Introduction to Predictive AI Training Course

Introduction

• Defining Predictive AI
• Historical context and evolution of predictive analytics
• Basic principles of machine learning and data mining

Data Collection and Preprocessing

• Gathering relevant data
• Cleaning and preparing data for analysis
• Understanding data types and sources

Exploratory Data Analysis (EDA)

• Visualizing data for insights
• Descriptive statistics and data summarization
• Identifying patterns and relationships in data

Statistical Modeling

• Basics of statistical inference
• Regression analysis
• Classification models

Machine Learning Algorithms for Prediction

• Overview of supervised learning algorithms
• Decision trees and random forests
• Neural networks and deep learning basics

Model Evaluation and Selection

• Understanding model accuracy and performance metrics
• Cross-validation techniques
• Overfitting and model tuning

Practical Applications of Predictive AI

• Case studies across various industries
• Ethical considerations in predictive modeling
• Limitations and challenges of Predictive AI

Hands-On Project

• Working with a dataset to create a predictive model
• Applying the model to make predictions
• Evaluating and interpreting the results

Summary and Next Steps