Introduction to Predictive AI Training Course

Introduction

• Defining Predictive AI.
• The historical context and evolution of predictive analytics.
• Fundamental 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 to gain 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 fundamentals.

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