In the ever-evolving landscape of technology, Artificial Intelligence (AI) has become a well known force , especially in the last couple of years. The impact of this force is revolutionizing the world we see today and is now changing industries. As future STEM workers, we’re first hand seeing history being made, and we can only anticipate more changes to come. With this transformative force, though comes a potentially scary element, AI also presents a myriad of opportunities. There is an abundance of AI tools and resources available that simplify the lives of learners and working professional alike, as well as allowing for faster results. These tools range from powerful libraries for machine learning and deep learning to intuitive platforms for natural language processing and computer vision, to interactive tools like ChatGPT.

Students can leverage these resources to delve into cutting-edge research and thoroughly immerse themselves and use their creativity in ways that were thought to be fictional. These tools have the potential to make life easier, both in your student life and beyond as you enter the professional world. Below, you’ll find a list of AI tools that you can use, as well as a simple explanation of what thy are.

Libraries and Platforms for Machine Learning AI Tools:

1. Google Colab: Google Colab is a free cloud-based platform that provides a Jupyter notebook environment with free access to GPUs and TPUs. It’s great for running machine learning models, experimenting with code, and collaborating with others.
2. Jupyter Notebook: Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. It’s widely used in scientific computing, data analysis, and machine learning.
3. TensorFlow Playground: TensorFlow Playground is a web-based interactive visualization tool for training neural networks. It’s great for learning the basics of neural networks and experimenting with different network architectures and parameters.
4. Kaggle: Kaggle is a platform for data science and machine learning competitions, datasets, and notebooks. It provides free access to datasets, kernels (Jupyter notebooks), and competitions, allowing students to learn from real-world data and compete with others.
5. scikit-learn: scikit-learn is a free machine learning library for Python. It provides simple and efficient tools for data mining and data analysis and is widely used in academia and industry for building machine learning models.
6. NumPy: NumPy is a fundamental package for scientific computing with Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.
7. matplotlib: matplotlib is a Python plotting library that produces publication-quality figures in a variety of formats. It’s great for visualizing data and results in scientific and engineering projects.
8. SciPy: SciPy is a Python library for scientific computing and technical computing. It provides functions for optimization, integration, interpolation, linear algebra, and more, making it useful for a wide range of scientific and engineering applications.
9. OpenCV: OpenCV (Open Source Computer Vision Library) is a free computer vision library that provides a wide range of functionalities for image and video processing. It’s widely used in research and industry for applications such as object detection, face recognition, and image segmentation.
10. Anaconda: Anaconda is a free and open-source distribution of Python and R programming languages for scientific computing, that aims to simplify package management and deployment. It comes with a wide range of pre-installed packages commonly used in data science and machine learning.

Interactive AI:

1. OpenAI Codex: OpenAI Codex is an AI model designed to interpret and generate code in natural language. It’s built on top of GPT (like ChatGPT), enabling it to understand and generate code in over a dozen programming languages. It can be incredibly useful for students learning to code or working on programming projects.
2. TensorFlow and PyTorch: TensorFlow and PyTorch are two of the most popular deep learning frameworks. They provide tools and APIs for building and training neural networks, making them essential resources for students studying deep learning and neural networks.
3. Fast.ai: Fast.ai is a deep learning library built on top of PyTorch. It provides high-level abstractions and pre-trained models that make it easy to get started with deep learning, even for beginners.
4. IBM Watson: IBM Watson is a suite of AI tools and services offered by IBM. It includes tools for natural language processing, computer vision, speech recognition, and more. Watson can be used for a wide range of applications, from building chatbots to analyzing medical images.
5. Microsoft Azure Cognitive Services: Microsoft Azure Cognitive Services is a collection of AI services provided by Microsoft. It includes services for vision, speech, language, and decision-making, making it easy to add AI capabilities to applications without needing to build and train models from scratch.
6. Hugging Face Transformers: Hugging Face Transformers is a popular library for natural language processing (NLP) tasks. It provides pre-trained models for a wide range of NLP tasks, along with easy-to-use APIs for fine-tuning and deploying these models.
7. spaCy: spaCy is an open-source library for natural language processing in Python. It provides efficient tools for tokenization, part-of-speech tagging, named entity recognition, and more, making it a valuable resource for students studying NLP.
8. AllenNLP: AllenNLP is a deep learning library specifically designed for natural language processing tasks. It provides pre-built models and tools for tasks such as text classification, semantic role labeling, and coreference resolution.