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Intermediate Python, Statistics, Calculus, Linear Algebra.
Learn the difference between Regression and Classification, train a Linear Regression model to predict values, and learn to predict states using Logistic Regression.
Learn the definition of a perceptron as a building block for neural networks and the perceptron algorithm for classification.
Train Decision Trees to predict states and use Entropy to build decision trees, recursively.
Learn Bayes’ rule, and apply it to predict cases of spam messages using the Naive Bayes algorithm. Train models using Bayesian Learning and complete an exercise that uses Bayesian Learning for natural language processing.
Learn to train a Support Vector Machines to separate data, linearly. Use Kernel Methods in order to train SVMs on data that is not linearly separable.
Build professional presentations and data visualizations for quantitative and categorical data. Create pie, bar, line, scatter, histogram, and boxplot charts.
Calculate accuracy, precision and recall to measure the performance of your models.
Train and test models with Scikit-learn. Choose the best model using evaluation techniques such as cross-validation and grid search.
In this project, your goal will be to evaluate and optimize several different supervised learning algorithms to determine which algorithm will provide the highest donation yield while under some marketing constraints.
Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.
Data Scientist at Nerd Wallet
Josh has been sharing his passion for data for nearly a decade at all levels of university, and as Lead Data Science Instructor at Galvanize. He's used data science for work ranging from cancer research to process automation.
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