Identifying and scoping the use case, such as customer segmentation, fraud detection, or recommendation engines.
02.
Collecting and cleaning datasets from structured databases, CSV files, or web scraping tools.
03.
Training models using Python libraries such as scikit-learn for classical ML, TensorFlow and PyTorch for deep learning, and XGBoost for high-performance gradient boosting.
04.
Implementing Natural Language Processing (NLP) with NLTK, spaCy, and Transformers to extract meaning from text and generate contextual responses.
05.
Performing computer vision tasks using OpenCV and YOLO (You Only Look Once) for object detection, facial recognition, and automated inspection.
06.
Deploying models using Flask, FastAPI, and Docker containers to integrate AI into web apps and enterprise software.
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