1. Python Programming Language - Python is the go-to language for AI due to its readability and robust libraries. Variables, loops, functions, object-oriented programming.
2. Jupyter Notebook - An interactive coding environment used for writing and running Python code. Supports visualizations, markdown, and inline documentation. Ideal for AI experiments, model training, and tutorials.
3. NumPy and Pandas - Foundational libraries for numerical computation and data manipulation. Efficient matrix operations and math functions. Ideal for working with datasets (CSV, Excel, SQL).
4. Scikit-learn - A beginner-friendly machine learning library. Classification, regression, clustering, and preprocessing. Learning the basics of ML models like decision trees, SVM, and KNN.
5. TensorFlow or PyTorch - Leading deep learning frameworks. More production-oriented, backed by Google. Preferred for research and fast prototyping (easy to debug).
6. Google Colab - Free cloud-based platform with GPU/TPU support. No setup needed, runs Jupyter Notebooks in the browser. Beginners without access to high-end hardware.
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