Introduction to Predictive AI Training Course

Introduction

• Defining the scope and meaning of Predictive AI.
• Reviewing the historical context and evolution of predictive analytics.
• Exploring the basic principles of machine learning and data mining.

Data Collection and Preprocessing

• Methods for gathering relevant data.
• Cleaning and preparing data for analytical processes.
• Understanding different data types and their sources.

Exploratory Data Analysis (EDA)

• Using visualization techniques to gain insights.
• Applying descriptive statistics and data summarization.
• Identifying patterns and correlations within the data.

Statistical Modeling

• Fundamentals of statistical inference.
• Techniques in regression analysis.
• Developing classification models.

Machine Learning Algorithms for Prediction

• An overview of supervised learning algorithms.
• Utilizing decision trees and random forests.
• Understanding the basics of neural networks and deep learning.

Model Evaluation and Selection

• Interpreting model accuracy and performance metrics.
• Applying cross-validation techniques.
• Addressing overfitting and performing model tuning.

Practical Applications of Predictive AI

• Examining case studies from various industries.
• Considering ethical implications in predictive modeling.
• Discussing the limitations and challenges associated with Predictive AI.

Hands-On Project

• Creating a predictive model using a real dataset.
• Applying the developed model to make predictions.
• Evaluating and interpreting the final results.