12 trending AI tools individuals can leverage to boost their productivity

Artificial Intelligence or popularly known as AI has become omnipresent in each individual’s life. It is impacting every activity one is engaged in. Thus, leveraging AI in education and research is only enhancing and augmenting learners’ educational or learning experiences. And so, it’s imperative to learn trending AI tools as these can greatly enhance the skill of students, researchers or any professional. The idea is to prepare them for the evolving technological landscape.

Thus, here is a list including a few AI tools that students must learn to understand their potential applications:

TensorFlow: TensorFlow is an open-source machine learning framework developed by Google. It’s highly versatile and widely used for building various AI models, from simple linear regression to complex neural networks. Students can learn to implement algorithms, experiment with different architectures, and work on real-world projects.

PyTorch: Similar to TensorFlow, PyTorch is another popular open-source deep learning framework. It’s known for its dynamic computation graph, making it more intuitive for beginners. Students can use PyTorch to build neural networks, work on image and text processing, and delve into cutting-edge research

Scikit-Learn: This is a user-friendly machine learning library that provides a wide range of algorithms for classification, regression, clustering, and more. It’s great for students who are just getting started with AI and want to grasp the basics of machine learning concepts.

Keras: It is an easy-to-use high-level neural networks API that can run on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. It’s particularly useful for rapid prototyping and experimentation. Students can quickly build and test different neural network architectures.

Jupyter Notebooks: This provides an interactive environment for writing and running code, making them an excellent tool for learning and experimenting with AI algorithms. Students can create data visualizations, document their code, and share their findings.

OpenCV: It is a library designed for computer vision tasks. Students can use it to work with images and videos, implement image processing techniques, and even develop their own computer vision applications.

NLTK (Natural Language Toolkit): For students interested in natural language processing (NLP), NLTK is a valuable tool. It provides resources and tools for working with human language data, making it easier to analyze and process text.

IBM Watson Studio: This cloud-based platform offers a suite of tools for data scientists and AI developers. Students can collaborate on projects, build and deploy machine learning models, and experiment with various data sets.

Azure AI: Azure provides a range of AI services that students can leverage, such as cognitive services for vision, speech, and language understanding. They can also explore Azure Machine Learning for model building and deployment.

Google Colab: This is a free cloud-based platform that offers GPU support and allows students to write and execute Python code in a Jupyter Notebook-like environment. It’s a great way to experiment with AI models without worrying about hardware limitations.

Fast.ai: Tis AI tool offers practical deep learning courses and provides a library that simplifies training fast and accurate neural nets. It’s especially useful for students who want to quickly gain practical experience.

AutoML Tools: Platforms like Google AutoML, H2O.ai, and DataRobot offer automated machine learning solutions. These tools can help students understand the process of training and tuning models while abstracting some of the complexities.

In conclusion, any professional can explore these tools and apply their knowledge in their work. Remember to emphasise the importance of understanding the underlying concepts along with using these tools, as a strong foundation in AI fundamentals will be invaluable in their careers.

Disclaimer: This article has been written by Dr. Amit Kumar Goel. The views expressed in this article are that of the author only