Get in Touch
 Duration 21 hours

Course Outline

Introduction

  • Defining the scope of Predictive AI
  • The evolution of predictive analytics
  • Foundations of machine learning and data mining

Data Acquisition and Preparation

  • Sourcing relevant data
  • Refining and formatting data for analysis
  • Recognizing data types and origins

Exploratory Data Analysis (EDA)

  • Using visualization to gain insights
  • Applying descriptive statistics and summarization
  • Detecting patterns and correlations within data

Statistical Modeling

  • Foundations of statistical inference
  • Regression analysis techniques
  • Building classification models

Machine Learning Algorithms for Forecasting

  • Introduction to supervised learning algorithms
  • Decision trees and random forests
  • Essentials of neural networks and deep learning

Model Assessment and Selection

  • Interpreting accuracy and performance metrics
  • Implementing cross-validation methods
  • Managing overfitting and optimizing models

Real-World Applications of Predictive AI

  • Industry-specific case studies
  • Ethical aspects of predictive modeling
  • Limitations and challenges in Predictive AI

Practical Project

  • Creating a predictive model using a dataset
  • Deploying the model for predictions
  • Reviewing and interpreting outcomes

Requirements

  • A foundational understanding of basic statistics
  • Proficiency in at least one programming language
  • Comfort with data management and spreadsheet tools
  • Previous experience in AI or data science is not necessary

Target Audience

  • IT professionals
  • Data analysts
  • Technical staff

Number of participants


Price per participant

Testimonials (3)

Upcoming Courses

Related Categories