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Chapter 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