𝗧𝗼𝗽 8 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 𝗶𝗻 𝗹𝗲𝘀𝘀 𝘁𝗵𝗮𝗻 1 𝗺𝗶𝗻𝘂𝘁𝗲 𝗲𝗮𝗰𝗵
by @DataScienceDojo 🤓🙌
📌datasciencedojo.com
#AI #algorithms #DataScience #MachineLearning
by @DataScienceDojo 🤓🙌
📌datasciencedojo.com
#AI #algorithms #DataScience #MachineLearning
1. 𝗟𝗶𝗻𝗲𝗮𝗿 𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 is a simple AI model! It's like plotting 𝘺=𝘮𝘹+𝘤 in algebra: estimating y for any x. Likewise, linear regression estimates the relationship between independent (x) and dependent (y) variables. #MachineLearning #AI
2. 𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻: Similar to linear regression. Maps probabilities of outcomes between 0-1, predicting a binary outcome based on relationships between variables. Great for classification! #AI #MachineLearning
3. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲𝘀: Supervised ML model making decisions via a flowchart-like structure. Asks yes/no questions until a prediction is made. Clear & interpretable! #MachineLearning #AI
4. 𝗥𝗮𝗻𝗱𝗼𝗺 𝗙𝗼𝗿𝗲𝘀𝘁: Builds on decision trees to reduce overfitting. Aggregates results of multiple trees; majority vote is final prediction. #AI #MachineLearning
5. 𝗞-𝗡𝗲𝗮𝗿𝗲𝘀𝘁 𝗡𝗲𝗶𝗴𝗵𝗯𝗼𝗿: Simple but powerful, KNN predicts values of new datapoints based on their resemblance to points in the training set. #MachineLearning #AI
6. 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗩𝗲𝗰𝘁𝗼𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲: Versatile ML model used for classification & regression. Classifies data by finding the optimal separating hyperplane. #AI #MachineLearning
7. 𝗞-𝗺𝗲𝗮𝗻𝘀 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝗶𝗻𝗴: Unsupervised learning algorithm dividing a dataset into 'k' distinct clusters. Widely used in market segmentation, document clustering, etc. #MachineLearning #AI
8. 𝗡𝗮𝗶𝘃𝗲 𝗕𝗮𝘆𝗲𝘀: Applies Bayes theorem to predict likelihood of an item belonging to a class. Assumes feature independence. Great for large datasets! #AI #MachineLearning
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