Digital Innovation Partner

ML for Life Science


Help professionals and researchers in life Science business (health, pharma, agribusiness) to:

Benefits:  Develop and improve the skills to build and test end-to-end Machine learning models on life science data. 
Target Audience: Clinical Researchers, Clinicians, Medical Doctors, Researchers, Pharmaceutical scientists, Biologists,
Pre-requisites: Basic knowledge in biology and physiology, clinical analysis, medical research

The Program

Module 1: Data Science Fundamentals

  • Data Science Overview
  • Skills needed to be a successful data
  • How to use and share Jupyter
    Notebooks including its features
  • Introduction to Python programming
  • Use case: Diabetics Data Analysis
  • How to identify a problem, collect and
    analyze data, build a model, and understand the feedback

Key Learnings

  • Introduction to data sciences
  • Methodology to handle data science questions

Module 2: Data science workflow

  • Methods, models and practices
  • Data Strategy
  • Data visualization and Cleaning tools
  • Operation on Data
    Models with Machine

Key Learnings

  • Specify meaningful pipeline
  • Get hands-on experience in DS

Module 3: Building Valuable ML models in Life Sciences

  • Familiarize with Scikit-learn
  • Introduction to Neural Network
  • Build & Train Machine Learning Models
    Hyperparameters optimization
  • Case studies for prediction models using hyperparameters, NN , and  image classification 

Key Learnings

  • Build and test an end-to-end ML model

Effective SRE

Effective MLOps

ML for Life Sciences

ML4 DevOps

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