Discover how surprisingly easy & fun machine learning can be! By the end of this course, you’ll be shocked at how well you understand and can apply a wide range of supervised-learning techniques — from simple linear regression to support vector machines (SVM).
Overview
Syllabus
- Regression
- Learn the difference between Regression and Classification, train a Linear Regression model to predict values, and learn to predict states using Logistic Regression.
- Perceptron Algorithms
- Learn the definition of a perceptron as a building block
for neural networks and the perceptron algorithm for
classification. - Decision Trees
- Train Decision Trees to predict states and use Entropy to build decision trees, recursively.
- Naive Bayes
- 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. - Support Vector Machines
- 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. - Ensemble of Learners
- Build professional presentations and data visualizations for quantitative and categorical data. Create pie, bar, line, scatter, histogram, and boxplot charts.
- Evaluation Metrics
- Calculate accuracy, precision and recall to measure the performance of your models.
- Training and Tuning Models
- Train and test models with Scikit-learn. Choose the best model using evaluation techniques such as cross-validation and grid search.
- Course Project: Find Donors for CharityML
- 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.
Taught by
Luis Serrano and Josh Bernhard_color
