Get in Touch
 Duration 21 hours

Course Outline

Introduction

  • Definition and scope of Predictive AI
  • Historical background and the development of predictive analytics
  • Fundamentals of machine learning and data mining

Data Acquisition and Preparation

  • Sourcing appropriate data
  • Refining and formatting data for analytical purposes
  • Recognizing various data types and origins

Exploratory Data Analysis (EDA)

  • Using visualization to extract insights
  • Applying descriptive statistics and summarizing data
  • Detecting trends and correlations within datasets

Statistical Modeling

  • Core concepts of statistical inference
  • Techniques in regression analysis
  • Implementation of classification models

Predictive Machine Learning Algorithms

  • Introduction to supervised learning methods
  • Utilizing decision trees and random forests
  • Basics of neural networks and deep learning

Model Assessment and Selection

  • Interpreting accuracy and performance indicators
  • Applying cross-validation methods
  • Managing overfitting and optimizing model parameters

Real-World Predictive AI Use Cases

  • Analyzing case studies from diverse sectors
  • Navigating ethical implications in predictive modeling
  • Addressing constraints and obstacles in Predictive AI

Practical Project

  • Developing a predictive model using a provided dataset
  • Deploying the model to generate forecasts
  • Analyzing and interpreting the outcomes

Requirements

  • Foundational knowledge of statistics
  • Proficiency in any programming language
  • Experience with data management and spreadsheet tools
  • No previous background in AI or data science is necessary

Target Audience

  • IT professionals
  • Data analysts
  • Technical personnel

Number of participants


Price per participant (excl. VAT)

Testimonials (3)

Upcoming Courses

Related Categories