AI Byte-Sized Series: Machine Learning Model Deployment
- Created By raju2006
- Last Updated December 27th, 2023
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Session Description:
A short session indented to get started with Classification Algorithm to classify structured data and make predictions.Get Hands-on experience and also work on Real-Time Applications.
Setup:
Because this is an abbreviated session, attendees MUST install Anaconda software https://www.anaconda.com/ and have a basic understanding of using Jupyter Notebook."
Course Code/Duration:
BDT76 / 90 Minutes
Learning Objectives:
Learn about different libraries involved in saving and using a machine learning model. In the hands-on session we will look at the following:
- Understand the concept of classification in machine learning and its real-world applications.
- Identify and differentiate between key classification algorithms: Logistic Regression, Decision Trees, SVM, k-NN.
- Build a hands-on classification model using one of the algorithms.
- Evaluate model performance using essential classification metrics like accuracy, precision, and recall.
- Acquire practical skills to confidently apply classification techniques to real datasets.
- Learn basic understanding of python language, pandas library and understanding of how to use Juypter Notebook.
- This session is designed for anyone who is familiar with machine learning terminology and understands how to build a machine learning model.
- This session is designed for anyone who is familiar with machine learning terminology and understands how to build a machine learning model.
Course Outline:
- Introduction to Machine Learning Model Deployment
- Introduction to different libraries involved in saving and loading machine learning models
- Saving a machine learning model
- Loading a machine learning model
- Techniques for making predictions on new data using a loaded model.
The curriculum is empty
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