Supervised Learning Course

Overview

Syllabus 10

1. Introduction to Supervised Learning

   Before diving into the many algorithms of machine learning, it is important to take a step back and understand the big picture associated with the entire field.

2. Linear Regression

   Linear regression is one of the most fundamental algorithms in machine learning. In this lesson, learn how linear regression works!

3. Perceptron Algorithm

   The perceptron algorithm is an algorithm for classifying data. It is the building block of neural networks.

4. Decision Trees

   Decision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.

5. Naive Bayes

   Naive Bayesian Algorithms are powerful tools for creating classifiers for incoming labeled data. Specifically Naive Bayes is frequently used with text data and classification problems.

6. Support Vector Machines

   Support vector machines are a common method used for classification problems. They have been proven effective using what is known as the 'kernel' trick!

7. Ensemble Methods

   Bagging and boosting are two common ensemble methods for combining simple algorithms to make more advanced models that work better than the simple algorithms would on their own.

8. Model Evaluation Metrics

   Learn the main metrics to evaluate models, such as accuracy, precision, recall, and more!

9. Training and Tuning

   Learn the main types of errors that can occur during training, and several methods to deal with them and optimize your machine learning models.

10. Finding Donors Project

    You've covered a wide variety of methods for performing supervised learning -- now it's time to put those into action!

Skills you'll practise

Naive bayes classifiersModel evaluationSupport vector machinesDecision treesConvolutional kernelsscikit-learnPerceptronCategorical data visualizationStatistical modeling fundamentalsChart typesQuantitative data visualizationSpam detectionLogistic regressionProfessional presentationsHyperparameter tuningLinear regression

Advantages and disadvantages

Some points apply to every course of this kind; see how we rank.

Before you start

Basic descriptive statistics. Data wrangling. Linear algebra. Basic probability. NumPy. Intermediate Python. Basic calculus

Taught by

• Josh Bernhard
• Luis Serrano, PhD