Data Science Diploma

Overview

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Learning Outcomes

• Optimize Python code for high-performance data manipulation tasks.
• Apply advanced feature engineering and data cleaning techniques on messy, real- world data.
• Implement regularization techniques to prevent overfitting in complex models.
• Master advanced ensemble methods like XGBoost and LightGBM for classification and regression.
• Design and evaluate models for imbalanced datasets and time-series forecasting.
• Build and implement simple recommendation systems and NLP applications.
• Use dimensionality reduction techniques (PCA, t-SNE) to simplify high-dimensional data.
• Deploy machine learning models into simple web applications using Streamlit.

Who should Enroll

• Intermediate level learners with basic statistics knowledge (no prior Python experience required).

Instructor

asmaa mohamed

Artificial intelligence and data science

1 Training Courses20 Learners

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