Software tools you'll need for this course

How this specialization was designed. The learning approach in this specialization is to start from use cases and then dig into algorithms and methods, what we call a case-studies approach. We are very excited about this approach, since it has worked well in several other courses. The first course, Machine Learning Foundations, was focused on understanding how ML can be used in various cases studies, and the follow on courses will dig into the details of algorithms and methods for each of the main ML areas. We expect all learners to have taken the first course, before taking this course.

Regression - A Machine Learning Approach. This course focuses regression, one of the most important types of data analysis, with a wide range of applications. After successfully completing this course, you will be able to use regression methods in practice, implement some of the most fundamental algorithms in this area, and choose the right model for your task. In addition, you will become proficient in core ML concepts that transcend regression settings: you will understand the practical implications of fundamental ML concepts, such as the bias-variance tradeoff, key ML data analysis techniques, such as cross validation, and the most important optimization techniques used to learn ML models, gradient descent and coordinate descent.

Programming assignment format

Every module will be associated with one or two programming assignments. The goal of these assignments is to have hands-on experience on the techniques we discuss in lectures. To test your implementations, you will be asked questions in a quiz following the assignment.

In every module, you will be implementing a core regression technique or other ML concept from scratch. In modules where we have two programming assignments:

Why Python

In this course, we are going to use the Python programming language to build several intelligent applications that use machine learning. Python is a simple scripting language that makes it easy to interact with data. Furthermore, Python has a wide range of packages that make it easy to get started and build applications, from the simplest ones to the most complex. Python is widely used in industry, and is becoming the de facto language for data science in industry. (R is another alternative language. However, R tends to be significantly less scalable and has very few deployment tools, thus it is seldomly used for production code in industry. It is possible, but discouraged to use R in this specialization.)

We will also encourage the use the IPython Notebook in our assignments. The IPython Notebook is a simple interactive environment for programming with Python, which makes it really easy to share your results. Think about it as a combination of a Python terminal and a wiki page. Thus, you can combine code, plots and text to explain what you did. (You are not required to use IPython Notebook in the assignments, and should have no problem using straight up Python if you prefer.)

Useful software tools

Although you will be implementing algorithms from scratch in various assignments, some software tools will be useful in the process. In particular, there are four types of data tools that would be helpful:

Tools for data manipulation

For data manipulation, we recommend using SFrame , an open-source, highly-scalable Python library for data manipulation. An alternative is the Pandas library. A huge advantage of SFrame over Pandas is that with SFrame, you are not limited to datasets that fit in memory, which allows you to deal with large datasets, even on a laptop. (The SFrame API is very similar to Pandas' API. Here is a doc showing the relationship between the two of them. )

Tools for matrix operation

For matrix operations, we strongly recommend Numpy , an open-source Python library that provides fast performance, for data that fits in memory.

Tools for plotting

For plotting, we strongly recommend you use Matplotlib , an open-source Python library with extensive plotting functionality.

Tools with pre-implemented ML algorithms

For the few assignments where you will be using pre-implemented ML algorithms, we recommend you use GraphLab Create , which we used in the first course, a package we have been working on for many years now, and has seen an exciting adoption curve, especially in industry with folks building real applications. A popular alternative is to use scikit-learn . GraphLab Create is more scalable than scikit-learn and simpler to use when your data is not numeric vectors. On the other hand, scikit-learn is open-source.

In this course, most of the assignments are about implementing algorithms from scratch, so this choice is more flexible than in the first course. We are happy, however, for you to use any tool(s) of your liking. As you will notice, we are only grading the output of your programs, so the specific software tool is not the focus of the course. More details on using other tools are at the end of this doc.

It's important to emphasize that this specialization is not about providing training for a specific software package. The goal of the specialization is for your effort to be spent on learning the fundamental concepts and algorithms behind machine learning in a hands-on fashion. These concepts transcend any single package. What you learn here you can use whether you write code from scratch, use any existing ML packages out there, or any that may be developed in the future. We are happy to hear that so many of you are enjoying this approach so far!

Licenses for SFrame & GraphLab Create

The SFrame package is available in open-source under a permissive BSD license . So, you will always be able to use SFrames for free. GraphLab Create is free on a 1-year, renewable license for educational purposes, including Coursera. The reason we suggest you use GraphLab Create for this course is because this software will make it much easier for you see machine learning in action and to help you complete your assignments quickly.

Upgrade GraphLab Create

If you are using GraphLab Create and already have it installed (e.g., from the first course), please make sure you upgrade to the latest version! The simplest way to do this is to:

  1. Open the GraphLab Launcher.

  2. Click on 'TERMINAL'.

  3. On the terminal window, type: 

pip install --upgrade graphlab-create

Resources

These are some good resources you can explore, if you are using the recommended software tools:

Installing the recommended software tools

If you choose to use the recommended tools, you have two options: downloading and installing the required software or using a prepackaged version on a free instance on Amazon EC2.

Option 1: Downloading and installing all software on your own machine

Download and install Python, IPython Notebook, Numpy, SFrame and GraphLab Create. You can find the instructions here .

Option 2: Using a free Amazon EC2 with all the software pre-installed

If you do not have a 64-bit computer, you will not be able to run GraphLab Create. Additionally, some of you may want a simple experience where you don't have to download the course content and install everything locally. Here, we'll address these situations!

Amazon EC2 offers free cloud computing hours with what they call micro instances. These instances are all we need to do the work for this course. We have created an image for one such instance that is easy to launch and contains all the course content. This will allow you to run everything you need for this course in the cloud for free, without having to install anything locally. (You do need to create an Amazon EC2 account and have internet access.)

You can find step-by-step instructions here:

https://turi.com/download/install-graphlab-create-aws-coursera.html

We note that installing all the software on your own local machine may be the right option for most people; especially since you can run locally everything without needing to be online to do the homeworks. But, the option using Amazon EC2 should be a great alternative.

Github repository with starter code

In each module of the course, we have a reading with the assignments for that module as well as some starter code. For those interested, the starter code and demos used in this course are also available in a public Github repository:

https://github.com/learnml/machine-learning-specialization

Using other software packages

We strongly encourage you to use the recommended software packages for this course, since they will allow you to learn the fundamental concepts more quickly. However, you are welcome to use others. Here are a few notes if you do so.

Installing other software tools

In the instructions above, you will be using the Graphlab Launcher, which will automatically install Python, IPython Notebook, Numpy, Matplotlib, SFrame and GraphLab Create. If you don’t use the Graphlab Launcher, you will need to install each of these tools separately, by following the pages linked above. Anaconda is a good tool to help simplify some of this installation.

If you are using SFrame, but not GraphLab Create

GraphLab Create uses SFrame under the hood, but you can use just SFrame for most assignments. If you choose to do so, in the starter code for the assignments, you should change the line 

import graphlab

import sframe 

import sframe

and everything should work with just some small modifications, e.g., the calls: 

graphlab.SFrame(...)

will become 

sframe.SFrame(...)

If you are using other software tools out there

You are welcome to use other packages, e.g., scikit-learn instead of GraphLab Create, or Pandas instead of SFrame, or even R instead of Python. If you choose to use all these different packages, we will provide the datasets (in standard CSV format) and the assignment questions will not depend specifically on the recommended tools.