Machine and Deep learning (ML/DL) predictive Modeling: Theory and Practice

This course is designed for individuals with a solid understanding of programming and statistics who want to delve into the world of machine learning and deep learning. Participants will learn the theoretical foundations and practical applications of ML/DL algorithms for predictive modeling. 

The course will cover essential skills for building, evaluating, and deploying machine learning models using popular frameworks.Course Topics:

• Introduction to machine learning and deep learning
  
  
• Supervised and unsupervised learning algorithms
  
  
• Data preprocessing and feature engineering
  
  
• Model evaluation and selection techniques
  
  
• Neural networks and deep learning architectures
  
  
• Training deep learning models with TensorFlow and Keras
  
  
• Hyperparameter tuning and optimization
  
  
• Model deployment and serving
  
  
• Case studies and real-world projects
  
  
• Ethical considerations and best practices in ML/DL