ML Hands On

 

Supervised Learning

Supervised learning involves training a model on a dataset that is already labeled, where the goal is to predict the label for new, unseen data.

Classification

Classification is a type of supervised learning where the goal is to predict a discrete label or class.

  • penguins_rf: This experiment involves a classification task using the penguins dataset and a Random Forest Classifier.

  • breast_cancer_svm: A classification experiment on the breast cancer dataset. It uses a Logistic Regression model, which is used here as a stand-in for a simple classifier.

  • wine_knn: This is another classification experiment, this time with the wine dataset, and it also uses a Random Forest Classifier.

  • sms_spam_nb: This is a text classification task to detect SMS spam using a Multinomial Naive Bayes model.

  • lang_detect_nb: A text classification experiment for language detection using a small synthetic dataset and a Multinomial Naive Bayes model.

Regression

Regression is a type of supervised learning where the goal is to predict a continuous value.

  • diamonds_reg: A regression experiment that predicts diamond prices using the diamonds dataset and a Random Forest Regressor.

Unsupervised Learning

Unsupervised learning involves training a model on a dataset with no labels. The goal is to discover hidden patterns or intrinsic structures in the data.

Clustering

Clustering is a type of unsupervised learning where the goal is to group similar data points together.

  • iris_kmeans: This experiment performs clustering on the Iris dataset using the K-Means algorithm with clusters. The performance is evaluated using the silhouette score.

Reinforcement Learning (RL)

Reinforcement learning is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward.

  • bandits_pricing: This experiment uses a simulated environment to test different multi-armed bandit algorithms, including Epsilon-Greedy, UCB1, and Thompson Sampling. The goal is to maximize cumulative rewards in a surge pricing scenario.

  • elevator_qlearning: A Q-learning agent is trained in a simple elevator environment to minimize the total waiting time for passengers.

  • mountaincar_qlearning: This experiment uses a Q-learning agent to solve the classic Mountain Car problem, where the agent learns to drive an underpowered car up a hill.

Comments

Post a Comment

Popular posts from this blog

Elevator Q-Learning Pipeline

Elevator Q-Learning Pipeline Elevator Q-Learning Pipeline A step-by-step guide to applying Q-learning to control a simple multi-floor elevator to minimize wait time. Libraries Used This project uses a few key Python libraries. Here is a quick overview of their roles: numpy: Used for numerical operations and managing arrays. random: Used for generating random numbers for the simulation. collections: Used for the `defaultdict` to store the Q-table. 1. Define the Environment What the code does: It defines an `ElevatorEnv` class that simulates an el...
Read more

MountainCar Q-Learning Pipeline

MountainCar Q-Learning Pipeline MountainCar Q-Learning Pipeline A step-by-step guide to applying Q-learning to the classic MountainCar environment from Gymnasium. Libraries Used This project uses a few key Python libraries. Here is a quick overview of their roles: numpy: Used for numerical operations and managing arrays. gymnasium: The library that provides the classic MountainCar-v0 environment. collections: Used for the `defaultdict` to store the Q-table. 1. Define the Discretization and Q-Learning Agent What the code does: It defines a `Discr...
Read more

SMS Spam Detection Pipeline

SMS Spam Detection Pipeline SMS Spam Detection Pipeline A step-by-step guide to building a machine learning model to classify SMS messages as spam or not-spam using a Naive Bayes classifier. Libraries Used This pipeline uses a few key Python libraries. Here is a quick overview of their roles: pandas: Used for data manipulation and analysis. scikit-learn (sklearn): The core machine learning library. It provides all the necessary tools for loading data, splitting, preprocessing text, building the model, and evaluating performance. 1. Load the Dataset What the code does: It uses pandas to read a CSV f...
Read more