Introduction to Predictive AI

• 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

Predictive AI is the art and science of forecasting future events using data.

This instructor-led, live training (online or onsite) is aimed at beginner-level IT professionals who wish to grasp the fundamentals of Predictive AI.

By the end of this training, participants will be able to:

• Understand the core concepts of Predictive AI and its applications.
• Collect, clean, and preprocess data for predictive analysis.
• Explore and visualize data to uncover insights.
• Build basic statistical models to make predictions.
• Evaluate the performance of predictive models.
• Apply Predictive AI concepts to real-world scenarios.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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