20 Best Chat GPT’s Free AI Tools List

With the age of technology, AI provides changes in many sectors. AI has transformed how we work and live by automating tasks, analyzing data, and creating insights. Nevertheless, unlocking the power of AI usually demands expenditure, which can make it unreachable for several users. Won’t it be fascinating when we say here is a trove of free AI tools ready for you to use from the wealth of AI that Chat GPT provides, all at no cost?

For this article, we have compiled a list of free AI tools that would help equip you with the necessary skills in the AI world.

Why Use Free AI Tools?

First, let us explore why such free AI tools are necessary before we jump into the thrilling world of cost-free AI tools.

1. Cost-Effective

Cost-effectiveness is one of the most significant benefits of utilizing free AI tools. The problem is that traditional AI solutions may cost too much to allow individuals, start-ups, and small business entities to use them. The use of free AI tools enables anybody to have access to AI power with AI solutions at no high cost.

2. Wider Accessibility

Free AI tools broaden the accessibility of AI technology. Regardless of financial resources, they empower individuals and organizations to leverage AI for solving real-world problems, exploring creative projects, and enhancing productivity.

List of Free AI Tools by Chat GPT

Now, let’s explore a selection of free AI tools by Chat GPT, along with examples to illustrate their capabilities.

1. TensorFlow

Open source machine learning frameworks: tensor flows.

Features: Open source machine learning frameworks: tensor flows. Thanks to its all-inclusive ecosystem of tools, libraries, and community resources, it is one of the best for deep learning and neural networks.
Usage: TensorFlow has a wide worldwide distribution, being especially common in the United States, China, and India. This is an active field of research and industrial implementation, covering areas like deep learning, NLP, computer vision, and others.
Example: You want to create a deep-learning model to classify images. It becomes easier for you to develop and tune your model with TensorFlow.

2. PyTorch

PyTorch is another powerful machine learning tool that excels in flexibility and multiple computations.

Features: PyTorch is another powerful machine learning tool that excels in flexibility and multiple computations. People widely use it for natural language processing (NLP) and computer vision tasks.
Usage: PyTorch is a favorite tool among researchers and practitioners, particularly in the United States, Canada, and the United Kingdom. It has gained traction in the deep learning community for its flexibility and dynamic computation graph.
Example: You’re working on a sentiment analysis project for social media data. PyTorch’s dynamic computation graph can help you efficiently design and train your NLP model.

3. scikit-learn

sci-kit-learn is a popular tool for machine learning in Python, offering straightforward and efficient tools for data mining and analysis.

Features: sci-kit-learn is a popular tool for machine learning in Python, offering straightforward and efficient tools for data mining and analysis.
Usage: Scikit-learn enjoys popularity in the United States, Europe, and Asia. Primarily employed for machine learning and data mining tasks, this invaluable tool serves data scientists and engineers worldwide.
Example: You have a dataset and need to perform classification or regression. scikit-learn offers many algorithms and tools to streamline your machine-learning tasks.

4. OpenCV

OpenCV (Open Source Computer Vision Library) is a versatile tool for computer vision applications.

Features: OpenCV (Open Source Computer Vision Library) is a versatile tool for computer vision applications. Primarily, it is used for image and video analysis.
Usage: Globally, people rely on OpenCV, particularly in the United States, Russia, and India. This indispensable tool drives computer vision tasks and is pivotal in robotics, image processing, and more applications.
Example: OpenCV simplifies complex computer vision tasks as you’re developing a system to detect objects in real-time video streams. Additionally, it provides various tools for object detection, tracking, and more.

5. NLTK

NLTK (Natural Language Toolkit) is a Python library for working with human language data.

Features: NLTK (Natural Language Toolkit) is a Python library for working with human language data. It is an invaluable tool for text processing and NLP tasks. Check also, Bard’s Best AI Tools.
Usage: NLTK is popular in the United States and Europe, especially in the United Kingdom and the Netherlands. It is a go-to natural language processing (NLP) research and application tool.
Example: If you want to build a chatbot that understands and generates human-like responses, NLTK provides pre-built language models and text processing functions to make your task easier.

6. spaCy

spaCy is a popular NLP library that excels at processing large volumes of text data quickly and efficiently.

Features: spaCy is a popular NLP library that excels at processing large volumes of text data quickly and efficiently.
Usage: spaCy garners widespread adoption in the United States, Germany, and France, where it finds application in various NLP tasks, such as text parsing and entity recognition
Example: When tackling a project that requires named entity recognition or part-of-speech tagging, spaCy’s pre-trained models and user-friendly APIs come to the rescue. These tools efficiently streamline these tasks, ensuring a smoother workflow.

7. Gensim

Gensim is a library for topic modeling and document similarity analysis

Features: Gensim is a library for topic modeling and document similarity analysis. It’s ideal for extracting semantic meaning from text data.
Usage: Globally, Gensim holds a strong presence, particularly in the United States, India, and the United Kingdom. It proves invaluable for conducting topic modeling and document similarity analysis.
Example: Suppose you’re a researcher dealing with a vast corpus of text documents. In this scenario, Gensim can be your go-to tool to pinpoint common topics and themes in the data.

8. Kaldi

Kaldi is an open-source speech recognition toolkit offering acoustic and language modeling tools.

Features: Kaldi is an open-source speech recognition toolkit offering acoustic and language modeling tools.
Usage: Moreover, Kaldi is frequently employed in the United States, Germany, and China, playing a pivotal role in speech recognition research and applications.
Example: For instance, if you’re developing a speech recognition system for a voice assistant, Kaldi’s tools can greatly aid in training acoustic models and decoding speech.

9. Apache OpenNLP

Apache OpenNLP is a machine learning-based toolkit for working with natural language text.

Features: Apache OpenNLP is a machine learning-based toolkit for working with natural language text. It offers entity recognition, parsing, and more.
Usage: Globally adopted, Apache OpenNLP enjoys a strong presence in the United States, the United Kingdom, and India, serving as a preferred machine learning toolkit for NLP tasks.
Example: If you require named entity extraction and text document parsing, turn to Apache OpenNLP as your primary tool for these NLP tasks.

10. Jupyter Notebook

Jupyter Notebook is an interactive data analysis and visualization environment.

Features: Jupyter Notebook is an interactive data analysis and visualization environment. Data scientists and researchers extensively use it to share and document code.
Usage: Moreover, Jupyter Notebook boasts a substantial global user base, particularly in the United States, India, and Brazil. This versatile platform is widely favored for data analysis and scientific computing.
Example: Suppose you aim to present your data analysis and visualizations to colleagues or clients. Jupyter Notebook empowers you to craft interactive and shareable notebooks.

11. Orange

Orange is an open-source data visualization and analysis tool. It simplifies the processes of data mining, machine learning, and data visualization.

Features: Orange is an open-source data visualization and analysis tool. It simplifies the processes of data mining, machine learning, and data visualization.
Usage: Orange is known globally but is particularly popular in Europe, with a strong presence in Slovenia, Italy, and the Netherlands. It’s a versatile tool for data visualization and analysis.
Example: You have a dataset and want to create visualizations to gain insights. Orange provides a user-friendly interface to explore and analyze your data.

12. RapidMiner

RapidMiner provides an integrated environment for data science, machine learning, and analytics.

Features: RapidMiner provides an integrated environment for data science, machine learning, and analytics. Moreover, it offers a user-friendly GUI for building and deploying predictive models.
Usage: With a global user base and a strong presence in the United States, Germany, and Brazil, RapidMiner is a robust platform for machine learning, data mining, and advanced analytics.
Example: You’re a business analyst without coding experience but want to build predictive models. RapidMiner allows you to create models with its intuitive interface.

13. DeepAI

DeepAI, an online platform, provides diverse AI services, such as image generation, style transfer, and text generation.

Features: DeepAI, an online platform, provides diverse AI services, such as image generation, style transfer, and text generation.
Usage: With a user base spanning the United States, China, and India, DeepAI is a go-to choice for various AI resources, including image recognition and text generation.
Example: Suppose you’re eager to create art-inspired images rapidly for your creative project. In this case, DeepAI’s image-generation tool will bring your artistic vision to life.

14. Weka

Weka offers a comprehensive array of machine-learning algorithms for data mining tasks.

Features: Weka offers a comprehensive array of machine-learning algorithms for data mining tasks. Furthermore, it provides a suite of tools for data pre-processing, classification, regression, and more.
Usage: With a global footprint, Weka enjoys significant popularity in the United States, Germany, and the United Kingdom, where it serves as a versatile resource for data mining tasks.
Example: If you’re a student embarking on a machine learning project, Weka’s user-friendly interface facilitates the exploration and application of diverse algorithms.

15. Orange3

Features: Orange3, on the other hand, is an upgraded iteration of Orange, providing a broader range of data analysis and machine learning tools.
Usage: Globally recognized, Orange3 boasts users across Europe, the United States, and India. It stands as open-source software for data visualization and analysis.
Example: If you’re striving to conduct advanced data analysis and visualization, consider leveraging Orange3’s additional features for crafting intricate workflows.

16. FastText

FastText, a library for efficient text classification and representation learning, is renowned for its swift training and testing of text-based models.

Features: FastText, a library for efficient text classification and representation learning, is renowned for its swift training and testing of text-based models.
Usage: FastText is globally embraced, with a significant user base in the United States, India, and China, primarily due to its robust word representations and sentence classification capabilities.
Example: If you’re developing a text classification system to manage extensive text datasets, opt for FastText for its unparalleled efficiency and speed.

17. SpaCy2Java

Features: SpaCy2Java is a library for integrating SpaCy’s NLP capabilities into Java applications.
Usage: SpaCy2Java is recognized globally, with usage in the United States, Germany, and India. It enables the conversion of SpaCy models for use in Java applications.
Example: You’re a Java developer working on a text analysis project. SpaCy2Java allows you to leverage SpaCy’s NLP functions within your Java-based application.

18. BERT as a Service (BaaS)

BERT as a Service is an implementation of the BERT (Bidirectional Encoder Representations from Transformers) model as a service, allowing you to use BERT's capabilities for various NLP tasks.

Features: BERT as a Service is an implementation of the BERT (Bidirectional Encoder Representations from Transformers) model as a service, allowing you to use BERT’s capabilities for various NLP tasks.
Usage: BERT as a Service is used worldwide, with a strong presence in the United States, China, and the United Kingdom. It offers access to BERT models for NLP tasks.
Example: You’re working on a sentiment analysis project, and you want to take advantage of BERT’s state-of-the-art language understanding. BaaS simplifies the integration of BERT into your application.

19. Mycroft AI

Mycroft AI is an open-source voice assistant platform that enables the creation of custom voice-controlled applications.

Features: Mycroft AI is an open-source voice assistant platform that enables the creation of custom voice-controlled applications.
Usage: Mycroft AI is used globally but is particularly popular in the United States, Germany, and Canada. It’s an open-source voice assistant that can be customized for various applications.
Example: You want to develop your own voice assistant for a specific task or industry. Mycroft AI provides the framework and tools to build a tailored voice assistant.

20. Rasa

Rasa is an open-source framework for building conversational AI.

Features: Rasa is an open-source framework for building conversational AI. It allows you to create chatbots and virtual assistants with natural language understanding and dialogue management.
Usage: Rasa is recognized worldwide, with users in the United States, India, and the United Kingdom. It’s an open-source conversational AI platform for building chatbots and voice assistants.
Example: You’re building a customer support chatbot with advanced dialogue capabilities. Rasa enables you to create a highly interactive and context-aware chatbot for your website or application.

Also, check out Bard Free AI Tools.

Tips and Best Practices

To maximize the utility of these free AI tools, consider the following tips:

Clearly define your goals and objectives before using AI tools to ensure their effective application.
Experiment and iterate to fine-tune the output to match your specific requirements.
Stay updated with Chat GPT’s latest offerings and features for continued improvement.

Real-World Examples

Chat GPT’s free AI tools have been instrumental in numerous real-world scenarios.

Content Creation: Freelancers have used these tools to create compelling marketing content.
Education: Teachers have employed AI for personalized tutoring.
Healthcare: Researchers have leveraged AI for analyzing medical data.

Conclusion

Free AI tools by Chat GPT empower individuals and organizations to tap into the potential of AI without financial constraints. Whether you’re a developer, content creator, educator, or entrepreneur, these tools can enhance your capabilities and open doors to new possibilities. So, why wait? Dive in, experiment, and discover AI’s limitless opportunities.