[](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning - What's the difference?")
> 🎥 Click the image above for a video discussing the difference between machine learning, AI, and deep learning.
[](https://youtu.be/6mSx_KJxcHI "ML for beginners - Introduction to Machine Learning for Beginners")
> 🎥 Click the image above for a short video working through this lesson.
Welcome to this course on classical machine learning for beginners! Whether you're completely new to this topic, or an experienced ML practitioner looking to brush up on an area, we're happy to have you join us! We want to create a friendly launching spot for your ML study and would be happy to evaluate, respond to, and incorporate your [feedback](https://github.com/microsoft/ML-For-Beginners/discussions).
[](https://youtu.be/h0e2HAPTGF4 "Introduction to ML")
[](https://youtu.be/N6wxM4wZ7V0 "ML for beginners - History of Machine Learning")
> 🎥 Click the image above for a short video working through this lesson.
In this lesson, we will walk through the major milestones in the history of machine learning and artificial intelligence.
The history of artificial intelligence (AI) as a field is intertwined with the history of machine learning, as the algorithms and computational advances that underpin ML fed into the development of AI. It is useful to remember that, while these fields as distinct areas of inquiry began to crystallize in the 1950s, important [algorithmic, statistical, mathematical, computational and technical discoveries](https://wikipedia.org/wiki/Timeline_of_machine_learning) predated and overlapped this era. In fact, people have been thinking about these questions for [hundreds of years](https://wikipedia.org/wiki/History_of_artificial_intelligence): this article discusses the historical intellectual underpinnings of the idea of a 'thinking machine.'
[](https://youtu.be/4NGM0U2ZSHU "ML for beginners - Techniques of Machine Learning")
> 🎥 Click the image above for a short video working through this lesson.
## Introduction
On a high level, the craft of creating machine learning (ML) processes is comprised of a number of steps:
@ -21,6 +21,10 @@ In this lesson, you will learn how to:
## Installations and configurations
[](https://youtu.be/-DfeD2k2Kj0 "ML for beginners -Setup your tools ready to build Machine Learning models")
> 🎥 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.
@ -199,7 +203,7 @@ Congratulations, you built your first linear regression model, created a predict
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## 🚀Challenge
Plot a different variable from this dataset. Hint: edit this line: `X = X[:, np.newaxis, 2]`. Given this dataset's target, what are you able to discover about the progression of diabetes as a disease?
Plot a different variable from this dataset. Hint: edit this line: `X = X[:,2]`. Given this dataset's target, what are you able to discover about the progression of diabetes as a disease?
@ -108,7 +108,7 @@ Seaborn offers some neat ways to visualize your data. For example, you can compa
✅ Given this categorical plot, what are some interesting explorations you can envision?
### Data pre-processing: feature and label encoding
Our pumpkins dataset contains string values for all its columns. Working with categorical data is intuitive for humans but not for machines. Machine learning algorithms work well with numbers. There's why encoding is a very important step in the data pre-processing phase, since it enables to turn categorical data into numerical data, without losing any information. A good encoding leads to build a good model.
Our pumpkins dataset contains string values for all its columns. Working with categorical data is intuitive for humans but not for machines. Machine learning algorithms work well with numbers. That's why encoding is a very important step in the data pre-processing phase, since it enables us to turn categorical data into numerical data, without losing any information. Good encoding leads to building a good model.
For feature encoding there are two main types of encoders:
@ -44,6 +44,14 @@ Travel with us around the world as we apply these classic techniques to data fro
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## Video walkthroughs
Some of the lessons are available as short form video. You can find all these in-line in the lessons, or on the [ML for Beginners playlist on the Microsoft Developer YouTube channel](https://aka.ms/ml-beginners-videos) by clicking the image below.
[](https://aka.ms/ml-beginners-videos)
Jim Bennett
2 years ago
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GitHub
