Everything you need to know about AI Engineering – Hands-on from Algorithms, Programming to Real Projects
What you will learn
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Master Python for AI: Write efficient Python code, essential for AI and ML programming tasks.
Data Preprocessing Skills: Prepare, clean, and transform data to enhance model performance.
Statistical Knowledge: Apply core statistics to understand data patterns and inform decisions.
Build Machine Learning Models: Develop and fine-tune ML models for classification, regression, and clustering.
Deep Learning Proficiency: Design and train neural networks, including CNNs and RNNs, for image and sequence tasks.
Utilize Transfer Learning: Adapt pre-trained models to new tasks, saving time and resources.
Deploy ML Models with APIs: Create scalable APIs to serve ML models in real-world applications.
Containerize with Docker: Package models for portable deployment across environments.
Monitor and Maintain Models: Track model performance, detect drift, and implement retraining pipelines.
Complete ML Lifecycle: Master end-to-end AI project skills, from data to deployment and ongoing maintenance.
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