDB, most of the deep learning libraries listed below have been implemented using C++. Frameworks that have been based on this language are known for their high throughput and ultra-low latency. Here are some of the top C++ machine learning tools.
| Tool | Specific use | Benefits |
|---|---|---|
| 1. Shogun | Open-source library for algorithm development and other machine learning-related tasks. Incorporates various general-purpose tools, data representations, and algorithm classes. | – Open-source solution; – Offers advanced classes of ML algorithms like support vector machine and kernel methods; – Substantial testing infrastructure. |
| 2. Caffe | Deep learning framework that lets you work with extensible code and expressive architecture for Convolutional Neural Network development. A lot of startup prototypes and academic research projects use this solution. | – Extremely fast: with the fitting hardware, the framework can process over 60M images per day; – Extensible code (forked by over 1,000 developers); – Has an interface for Python. |
| 3. DyNet | It’s a neural network library that is written in C++ with bindings in Python. Has support for reinforcement learning, graph structures, and NLP. | – Ideal for complex structures (language modeling, graph parsing, speech recognition, and morphology); – Runs efficiently on GPU and CPU; – Dynamic operation batching. |
With unique advantages like performance characteristics and memory management, C++ is one of the most efficient languages for developing fast, scalable machine learning tools for big data analysis. Big tech companies such as Apple, Google, and Microsoft develop their software using C++.
R-based popular machine learning tools
R is one of the leading programming languages for computational and statistical analysis. Researchers in the field of data science have been using this language for a few years now. Its chief features include robust, open-source visualization libraries and the possibility to run code without a compiler.
R has a lot of open-source machine learning frameworks and packages. Here are some of the machine learning tools examples that are used by R developers and data scientists.
| Tool | Specific use | Benefits |
|---|---|---|
| 1. Tidyr, Dplyr | Tidyr helps users tidy their data. It does so through plotting and using different R reshaping functions like data format conversion and column combination. Dplyr is a part of Tidyr that helps with data manipulation challenges. | – Efficient code; – Directs connections to external databases; – Reduces clutter and optimizes coding with the help of chain functions. |
| 2. Knime | It’s an open-source solution, used primarily for data analytics and form reports. Knime has pre-built templates for fraud detection, predictive modeling, customer churn, etc. | – Easy to deploy, install, and learn; – Integrates with the code of various programming languages like C++, C, R, Java, JavaScript, and Python; – Library of over 1,500 functions and standard ML algorithms; – Easy transition from other software solutions such as SAS and Alteryx. |
| 3. Tidyquant | It’s designed for modeling and scaling financial analyses using tidyverse tools like ggplot2, readr, and purrr. | – Creates top financial visualizations with ggplot2 functionality; – Integrates the quantitative analysis functionality of TTR, xts, quantmod, PerformanceAnalytics, and zoo tool packages. |
With R tools, one can develop an efficient data science or machine learning model. For instance, Airbnb has an internal tool package called Rbnb. It’s an R-powered ML kit that the startup uses for automation of host or guest matching and prediction of re-booking rates. Companies like IBM, Twitter, and Google also use R.
Which machine learning framework is the best
It depends on the functionality your project requires. Let’s see what you will be getting with each programming language.
Python
It’s a go-to programming language for most data scientists. The language has a large following and numerous guides available only for free. Python’s simple syntax makes it super intuitive and relatively easy to get into. The simplicity of the language allows developers to focus more on algorithms and workflows rather than on the complexity of the language.
C/C++
C++ lets you efficiently control CPU and memory. Most frameworks are compatible with C++, meaning you can access a wide range of tools. It’s a perfect language when high speed is something you prioritize. C++ is also usually utilized in large enterprise-scale projects.
R
R is ideal for data visualization and high-level statistics. It represents a combination of datasets and rapid prototyping that creates handy ML models. R has an extensive collection of tools and libraries to help you develop your project-specific solution.
Python, C++, and R are three of the most popular programming languages, that are used in machine learning. Each connects to multiple machine learning frameworks and libraries. These languages boast an extensive ecosystem of libraries, active community, commendable runtime performance, and dedicated tool support — hope this article helped and its list of machine learning tools helped you figure out what are machine learning frameworks and how they extend and enrich the functionality of each language.
Each tool comes with its specific features. We can help you maximize your project’s possibilities and minimize losses by choosing the best framework fit for your objectives, and experimenting with different libraries, including Tensorflow, Keras, Pytorch, and nltk. Check out what ML tasks we’re accomplishing with our custom AI software development services and contact us.
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