Want to learn the basics of large-scale data processing? Need to make predictive models but don’t know the right tools? This course will introduce you to open source tools you can use for parallel, distributed and scalable machine learning.

STARTS

Dec 12th 2016

Created by:Delivered by:

Taught by:

Case Study - Predicting Housing Prices

In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets.

By the end of this course, you will be able to:

-Describe the input and output of a regression model.

-Compare and contrast bias and variance when modeling data.

-Estimate model parameters using optimization algorithms.

-Tune parameters with cross validation.

-Analyze the performance of the model.

-Describe the notion of sparsity and how LASSO leads to sparse solutions.

-Deploy methods to select between models.

-Exploit the model to form predictions.

-Build a regression model to predict prices using a housing dataset.

-Implement these techniques in Python.

Syllabus

Week 1: Welcome:Simple Linear Regression

Week 2: Multiple Regression

Week 3: Assessing Performance

Week 4: Ridge Regression

Week 5: Feature Selection & Lasso

Week 6: Nearest Neighbors & Kernel Regression ;Closing Remarks

Machine Learning: Regression is course 2 of 6 in the Machine Learning Specialization.

This Specialization provides a case-based introduction to the exciting, high-demand field of machine learning. You’ll learn to analyze large and complex datasets, build applications that can make predictions from data, and create systems that adapt and improve over time. In the final Capstone Project, you’ll apply your skills to solve an original, real-world problem through implementation of machine learning algorithms.