نظرة عامة

This intensive, project-based course dives deep into the advanced topics of Machine Learning and data analysis using the Python ecosystem (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch). We will move beyond basic predictive modeling to cover complex algorithms like Deep Learning, Natural Language Processing (NLP), and time-series analysis. Emphasis is placed on practical application, model optimization, deployment best practices, and ethical considerations in AI. The final project involves building, training, and deploying a scalable, production-ready machine learning model.

محتوى الدورة

Course syllabus includes deep dives into neural networks, NLP, time-series, and practical deployment strategies. Materials include downloadable code notebooks and weekly readings.

النتائج

Upon completion of this course, students will be able to: * Design and implement deep neural networks using modern frameworks (TensorFlow/PyTorch) for image recognition and sequence modeling. * Master data preprocessing, feature engineering, and model selection techniques to achieve state-of-the-art predictive accuracy. * Apply advanced NLP techniques, including Transformers and BERT models, to analyze and derive insights from text data. * Develop robust pipelines for model deployment, monitoring, and maintenance in a cloud environment (e.g., AWS/GCP). * Critically evaluate and address algorithmic bias and ethical implications in real-world ML systems.

معايير التسجيل

Successful registration requires meeting the following prerequisites: 1. Intermediate Python proficiency: Strong understanding of functions, classes, data structures (lists, dictionaries), and code organization. 2. Foundational Statistics/Math: Working knowledge of linear algebra, calculus, and basic probability and statistics (e.g., hypothesis testing, regression). 3. Prior ML Exposure (Recommended): Completion of an introductory machine learning course or 6+ months of practical experience working with Scikit-learn or similar libraries. 4. Hardware Requirements: Reliable internet connection and a personal computer capable of running virtual environments and local model training (8GB RAM minimum recommended).