Course Outline

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

Requirements

  • An understanding of basic statistics
  • Experience with any programming language
  • Familiarity with data handling and spreadsheets
  • No prior experience in AI or data science required

Audience

  • IT professionals
  • Data analysts
  • Technical staff
 21 Hours

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