Artificial Intelligence (Machine_learning)

  1. Measuring model performance Now we can make predictions using a classifier, but how do we know if the model is making correct predictions? We can evaluate its performance! 2. Measuring model performance In classification, accuracy is a commonly-used metric. Accuracy is the number of correct predictions divided by the total number of observations. 3. Measuring model performance How do we measure accuracy? We could compute accuracy on the data used to fit the classifier. However, as this data was used to train the model, performance will not be indicative of how well it can generalize to unseen data, which is what we are interested in! 4. Computing accuracy It is common to split data into a training set and a test set. 5. Computing accuracy We fit the classifier using the training set, 6. Computing accuracy then we calculate the model's accuracy against the test set's labels. 7. Train/test split To do this, we import train_test_split from sklearn-dot-model_selection. We call...

  . The classification challenge Previously, we learned that supervised learning uses labels. Let's discuss how we can build a classification model, or classifier, to predict the labels of unseen data. 2. Classifying labels of unseen data There are four steps. First, we build a classifier, which learns from the labeled data we pass to it. We then pass it unlabeled data as input, and have it predict labels for this unseen data. As the classifier learns from the labeled data, we call this the training data. 3. k-Nearest Neighbors Let's build our first model! We'll use an algorithm called k-Nearest Neighbors, which is popular for classification problems. The idea of k-Nearest Neighbors, or KNN, is to predict the label of any data point by looking at the k, for example, three, closest labeled data points and getting them to vote on what label the unlabeled observation should have. KNN uses majority voting, which makes predictions based on what label the majority of nearest neig...

  1. Machine learning with scikit-learn Hi, and welcome! My name is George Boorman, and I'll be your instructor for this course on supervised learning with scikit-learn. 2. What is machine learning? Machine learning is the process whereby computers learn to make decisions from data without being explicitly programmed. 3. Examples of machine learning For example, learning to predict whether an email is spam or not spam given its content and sender. Or learning to cluster books into different categories based on the words they contain, then assigning any new book to one of the existing clusters. 4. Unsupervised learning Unsupervised learning is the process of uncovering hidden patterns and structures from unlabeled data. For example, a business may wish to group its customers into distinct categories based on their purchasing behavior without knowing in advance what these categories are. This is known as clustering, one branch of unsupervised learning. 5. Supervised learning Supervis...