Offered By: IBMSkillsNetwork
Machine Learning Fundamentals - Week 2
Develop strong skills in artificial intelligence by mastering fundamental techniques and concepts. Build upon theoretical machine learning through application on real world tasks.
Continue readingGuided Project
Artificial Intelligence
4.8
At a Glance
Develop strong skills in artificial intelligence by mastering fundamental techniques and concepts. Build upon theoretical machine learning through application on real world tasks.
Week 2 Content: Feature Mapping, Regularization, and Logistic Regression
This second workshop builds on the foundation from Week 1, moving from simple linear regression toward more advanced tools that let models handle non-linear patterns and make classification decisions.
Recap and Key Concepts
The session begins with a quick review of core ideas â features, targets, and how data is split into training, validation, and test sets. This reinforces how models learn patterns from data and why separating datasets helps prevent overfitting.
The session begins with a quick review of core ideas â features, targets, and how data is split into training, validation, and test sets. This reinforces how models learn patterns from data and why separating datasets helps prevent overfitting.
Feature Mapping
Feature Mapping introduces the idea of transforming simple inputs into new, more expressive features to capture non-linear relationships. For example, turning one variable into multiple (like (x, x^2, x^3)) helps a linear model fit curves rather than straight lines. Visual examples show how increasing complexity can lead to underfitting, good performance, or overfitting depending on the modelâs flexibility.
Feature Mapping introduces the idea of transforming simple inputs into new, more expressive features to capture non-linear relationships. For example, turning one variable into multiple (like (x, x^2, x^3)) helps a linear model fit curves rather than straight lines. Visual examples show how increasing complexity can lead to underfitting, good performance, or overfitting depending on the modelâs flexibility.
Regularization
To avoid overfitting, the concept of Regularization is introduced. Regularization helps keep models simpler by discouraging extreme weight values, balancing accuracy with generalization. It acts as a âcomplexity control,â ensuring the model doesnât just memorize training data.
To avoid overfitting, the concept of Regularization is introduced. Regularization helps keep models simpler by discouraging extreme weight values, balancing accuracy with generalization. It acts as a âcomplexity control,â ensuring the model doesnât just memorize training data.
Logistic Regression
Finally, the workshop transitions from regression to classificationâteaching models to distinguish between categories (like 0 and 1). Logistic Regression is introduced as a foundational method for binary classification. It outputs probabilities instead of continuous values, helping determine the likelihood that an input belongs to a certain class.
Finally, the workshop transitions from regression to classificationâteaching models to distinguish between categories (like 0 and 1). Logistic Regression is introduced as a foundational method for binary classification. It outputs probabilities instead of continuous values, helping determine the likelihood that an input belongs to a certain class.
Overall, this week connects regression to classification and introduces essential toolsâfeature mapping and regularizationâthat make models both powerful and reliable.
Estimated Effort
1 Hour
Level
Beginner
Skills You Will Learn
Machine Learning
Language
English
Course Code
GPXX0S25EN