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AI Tools and Frameworks for Beginners

Python: Widely used in AI due to its simplicity and the extensive support of libraries and frameworks.
R: Popular for statistical analysis and data visualization, useful in certain AI applications.

TensorFlow: Developed by Google, TensorFlow is a powerful and flexible framework for building machine learning models. It’s well-documented and has a large community.
- TensorFlow official tutorials
- Coursera TensorFlow in Practice
- TensorFlow documentation
Keras: A high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It’s user-friendly and suitable for beginners.
- Keras documentation
- Keras official tutorials
- Deep Learning with Python (book by François Chollet)
PyTorch: Developed by Facebook, PyTorch is known for its dynamic computation graph and ease of use, making it popular for research and learning.
- PyTorch official tutorials
- Deep Learning with PyTorch (book by Eli Stevens, Luca Antiga, and Thomas Viehmann)
- Fast.ai courses
Scikit-learn: A Python library for simple and efficient tools for data mining and data analysis. It provides a range of algorithms for classification, regression, clustering, and dimensionality reduction.
- Scikit-learn documentation
- Introduction to Machine Learning with Python (book by Andreas C. Müller and Sarah Guido)
Jupyter Notebooks: 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 for experimenting with code and data analysis.
- Jupyter documentation
- Data Science Handbook (book by Jake VanderPlas)

Matplotlib: A plotting library for Python which provides an object-oriented API for embedding plots into applications.
- Matplotlib documentation
- Python Data Science Handbook (book by Jake VanderPlas)
Seaborn: Built on top of Matplotlib, Seaborn provides a high-level interface for drawing attractive and informative statistical graphics.
- Seaborn documentation
- Python Data Science Handbook (book by Jake VanderPlas)
Plotly: An interactive graphing library that can create a wide range of visualizations, including 3D plots.
- Plotly documentation
- Plotly Express tutorials

Pandas: A Python library providing data structures and data analysis tools, particularly useful for handling tabular data.
- Pandas documentation
- Python for Data Analysis (book by Wes McKinney)
NumPy: A fundamental package for scientific computing with Python, providing support for arrays and matrices.
- NumPy documentation
- Python Data Science Handbook (book by Jake VanderPlas)

Coursera: Offers courses from leading universities and companies, including the “Machine Learning” course by Andrew Ng and the “Deep Learning Specialization” by Andrew Ng.
- Coursera AI courses
edX: Provides courses from institutions such as MIT and Harvard, covering various aspects of AI and machine learning.
- edX AI courses
Udacity: Known for its “Nanodegree” programs, including the AI and Machine Learning Nanodegrees.
- Udacity AI courses
Kaggle: An online platform for data science competitions, it also offers datasets and notebooks that can be a great resource for practical learning.
- Kaggle Learn courses

Anaconda: A distribution that includes Python and many popular data science libraries, along with tools like Jupyter Notebook.
- Anaconda documentation
Google Colab: A free cloud-based Jupyter Notebook environment provided by Google, which offers free access to GPUs.
- Google Colab documentation and tutorials

Google Cloud AI: Provides a suite of AI and machine learning services and tools, including AutoML and AI Platform.
- Google Cloud documentation and tutorials
AWS (Amazon Web Services) AI: Offers a range of AI services and tools, including SageMaker for building, training, and deploying models.
- AWS documentation and tutorials
Microsoft Azure AI: Provides various AI and machine learning services, including Azure Machine Learning.
- Azure documentation and tutorials