목록Artificial Intelligence/Machine Learning (21)
Code&Data Insights
https://www.youtube.com/watch?v=sbbYntt5CJk&t=4680s https://www.youtube.com/watch?v=Gv9_4yMHFhI&list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF 말모... 이해안되는 개념들 진짜 쉽게 설명해주심 ㅠㅠ https://www.coursera.org/collections/machine-learning Andrew Ng’s Machine Learning Collection www.coursera.org + 한국 유튜버분들 (두분다 박사생이신듯.. 짱 멋짐 ㅠㅠ) https://www.youtube.com/watch?v=74NvFfKZm7A https://www.youtube.com/watch?v=Eyxyn..
[Machine Learning] Unsupervised Learning - Clustering | K-Means | Anomaly Detection
Unsupervised Learning - Clustering Unsupervised Learning - Unsupervised learning uses unlabeled data. The training examples do not have targets or labels "y". Recall the T-shirt example. The data was height and weight but no target size. Clustering : find the data points related or similar - mostly used in marketing | segmentation | tracking | abnormaly detection - different cluster must have di..
[Machine Learning] Supervised Learning - Regression (Linear | Multiple | Logistic )
[ Regression ] What is the Regression? Regression : predict a number from infinitely many possible outputs - a technique for investigating the relationship between independent variables or features and a dependent variable or outcome. It's used as a method for predictive modelling in machine learning. => the point of the predictive modelling in Regression is to find the best(optimal) regression ..
[Machine Learning] Cross Validation | Confusion Matrix | ROC - AUC curves
[ Cross Validation ] : Cross Validation allows us to compare different machine learning methods and get a sense of how well they will work in practice. - K-Fold ( K could be arbitrary! ) [ Confusion Matrix ] Confusion Matrix : To decide which method should be performed with the given data sets, we need to summurize how each method performed on the testing Data. => one way to do this is by creati..
[Machine Learning] What is Machine Learning ?
[ Terminology in Machine Learning ] - Training set : data used to train the model => input features + target variables ( x + y ) - x : input variable | (input) features - y : output variable | target variable - m : number of training examples - (x,y) : single training example -ŷ (y hat) : predicted output The data type in ML : Machine learning builds predictive models based on your data and lea..