Skip to main content
grasp.study
About
Explore
Pricing
Log in
Try it for free
Back to course
👋
Welcome to a Grasp lesson
Create a free account
to keep studying and create your own study path.
Feuille de route complète LM
·
Module 7
Foundations of Classical Machine Learning
1
Introduction to Machine Learning Paradigms
Define machine learning and distinguish between supervised, unsupervised, and reinforcement learning.
2
Regression vs. Classification
Explain the difference between regression and classification tasks.
3
Train-Test Split: Why and How
Split a dataset into training and testing sets and explain the rationale.
4
Overfitting, Underfitting, and the Bias-Variance Tradeoff
Define overfitting and underfitting and explain the bias-variance tradeoff.
Understanding Cost Functions in Machine Learning
Explain the role of a cost/loss function in machine learning.
6
Gradient Descent: Minimizing Loss Functions
Describe the gradient descent algorithm and its role in minimizing a loss function.
Building a Linear Regression Model with Scikit-learn
Implement and evaluate a simple linear regression model using Scikit-learn.
Feature Scaling: Standardization
Apply feature scaling (Standardization) and explain its importance.
Previous module
Data Analysis with Pandas and Visualization
Next module
Advanced Supervised Learning Models