Predictive AI for Beginners
Learn Predictive AI – Future-Ready Skills for Everyone
No
Coding or AI Experience Required
4
Hours
Self Paced Course
1
Hands-On Practice
Certificate
of Completion
Course
Overview
This course is designed to give beginners a foundational understanding of predictive AI, focusing on regression modeling to forecast outcomes and drive data-informed decisions. With a no-code approach, this course enables learners from non-technical backgrounds to harness predictive AI in solving practical business problems, including understanding data trends and forecasting prices. By the end of this course, learners will be able to explore datasets, build simple regression models, evaluate model performance, and refine models based on test results.
Who Should
Attend
- Working Professionals aiming to improve decision-making with AI.
- Business Leaders aspiring to lead AI initiatives and build the organization’s AI capabilities
- Job Seekers looking to gain valuable AI skills to enhance their job prospects.
- Students preparing for the future with early knowledge of AI
Learning
Outcomes
- Understand the basic concepts and applications of Generative AI.
- Gain practical experience in Prompt Engineering
- Explore real-world applications of Generative AI in various industries such as marketing, content creation, and customer service.
Course
Curriculum
Duration: 4 hours
Self-Paced
A Certificate of Completion will be awarded upon successfully completing the course.
-
-
Core Concepts of Predictive AI
- What is AI?: Broad introduction to AI concepts and its different types.
- Understanding Regression: Explanation of regression, its role in making predictions, and why it’s essential in business.
-
Understanding a Real Predictive Problem
-
- Use Case Introduction: Real-world predictive problem, such as predicting car prices or housing prices.
- Defining Success Criteria: Goals of the prediction and evaluating the model’s impact.
-
- Data Acquisition and Exploration
-
- Dataset Selection: Steps to select, download, and load relevant datasets.
- Data Characteristics:
- Summary Statistics: Mean, median, mode, and distribution.
- Data Correlations: Identifying relationships between variables and how they impact the target variable.
-
-
Building a Predictive Model
- Choosing the Target Variable
- Building a Regression Model with AutoML
-
Evaluating Model Performance
- Model Evaluation Metrics:
- Mean Absolute Error (MAE): Measures the average absolute error between predicted and actual values.
- Mean Squared Error (MSE): Squares error values to emphasize larger errors.
- Root Mean Squared Error (RMSE), MAPE, WAPE, and R²: Definitions and relevance
- Model Evaluation Metrics:
-
Testing the Model
- Manual Testing Process:
- Selecting real-world scenarios to test the model.
- Manually inputting data to observe model outputs and compare with expected outcomes.
- Manual Testing Process:
-
Explore More Emly Lab
Courses
Modern Business Intelligence
Business intelligence in age of AI for Business Analysts, Data Analyst roles.
AI Primer For Starters
100% hands-on practices where you’ll apply AI in real-world problems.
Free Course Generative AI
Learn foundational concepts and applications of Generative AI. Through hands-on practices
Data Science with Python
Comprehensive Data Science Course for Data Science, AI Engineer or Data Engineer roles
Start Your AI Learning Journey With Free Learner Account!
