The article introduces a tutorial focused on building a sentiment analysis model using TensorFlow and Python. It emphasizes the prerequisites for engaging with the tutorial, particularly basic knowledge of Python and installation of Python 3.9 or higher. Furthermore, it explains the concept of Machine Learning (ML), illustrating its function through real-world examples and the recognition of patterns in data. It also elaborates on why Python is favored in ML applications due to its flexibility and extensive library support, setting the stage for the sentiment analysis model to be developed in the tutorial.
Machine Learning (ML) is making the computer learn and make predictions by studying data and statistics, helping to identify patterns and make predictions.
Python, while not built specifically for ML, is adaptable and has numerous libraries, making it a great choice for building Machine Learning models.
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