Course Outline
Introduction
- Definition of Predictive Artificial Intelligence
- Historical development and evolution of predictive analytics
- Foundational principles of machine learning and data mining
Data Acquisition and Preparation
- Systematic collection of relevant data assets
- Data cleansing and preprocessing for analytical rigor
- Classification of data types and identification of sources
Exploratory Data Analysis (EDA)
- Data visualization strategies for deriving insights
- Application of descriptive statistics and data summarization techniques
- Detection of patterns and correlations within datasets
Statistical Modeling
- Fundamental concepts of statistical inference
- Methodologies for regression analysis
- Development of classification models
Machine Learning Algorithms for Prediction
- Survey of supervised learning algorithms
- Implementation of decision trees and random forests
- Foundational understanding of neural networks and deep learning
Model Evaluation and Selection
- Assessment of model accuracy and relevant performance metrics
- Utilization of cross-validation techniques
- Management of overfitting and model parameter tuning
Practical Applications of Predictive AI
- Industry-specific case studies demonstrating predictive capabilities
- Ethical frameworks and considerations in predictive modeling
- Analysis of limitations and operational challenges associated with Predictive AI
Applied Practical Project
- Construction of a predictive model using a designated dataset
- Deployment of the model to generate predictive outputs
- Comprehensive evaluation and interpretation of resulting data
Conclusion and Recommended Pathways
Requirements
- Proficiency in fundamental statistical concepts
- Practical experience with any standard programming language
- Familiarity with data management protocols and spreadsheet applications
- No prior specialized experience in AI or data science is required
Target Audience
- Information Technology Professionals
- Data Analysts
- Technical Staff Members
Testimonials (3)
basics and loved the prepared documents and exercises
Rekha Nallam - GE Medical Systems Polska Sp. z o.o.
Course - Introduction to Predictive AI
Opportunity to use a pre-created models, understand how do they work and tweak them live and see the results. Choice ov VSCode with Jupyter was a perfect option for such way of leading the training.
Krzysztof - GE Medical Systems Polska Sp. z o.o.
Course - Introduction to Predictive AI
Difficult topics presented in simple, user-friendly way
