Objective:
The objective of this lesson is to empower learners to convert their theoretical understanding of Artificial Intelligence (AI) into practical, actionable skills that can be applied within various professional and personal growth arenas. By the end of this lesson, the learner should be capable of executing AI-related tasks, developing AI strategies, and solving real-world problems using AI technologies.
Comprehensive Content Overview:
AI skills encompass a variety of competencies including machine learning, natural language processing, robotics, and cognitive computing. To translate these theoretical concepts into practice, one must understand the tools and methods that enable the creation, implementation, and improvement of AI systems.
In-depth Explanations with Actionable Insights:
Data Preprocessing for Machine Learning Before building a machine learning model, data must be cleaned and prepared. This includes handling missing values, encoding categorical data, feature scaling, and splitting the dataset into training and testing sets.
Example: Consider a dataset containing information on housing prices. To prepare this data for a linear regression model, you would:
– Replace or remove records with missing values. – Convert categorical variables like “Neighborhood” into numeric codes using one-hot encoding. – Scale features like “Area” and “Number of Rooms” to normalize their ranges. – Split the dataset into 80% training data and 20% testing data.
Interactive Elements and Applied Learning:
Building a Simple Linear ...
