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Course Outline
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
Requirements
- 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
21 Hours
Testimonials (2)
examples and exercises
Kamil
Course - Introduction to Data Science and AI using Python
Machine Translated
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