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Understand the art of prompt engineering to maximize AI language model outputs. Covers prompt design, optimization techniques, and real-world applications with examples.
This course teaches applied machine learning using Python, covering data preprocessing, model building, evaluation, and deployment. You will work with libraries like scikit-learn, pandas, and NumPy on real-world datasets to build practical skills.
Master deep learning concepts and techniques using TensorFlow. This course covers convolutional and recurrent neural networks, LSTMs, and how to apply these models in real-world AI applications for image and text data.
Learn natural language processing with state-of-the-art transformer models such as BERT and GPT. This course covers tokenization, embeddings, attention mechanisms, and fine-tuning transformer models for various NLP tasks.
Explore how artificial intelligence can transform business strategies and operations. Learn about AI-driven automation, decision-making, predictive analytics, and integration in various industries to boost efficiency and growth.
Learn computer vision techniques and applications using OpenCV. Covers image processing, object detection, facial recognition, and video analysis with hands-on coding exercises.
2 Reviews on Introduction to Prompt Engineering
Daichi Kuroda
講義内容がよく整理されていて、理解しやすかったです。
Natalie Young
Clear and engaging delivery of complex topics.