revisiting capitalization strategy
This commit is contained in:
Jen Looper
Родитель
bf1513b5d7
Коммит
d78cf4386c
|
|
@ -1,27 +1,26 @@
|
|||
# Introduction to Machine Learning
|
||||
|
||||
[](https://youtu.be/lTd9RSxS9ZE "ML, AI, Deep Learning - What's the difference?")
|
||||
[](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.
|
||||
> 🎥 Click the image above for a video discussing the difference between machine learning, AI, and deep learning.
|
||||
|
||||
## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
|
||||
|
||||
### Introduction
|
||||
|
||||
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 learning and would be happy to evaluate, respond to, and incorporate your [feedback](https://github.com/microsoft/ML-For-Beginners/discussions).
|
||||
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")
|
||||
|
||||
> 🎥 Click the image above for a video: MIT's John Guttag introduces Machine Learning
|
||||
|
||||
### Getting started with Machine Learning
|
||||
> 🎥 Click the image above for a video: MIT's John Guttag introduces machine learning
|
||||
### Getting Started with Machine Learning
|
||||
|
||||
Before starting with this curriculum, you need to have your computer set up and ready to run notebooks locally.
|
||||
|
||||
- **Configure your machine with these videos**. Learn more about how to set up your machine in this [set of videos](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6).
|
||||
- **Learn Python**. It's also recommended to have a basic understanding of [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), a programming language useful for data scientists that we use in this course.
|
||||
- **Learn Node.js and JavaScript**. We also use JavaScript a few times in this course when building web apps, so you will need to have [node](https://nodejs.org) and [npm](https://www.npmjs.com/) installed, as well as [Visual Studio Code](https://code.visualstudio.com/) available for both Python and JavaScript development.
|
||||
- **Create a GH account**. Since you found us here on [GitHub](https://github.com), you might already have an account, but if not, create one and then fork this curriculum to use on your own. (Feel free to give us a star, too :))
|
||||
- **Create a GitHub account**. Since you found us here on [GitHub](https://github.com), you might already have an account, but if not, create one and then fork this curriculum to use on your own. (Feel free to give us a star, too 😊)
|
||||
- **Explore Scikit-Learn**. Familiarize yourself with [Scikit-Learn]([https://scikit-learn.org/stable/user_guide.html), a set of ML libraries that we reference in these lessons.
|
||||
|
||||
### What is Machine Learning?
|
||||
|
|
@ -32,68 +31,71 @@ The term 'Machine Learning' is one of the most popular and frequently used terms
|
|||
|
||||
> Google Trends shows the recent 'hype curve' of the term 'machine learning'
|
||||
|
||||
We live in a universe full of unusual and interesting mysteries. Great scientists such as Stephen Hawking, Albert Einstein, and many more have devoted their lives in search of meaningful information that uncovers the mysteries of the world around us. This is the human condition of learning: a human child learns new things and uncovers the structure of their world year by year as they grow to adulthood.
|
||||
We live in a universe full of fascinating mysteries. Great scientists such as Stephen Hawking, Albert Einstein, and many more have devoted their lives to searching for meaningful information that uncovers the mysteries of the world around us. This is the human condition of learning: a human child learns new things and uncovers the structure of their world year by year as they grow to adulthood.
|
||||
|
||||
A child's brain and senses perceive the facts of their surroundings and gradually learn the hidden patterns of life which help the child to craft logical rules to identify learned patterns. The learning process of the human brain makes humans the most sophisticated living creature of this world. Learning continuously by discovering hidden patterns and then innovating on those patterns enables us to make ourselves better and better throughout our lifetime. This learning capacity and evolving capability is related to a concept called [brain plasticity](https://www.simplypsychology.org/brain-plasticity.html). Superficially, we can draw some motivational similarities between the learning process of the human brain and the concepts of machine learning.
|
||||
|
||||
The [human brain](https://www.livescience.com/29365-human-brain.html) perceives things from the real world, processes the perceived information, makes rational decisions, and performs certain actions based on circumstances. This is what we called behaving intelligently. When we program a facsimile of the intelligent behavioral process to a machine, it is called Artificial Intelligence (AI). Although the terms can be confused, Machine Learning (ML) is an important subset of Artificial Intelligence. **ML is concerned with using specialized algorithms fetching meaningful information and finding hidden patterns from perceived data to corroborate the rational decision-making process**.
|
||||
The [human brain](https://www.livescience.com/29365-human-brain.html) perceives things from the real world, processes the perceived information, makes rational decisions, and performs certain actions based on circumstances. This is what we called behaving intelligently. When we program a facsimile of the intelligent behavioral process to a machine, it is called Artificial Intelligence (AI). Although the terms can be confused, Machine Learning (ML) is an important subset of Artificial Intelligence. **ML is concerned with using specialized algorithms to uncover meaningful information and find hidden patterns from perceived data to corroborate the rational decision-making process**.
|
||||
|
||||
## What you will learn in this course
|
||||
|
||||
In this curriculum, we are going to cover only the core concepts of Machine Learning that a beginner must know. We cover what we call 'Classical Machine Learning'. To understand broader concepts of Artificial Intelligence or Deep Learning, a strong fundamental knowledge of Machine Learning is indispensable, and so we would like to offer it here. You will additionally learn the basics of Regression, Classification, Clustering, Natural Language Processing, Time Series, and Reinforcement Learning, as well as real-world applications, the history of ML, ML and Fairness, and how to use your model in a web app.
|
||||
In this curriculum, we are going to cover only the core concepts of Machine Learning that a beginner must know. We cover what we call 'Classical Machine Learning' primarily using Scikit-Learn, an excellent library many students use to learn the basics. To understand broader concepts of Artificial Intelligence or deep learning, a strong fundamental knowledge of Machine Learning is indispensable, and so we would like to offer it here.
|
||||
|
||||
You will additionally learn the basics of Regression, Classification, Clustering, Natural Language Processing, Time Series Forecasting, and Reinforcement Learning, as well as real-world applications, the history of ML, ML and Fairness, and how to use your model in web apps.
|
||||
|
||||
In this course you will learn:
|
||||
|
||||
- Core concepts of Machine Learning
|
||||
- The history of ML
|
||||
- ML and fairness
|
||||
- The definition of "Classical Machine Learning"
|
||||
- Regression
|
||||
- Classification
|
||||
- Clustering
|
||||
- Natural Language Processing
|
||||
- Time series
|
||||
- Reinforcement learning
|
||||
- Real world applications
|
||||
- History of ML and ML and fairness
|
||||
- Time Series Forecasting
|
||||
- Reinforcement Learning
|
||||
- Real-world applications
|
||||
## What we will not cover
|
||||
|
||||
### We will not cover
|
||||
|
||||
To make for a better learning experience, we will avoid the complexities of neural networks, 'Deep Learning' - many-layered model-building - and AI, which we will discuss in a different curriculum.
|
||||
|
||||
- Deep Learning
|
||||
- deep learning
|
||||
- Neural Networks
|
||||
- AI
|
||||
|
||||
To make for a better learning experience, we will avoid the complexities of neural networks, 'deep learning' - many-layered model-building using neural networks - and AI, which we will discuss in a different curriculum. We also will offer a forthcoming data science curriculum to focus on that aspect of this larger field.
|
||||
|
||||
|
||||
|
||||
|
||||
> A diagram showing the relationships between AI, ML, Deep Learning, and Data Science. Infographic by [Jen Looper](https://twitter.com/jenlooper) inspired by [this graphic](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)
|
||||
> A diagram showing the relationships between AI, ML, deep learning, and data science. Infographic by [Jen Looper](https://twitter.com/jenlooper) inspired by [this graphic](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)
|
||||
|
||||
## Why learn Machine Learning
|
||||
## Why study Machine Learning?
|
||||
|
||||
Machine Learning is defined as the creation of automated systems that can learn hidden patterns from data to infer intelligent decisions.
|
||||
Machine learning, from a systems perspective, is defined as the creation of automated systems that can learn hidden patterns from data to aid in making intelligent decisions.
|
||||
|
||||
The major motivation behind leveraging Machine Learning is to create automated systems that can learn hidden patterns from data to infer intelligent decisions. This motivation seem to be loosely inspired by how the human brain learns certain things based on the data it perceives from the outside world.
|
||||
This motivation is loosely inspired by how the human brain learns certain things based on the data it perceives from the outside world.
|
||||
|
||||
✅ Think for a minute why a business would want to try to use Machine Learning strategies vs. creating a hard-coded rules-based engine.
|
||||
✅ Think for a minute why a business would want to try to use machine learning strategies vs. creating a hard-coded rules-based engine.
|
||||
|
||||
### Applications of Machine Learning
|
||||
|
||||
Applications of Machine Learning are now almost everywhere, and are as ubiquitous as the data that is flowing around our societies, generated by our smart phones, connected devices, and other systems. Considering the immense potential of state-of-the-art Machine Learning algorithms, researchers have been exploring their capability to solve multi-dimensional and multi-disciplinary real-life problems with great positive outcomes.
|
||||
Applications of machine learning are now almost everywhere, and are as ubiquitous as the data that is flowing around our societies, generated by our smart phones, connected devices, and other systems. Considering the immense potential of state-of-the-art machine learning algorithms, researchers have been exploring their capability to solve multi-dimensional and multi-disciplinary real-life problems with great positive outcomes.
|
||||
|
||||
You can use Machine Learning in many ways:
|
||||
**You can use machine learning in many ways**:
|
||||
|
||||
- Predict the likelihood of disease from a patient's medical history or reports.
|
||||
- Leverage weather data to predict weather events.
|
||||
- Understand the sentiment of a text.
|
||||
- Detect fake news to stop the spread of propaganda.
|
||||
- To predict the likelihood of disease from a patient's medical history or reports.
|
||||
- To leverage weather data to predict weather events.
|
||||
- To understand the sentiment of a text.
|
||||
- To detect fake news to stop the spread of propaganda.
|
||||
|
||||
Finance, economics, earth science, space exploration, biomedical engineering, cognitive science, and even fields in the humanities have adapted Machine Learning to solve the arduous, data-processing heavy problems of their domain.
|
||||
Finance, economics, earth science, space exploration, biomedical engineering, cognitive science, and even fields in the humanities have adapted machine learning to solve the arduous, data-processing heavy problems of their domain.
|
||||
|
||||
Machine Learning automates the process of pattern-discovery by finding meaningful insights from real-world or generated data. It has proven itself to be highly valuable in business, health, and financial applications, among others.
|
||||
Machine learning automates the process of pattern-discovery by finding meaningful insights from real-world or generated data. It has proven itself to be highly valuable in business, health, and financial applications, among others.
|
||||
|
||||
In the near future, understanding the basics of Machine Learning is going to be a must for people from any domain due to its widespread adoption.
|
||||
In the near future, understanding the basics of machine learning is going to be a must for people from any domain due to its widespread adoption.
|
||||
|
||||
## 🚀 Challenge
|
||||
|
||||
Sketch, on paper or using an online app like [Excalidraw](https://excalidraw.com/), your understanding of the differences between AI, ML, Deep Learning, and Data Science. Add some ideas of problems that each of these techniques are good at solving.
|
||||
Sketch, on paper or using an online app like [Excalidraw](https://excalidraw.com/), your understanding of the differences between AI, ML, deep learning, and data science. Add some ideas of problems that each of these techniques are good at solving.
|
||||
|
||||
## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
|
||||
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
## Instructions
|
||||
|
||||
In this non-graded assignment, you should brush up on Python and get your environment up and running and able to run notebooks.
|
||||
In this non-graded assignment, you should brush up on Python and get your environment up and running and able to run notebooks.
|
||||
|
||||
Take this [Python Learning Path](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), and then get your systems setup by going through these introductory videos:
|
||||
|
||||
|
|
|
|||
|
|
@ -93,8 +93,8 @@ Today, Machine Learning and AI touch almost every part of our lives. This era ca
|
|||
|
||||
It remains to be seen what the future holds, but it is important to understand these computer systems and the software and algorithms that they run. We hope that this curriculum will help you to gain a better understanding so that you can decide for yourself.
|
||||
|
||||
[](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of Deep Learning")
|
||||
> 🎥 Click the image above for a video: Yann LeCun discusses the history of Deep Learning in this lecture
|
||||
[](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of deep learning")
|
||||
> 🎥 Click the image above for a video: Yann LeCun discusses the history of deep learning in this lecture
|
||||
|
||||
## 🚀Challenge
|
||||
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
In this curriculum, you have learned many ways to prepare data for training and create machine learning models. You built a series of classic Regression, Clustering, Classification, Natural Language Processing, and Time Series models. Congratulations! Now, you might be wondering what it's all for... what are the real world applications for these models?
|
||||
|
||||
While a lot of interest in industry has been garnered by AI, which usually leverages Deep Learning, there are still valuable applications for classical machine learning models. You might even use some of these applications today! In this lesson, you'll explore how eight different industries and subject-matter domains use these types of models to make their applications more performant, reliable, intelligent, and valuable to users.
|
||||
While a lot of interest in industry has been garnered by AI, which usually leverages deep learning, there are still valuable applications for classical machine learning models. You might even use some of these applications today! In this lesson, you'll explore how eight different industries and subject-matter domains use these types of models to make their applications more performant, reliable, intelligent, and valuable to users.
|
||||
|
||||
## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/47/)
|
||||
|
||||
|
|
@ -138,7 +138,7 @@ Identify another sector that benefits from some of the techniques you learned in
|
|||
|
||||
## Review & Self Study
|
||||
|
||||
The Wayfair Data Science team has several interesting videos on how they use ML at their company. It's worth [taking a look](https://www.youtube.com/channel/UCe2PjkQXqOuwkW1gw6Ameuw/videos)!
|
||||
The Wayfair data science team has several interesting videos on how they use ML at their company. It's worth [taking a look](https://www.youtube.com/channel/UCe2PjkQXqOuwkW1gw6Ameuw/videos)!
|
||||
|
||||
## Assignment
|
||||
|
||||
|
|
|
|||
|
|
@ -86,7 +86,7 @@
|
|||
"isCorrect": "true"
|
||||
},
|
||||
{
|
||||
"answerText": "Deep Learning",
|
||||
"answerText": "deep learning",
|
||||
"isCorrect": "false"
|
||||
},
|
||||
{
|
||||
|
|
|
|||
Загрузка…
Ссылка в новой задаче