What are the basic points of Machine Learning?

1. • • Supervised Learning: The algorithm is trained on a labeled dataset, meaning that each training example is paired with an output label. Common tasks include classification and regression.
       • Example: Predicting house prices based on features like size, location, and number of bedrooms.
     • Unsupervised Learning: The algorithm works on unlabeled data and tries to find hidden patterns or intrinsic structures in the input data. Common tasks include clustering and association.
       • Example: Grouping customers into different segments based on purchasing behavior.
     • Semi-supervised Learning: Combines a small amount of labeled data with many unlabeled data during training. It falls between supervised and unsupervised learning.
     • Reinforcement Learning: The algorithm learns by interacting with an environment, receiving rewards or penalties for actions, and aims to maximize cumulative rewards.
       • Example: Training a robot to navigate a maze.

2. Common Algorithms:

    

   • • Linear Regression: Used for regression tasks; models the relationship between a dependent variable and one or more independent variables.
     • Logistic Regression: Used for binary classification problems.
     • Decision Trees: Non-linear models that split data into branches to make predictions.
     • Support Vector Machines (SVM): Used for classification and regression tasks by finding the hyperplane that best divides a dataset into classes.
     • K-Nearest Neighbors (KNN): A simple, instance-based learning algorithm for classification and regression.
     • Neural Networks: A series of algorithms that attempt to recognize underlying relationships in a data set through a process miming how the human brain operates.
     • K-Means Clustering: An unsupervised learning algorithm that partitions data into K distinct clusters based on distance.

3. Model Evaluation:

    

   • • Accuracy: The ratio of correctly predicted observations to the total observations.
     • Precision and Recall: Precision is the ratio of correctly predicted positive observations to the total predicted positives, while recall is the ratio of correctly predicted positive observations to all actual positives.
     • F1 Score: The harmonic mean of precision and recall.
     • Confusion Matrix: A table used to describe the performance of a classification algorithm.
     • ROC-AUC: The area under the receiver operating characteristic curve plots the true positive rate against the false positive rate.

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