Enrol to start learning
Reading is open to everyone. Enrolling is free, and it is what unlocks the audio lessons, practice tests and progress tracking.
Introduction to Machine Learning
Machine Learning focuses on creating algorithms that can learn from data and make predictions or decisions autonomously. It covers types of learning, including supervised and unsupervised, alongside the basic workflow for building models using tools like scikit-learn. The importance of splitting data for training and evaluation, as well as understanding key evaluation metrics, are also emphasized.
Sections
Machine Learning is a subset of AI that focuses on systems that learn from data to make decisions with minimal human intervention.
This section covers the three main types of machine learning: supervised, unsupervised, and reinforcement learning, providing definitions and examples for each.
The basic ML workflow outlines the key steps involved in building a machine learning model, from data importation to performance evaluation.
This section demonstrates how to build a predictive model using supervised learning techniques, focusing on the relationship between hours studied and student scores.
This section covers critical terminology related to Machine Learning that is essential for understanding its concepts.
Machine learning involves training algorithms to learn patterns from data.
Supervised learning uses labeled data; unsupervised learning does not.
scikit-learn simplifies model training, prediction, and evaluation.
Data should always be split into training and testing sets.
Evaluation metrics are essential for understanding model performance.
Features (X)
Input variables used in machine learning models (e.g., age, hours studied).
Target (y)
The output variable that the model is trying to predict (e.g., salary, exam score).
Overfitting
Occurs when a model performs well on training data but poorly on unseen data due to capturing noise instead of the underlying pattern.
Underfitting
Occurs when a model is too simple to capture the underlying trends in the data.
Train/Test Split
The process of dividing data into a training set to train the model and a test set to evaluate its performance.
Mean Squared Error
A metric used to measure the average squared difference between predictions and actual outcomes in regression tasks.
R² Score
A metric that indicates the proportion of variance in the dependent variable that can be explained by the independent variables.
Accuracy
The ratio of correctly predicted instances to the total instances in classification tasks.
Precision
The ratio of true positive predictions to all positive predictions made by the model.
Recall
The ratio of true positive predictions to the total actual positives.
F1 Score
A metric that combines precision and recall into a single score, useful for evaluating classification models.