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  • Class and Course

    Applied Data Preparation and Visualization

    • Gain an updated understanding of AI trends and refresh your Python skills, enabling you to utilize key libraries for AI development.  

    • Acquire expertise in data handling with Pandas and Numpy, and apply machine learning techniques for data preparation. 

    • Develop the ability to clean data, engineer features, and construct machine learning models using sklearn effectively. 

    • Learn comprehensive data annotation tools and methodologies, ensuring high-quality data for AI models. 

    • Design engaging visuals with Plotly and develop interactive web applications using Streamlit. 

    • Understand how to initiate and manage AI projects, orchestrate data, and deploy machine learning models using Dataiku’s visual interface and MLOps practices.  


    Module1: Understanding Your Data ​

    Machine Learning Refresher​

    Why Data is Important ​

    Data Types ​

    Build the Dataset ​

    Feature Engineering


    Module 2: Data Preparation and Management​

    Systematic approach to data preparation​

    Cleaning and Organizing Data​

    Methods for consistent machine learning inputs​

    Advancing skills in data augmentation​

    Integrating diverse data sources


    Module3: Data Annotation​

    Fundamentals of Data Annotation​

    Annotation Tools Exploration​

    Best Practices in Data Annotation​

    Quality Assurance in Annotation


    Module 4: Introduction to Interactive Visualization​

    Introduction to Webapps​

    Engaging Visuals with Plotly​

    Dash Framework Overview​

    Component Deep Dive​

    Elevating Your Dashboards


    Module 5: Build End-to-End Workflow​

    Hands-on Lab with No Code tools​

    Connect Data​

    Enrich Data​

    Transform Data​

    Visualize Data


    Upcoming Classes

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