@ -25,10 +25,6 @@ In this lesson, you will learn how to:
> 🎥 Click the image above for a short video working through configuring your computer for ML.
[](https://youtu.be/yyQM70vi7V8 "Setup Python with Visual Studio Code")
> 🎥 Click the image above for a video: using Python within VS Code.
1. **Install Python**. Ensure that [Python](https://www.python.org/downloads/) is installed on your computer. You will use Python for many data science and machine learning tasks. Most computer systems already include a Python installation. There are useful [Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-77952-leestott) available as well, to ease the setup for some users.
Some usages of Python, however, require one version of the software, whereas others require a different version. For this reason, it's useful to work within a [virtual environment](https://docs.python.org/3/library/venv.html).
@ -36,6 +32,10 @@ In this lesson, you will learn how to:
2. **Install Visual Studio Code**. Make sure you have Visual Studio Code installed on your computer. Follow these instructions to [install Visual Studio Code](https://code.visualstudio.com/) for the basic installation. You are going to use Python in Visual Studio Code in this course, so you might want to brush up on how to [configure Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-77952-leestott) for Python development.
> Get comfortable with Python by working through this collection of [Learn modules](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-77952-leestott)
>
> [](https://youtu.be/yyQM70vi7V8 "Setup Python with Visual Studio Code")
>
> 🎥 Click the image above for a video: using Python within VS Code.
3. **Install Scikit-learn**, by following [these instructions](https://scikit-learn.org/stable/install.html). Since you need to ensure that you use Python 3, it's recommended that you use a virtual environment. Note, if you are installing this library on a M1 Mac, there are special instructions on the page linked above.
@ -47,6 +47,10 @@ You are going to use **notebooks** to develop your Python code and create machin
Notebooks are an interactive environment that allow the developer to both code and add notes and write documentation around the code which is quite helpful for experimental or research-oriented projects.
[](https://youtu.be/7E-jC8FLA2E "ML for beginners - Set up Jupyter Notebooks to start building regression models")
> 🎥 Click the image above for a short video working through this exercise.
### Exercise - work with a notebook
In this folder, you will find the file _notebook.ipynb_.
@ -88,6 +92,11 @@ Scikit-learn makes it straightforward to build models and evaluate them for use.
> This tutorial was inspired by the [linear regression example](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py) on Scikit-learn's web site.
[](https://youtu.be/2xkXL5EUpS0 "ML for beginners - Your First Linear Regression Project in Python")
> 🎥 Click the image above for a short video working through this exercise.
In the _notebook.ipynb_ file associated to this lesson, clear out all the cells by pressing the 'trash can' icon.
In this section, you will work with a small dataset about diabetes that is built into Scikit-learn for learning purposes. Imagine that you wanted to test a treatment for diabetic patients. Machine Learning models might help you determine which patients would respond better to the treatment, based on combinations of variables. Even a very basic regression model, when visualized, might show information about variables that would help you organize your theoretical clinical trials.
@ -17,16 +17,16 @@ In this lesson, you will learn:
- How to prepare your data for model-building.
- How to use Matplotlib for data visualization.
[](https://youtu.be/11AnOn_OAcE "Preparing and Visualizing data video - Click to Watch!")
> 🎥 Click the image above for a video covering key aspects of this lesson
## Asking the right question of your data
The question you need answered will determine what type of ML algorithms you will leverage. And the quality of the answer you get back will be heavily dependent on the nature of your data.
Take a look at the [data](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/data/US-pumpkins.csv) provided for this lesson. You can open this .csv file in VS Code. A quick skim immediately shows that there are blanks and a mix of strings and numeric data. There's also a strange column called 'Package' where the data is a mix between 'sacks', 'bins' and other values. The data, in fact, is a bit of a mess.
[](https://youtu.be/5qGjczWTrDQ "ML for beginners - How to Analyze and Clean a Dataset")
> 🎥 Click the image above for a short video working through preparing the data for this lesson.
In fact, it is not very common to be gifted a dataset that is completely ready to use to create a ML model out of the box. In this lesson, you will learn how to prepare a raw dataset using standard Python libraries. You will also learn various techniques to visualize the data.
## Case study: 'the pumpkin market'
@ -143,6 +143,10 @@ Now, you can analyze the pricing per unit based on their bushel measurement. If
Part of the data scientist's role is to demonstrate the quality and nature of the data they are working with. To do this, they often create interesting visualizations, or plots, graphs, and charts, showing different aspects of data. In this way, they are able to visually show relationships and gaps that are otherwise hard to uncover.
[](https://youtu.be/SbUkxH6IJo0 "ML for beginners - How to Visualize Data with Matplotlib")
> 🎥 Click the image above for a short video working through visualizing the data for this lesson.
Visualizations can also help determine the machine learning technique most appropriate for the data. A scatterplot that seems to follow a line, for example, indicates that the data is a good candidate for a linear regression exercise.
One data visualization library that works well in Jupyter notebooks is [Matplotlib](https://matplotlib.org/) (which you also saw in the previous lesson).
@ -15,6 +15,10 @@ The lessons in this section cover types of regression in the context of machine
In this series of lessons, you'll discover the differences between linear and logistic regression, and when you should prefer one over the other.
[](https://youtu.be/XA3OaoW86R8 "ML for beginners - Introduction to Regression models for Machine Learning")
> 🎥 Click the image above for a short video introducing regression models.
In this group of lessons, you will get set up to begin machine learning tasks, including configuring Visual Studio Code to manage notebooks, the common environment for data scientists. You will discover Scikit-learn, a library for machine learning, and you will build your first models, focusing on Regression models in this chapter.
> There are useful low-code tools that can help you learn about working with regression models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott)
Jim Bennett
1 year ago
committed by
GitHub