Mode - Pre-Recorded videos +  7 Live Zoom Classes (2-3/week) + 1 Bonus Session for 1:1 project discussion

Live classes will start from Tuesday, 24-10-2023, 7:15pm - 8pm IST

Note - These are live classes on Zoom. Exact schedule will be shared in respective Whatsapp Group.

Description:

This course is designed to introduce doctors to the concept of machine learning using the Python programming language. Machine learning has the potential to revolutionize the medical field by helping doctors make accurate diagnoses, predict patient outcomes, and improve treatment plans. With hands-on examples and real-world case studies, doctors will learn how to leverage Python libraries such as scikit-learn and TensorFlow to build and deploy machine learning models in a healthcare setting.

Prerequisite - Basic Python knowledge is must

Key Highlights:

• Understand the fundamentals of machine learning
• Explore various machine learning algorithms and their applications in healthcare
• Learn how to preprocess medical data for machine learning
• Build predictive models to aid in medical diagnosis and treatment
• Discover techniques for interpreting and evaluating machine learning models in a clinical setting

What you will learn:

• Gain a Solid Foundation in Machine Learning
  Develop a strong understanding of the principles and concepts behind machine learning, including supervised and unsupervised learning, regression, classification, and clustering.
• Apply Machine Learning in Healthcare
  Discover how machine learning can be integrated into various aspects of healthcare, such as disease prediction, image analysis, patient risk stratification, and personalized treatment recommendation.
• Preprocess and Analyze Medical Data
  Learn how to preprocess and clean medical data, handle missing values, deal with imbalanced datasets, and perform feature selection and extraction to optimize machine learning models.
• Build and Deploy Machine Learning Models
  Gain practical experience in building machine learning models using Python libraries like scikit-learn and TensorFlow. Understand the best practices for model training, validation, and deployment in a healthcare environment.
• Evaluate and Interpret Machine Learning Models
  Explore techniques for evaluating the performance of machine learning models in a clinical setting, interpret the results, and understand the role of explainable AI in healthcare.