Chapter 11. A measure of how close points are Similarity
Course Content
0 / 95 completedChapter 1. Some examples of models that humans use
Chapter 1. What is machine learning
Chapter 1. What is machine learning It is common sense, except done by a computer
Chapter 1. What is machine learning It is common sense, except done by a computer
Chapter 1. Example 4 More
Chapter 2. Types of machine learning
Chapter 2. Supervised learning The branch of machine learning that works with labeled data
Chapter 2. Unsupervised learning The branch of machine learning that works with unlabeled data
Chapter 2. Dimensionality reduction simplifies data without losing too much information
Chapter 3. Drawing a line close to our points Linear regression
Chapter 3. The remember step Looking at the prices of existing houses
Chapter 3. The linear regression algorithm Repeating the absolute or square trick many times to move the line closer to the points
Chapter 3. Some questions that arise and some quick answers
Chapter 3. Crash course on slope and y-intercept
Chapter 2. What is reinforcement learning
Chapter 3. How do we measure our results The error function
Chapter 3. Simple trick
Chapter 3. Gradient descent How to decrease an error function by slowly descending from a mountain
Chapter 3. Parameters and hyperparameters
Chapter 4. Another example of overfitting Movie recommendations
Chapter 4. How do we get the computer to pick the right model By testing
Chapter 3. Real-life application Using Turi Create to predict housing prices in India
Chapter 4. Optimizing the training process Underfitting, overfitting, testing, and regularization
Chapter 4. An intuitive way to see regularization
Chapter 4. A numerical way to decide how complex our model should be The model complexity graph
Chapter 4. Modifying the error function to solve our problem Lasso regression and ridge regression
Chapter 5. Sentiment analysis classifier
Chapter 5. Using lines to split our points The perceptron algorithm
Chapter 4. Polynomial regression, testing, and regularization with Turi Create The testing RMSE for the models follow
Chapter 4. Polynomial regression, testing, and regularization with Turi Create
Chapter 5. The problem We are on an alien planet, and we don t know their language!
Chapter 5. The step function and activation functions A condensed way to get predictions
Chapter 5. Error function 3 Score
Chapter 5. Pseudocode for the perceptron trick (geometric)
Chapter 5. The bias, the y-intercept, and the inherent mood of a quiet alien
Chapter 5. Pseudocode for the perceptron algorithm
Chapter 6. A continuous approach to splitting points Logistic classifiers
Chapter 5. Bad classifier
Chapter 5. Coding the perceptron algorithm using Turi Create
Chapter 6. Error function 3 log loss
Chapter 6. Formula for the log loss
Chapter 6. Pseudocode for the logistic trick
Chapter 6. The dataset and the predictions
Chapter 7. How do you measure classification models Accuracy and its friends
Chapter 6. Classifying into multiple classes The softmax function
Chapter 6. Coding the logistic regression algorithm
Chapter 7. False positives and false negatives Which one is worse
Chapter 7. A useful tool to evaluate our model The receiver operating characteristic (ROC) curve
Chapter 7. Recall Among the positive examples, how many did we correctly classify
Chapter 7. Recall is sensitivity, but precision and specificity are different
Chapter 7. The receiver operating characteristic (ROC) curve A way to optimize sensitivity and specificity in a model
Chapter 7. Summary
Chapter 8. Using probability to its maximum The naive Bayes model
Chapter 7. Combining recall and precision as a way to optimize both The F-score
Chapter 7. A metric that tells us how good our model is The AUC (area under the curve)
Chapter 8. Sick or healthy A story with Bayes theorem as the hero Let s calculate this probability
Chapter 8. Prelude to Bayes theorem The prior, the event, and the posterior
Chapter 8. What the math just happened Turning ratios into probabilities
Chapter 8. What the math just happened Turning ratios into probabilitiesProduct rule of probabilities
Chapter 8. What about more than two words
Chapter 8. Implementing the naive Bayes algorithm
Chapter 8. What about two words The naive Bayes algorithm
Chapter 9. Splitting data by asking questions Decision trees
Chapter 9. Gini impurity index How diverse is my dataset
Chapter 9. The graphical boundary of decision trees
Chapter 10. Combining building blocks to gain more power Neural networks
Chapter 10. Potential problems From overfitting to vanishing gradients
Chapter 10. Training the model
Chapter 9. Classes of different sizes No problem We can take weighted averages
Chapter 9. Applications
Chapter 10. Neural networks with more than one output The softmax function
Chapter 10. The boundary of a neural network
Chapter 9. Beyond questions like yesno
Chapter 10. Other architectures for more complex datasets
Chapter 10. How neural networks paint paintings Generative adversarial networks (GAN)
Chapter 11. Distance error function Trying to separate our two lines as far apart as possible
Chapter 10. Why two lines Is happiness not linear
Chapter 11. Finding boundaries with style Support vector machines and the kernel method
Chapter 11. Training SVMs with nonlinear boundaries The kernel method
Chapter 11. Overfitting and underfitting with the RBF kernel The gamma parameter
Chapter 11. Going beyond quadratic equations The polynomial kernel
Chapter 11. A measure of how close points are Similarity
Chapter 12. Combining the weak learners into a strong learner
Chapter 12. Gradient boosting Using decision trees to build strong learners
Chapter 12. Fitting a random forest manually
Chapter 12. XGBoost similarity score A new and effective way to measure similarity in a set
Chapter 12. Combining models to maximize results Ensemble learning
Chapter 12. Building the weak learners Split at 25
Chapter 13. Putting it all in practice A real-life example of data engineering and machine learning
Chapter 12. Tree pruning A way to reduce overfitting by simplifying the weak learners
Chapter 13. Turning categorical data into numerical data One-hot encoding
Chapter 13. Using Pandas to study our dataset
Chapter 13. Testing each model s accuracy
Chapter 13. Feature selection Getting rid of unnecessary features
Chapter 13. Tuning the hyperparameters to find the best model Grid search