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 Duration 21 hours

Course Outline

Introduction

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

Data Collection and Preprocessing

  • Acquiring relevant datasets
  • Cleaning and preparing data for analytical workflows
  • Recognizing various data types and their sources

Exploratory Data Analysis (EDA)

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

Statistical Modeling

  • Foundations of statistical inference
  • Conducting regression analysis
  • Implementing classification models

Machine Learning Algorithms for Prediction

  • Survey of supervised learning algorithms
  • Decision trees and random forest techniques
  • Basics of neural networks and deep learning

Model Evaluation and Selection

  • Interpreting model accuracy and performance indicators
  • Utilizing cross-validation methods
  • Addressing overfitting and optimizing model tuning

Practical Applications of Predictive AI

  • Reviewing industry-specific case studies
  • Considering ethical implications in predictive modeling
  • Identifying limitations and challenges associated with Predictive AI

Hands-On Project

  • Building a predictive model using a dataset
  • Deploying the model to generate predictions
  • Analyzing and interpreting the final results

Requirements

  • Foundation in basic statistics
  • Proficiency in any programming language
  • Experience with data management and spreadsheets
  • No prior background in AI or data science is necessary

Target Audience

  • IT professionals
  • Data analysts
  • Technical teams

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