Simplifying Machine Learning with PyCaret in Python

Description

In this course, you will learn :

• Understanding of PyCaret.
• Streamlit knowledge and app deployment on the Streamlit Cloud.
• Regression with PyCaret, as well as experience with exploratory data analysis, environment setup, and machine learning model development.
• PyCaret classification and machine learning model creation, tuning, plotting, and saving
• Using PyCaret to group tasks.
• PyCaret detects anomalies.

Syllabus :

1. Introduction to Machine Learning

• Machine Learning and its Types

 

2. Regression

• Regression with PyCaret
• Exploratory Data Analysis
• Getting Familiar with the PyCaret Environment
• Building the Model

 

3. Classification

• Classification with PyCaret
• Exploratory Data Analysis
• Getting Familiar with the PyCaret Environment
• Building the Model

 

4. Clustering

• Clustering with PyCaret
• Exploratory Data Analysis
• Getting Familiar with the PyCaret Environment
• Building the Model

 

5. Anomaly Detection

• Anomaly Detection with PyCaret
• Exploratory Data Analysis
• Getting Familiar with the PyCaret Environment
• Building the Model

 

6. Natural Language Processing

• Natural Language Processing with PyCaret
• Exploratory Data Analysis
• Initializing the NLP Environment
• Creating and Assigning the Topic Model
• Initializing the Classification Environment
• Creating the Classification Model

 

7. Deploying a Machine Learning Model

• Streamlit
• Insurance Charges Prediction Web App
• Iris Classification Web App
• Deploying an Application to Streamlit Cloud

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