Python is a high-level, easy-to-learn programming language that is widely used in artificial intelligence and machine learning due to its readability, vast collection of libraries, and large community of developers.
Imagine you want to build a Lego castle. Instead of starting from scratch, with Python, you have ready-made Lego set boxes (libraries) that you can use. Each box contains different pre-made parts (functions) that make building your castle (AI model) fast and fun!
Python is an interpreted, high-level, and general-purpose programming language that is known for its simplicity and readability. It allows developers to focus on problem-solving and algorithm development rather than intricate syntactical details present in some other languages - making it a popular choice for those venturing into the field of AI and ML for the first time.
Python’s widespread popularity in the AI/ML field is also due to its extensive ecosystem of scientific libraries and frameworks. Libraries such as NumPy and Pandas make data manipulation easy, while Matplotlib is used for data visualization. Most importantly, for the computation-intensive work of AI and ML, Python offers libraries such as TensorFlow, PyTorch, and Scikit-Learn. These libraries provide high-level API for building and training complex machine learning models.
TensorFlow, an open-sourced library developed by Google Brain, offers versatile tools for machine learning, ranging from running basic calculations in a manner similar to NumPy, to implementing multi-layer artificial neural networks. PyTorch, a Python library based on Torch, is considered more user-friendly and less error-prone, making it a popular choice for beginners. Scikit-Learn is best known for its ease of use and practicality for traditional, often less-computation heavy machine learning algorithms.
Furthermore, Python’s large, active developer community has made a significant contribution to its applicability in AI and ML. The readability of Python code and the language’s cultural emphasis on clear, explanatory comments encourage scientists and engineers to share their work, which leads to a wealth of Python-based tutorials, pre-trained models, and AI development tools being readily available to draw upon.
TensorFlow, PyTorch, Scikit-Learn, NumPy, Artificial Intelligence, Machine Learning (ML),, Data Visualization, Interpreted Language, High-Level Language, General-Purpose Language, AI Libraries, Matplotlib, Pandas.