[](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning - What's the difference?")
@ -13,7 +13,7 @@ Welcome to this course on classical machine learning for beginners! Whether you'
[](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
### Getting started with machine learning
Before starting with this curriculum, you need to have your computer set up and ready to run notebooks locally.
@ -23,9 +23,9 @@ Before starting with this curriculum, you need to have your computer set up and
- **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?
### What is machine learning?
The term 'Machine Learning' is one of the most popular and frequently used terms of today. There is a nontrivial possibility that you have heard this term at least once if you have some sort of familiarity with technology, no matter what domain you work in. The mechanics of Machine Learning, however, are a mystery to most people. For a Machine Learning beginner, the subject can sometimes feel overwhelming. Therefore, it is important to understand what Machine Learning actually is, and to learn about it step by step, through practical examples.
The term 'machine learning' is one of the most popular and frequently used terms of today. There is a nontrivial possibility that you have heard this term at least once if you have some sort of familiarity with technology, no matter what domain you work in. The mechanics of machine learning, however, are a mystery to most people. For a machine learning beginner, the subject can sometimes feel overwhelming. Therefore, it is important to understand what machine learning actually is, and to learn about it step by step, through practical examples.
@ -35,20 +35,26 @@ We live in a universe full of fascinating mysteries. Great scientists such as St
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 to uncover meaningful information and find 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**.
> 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)
## 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' 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.
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
- Core concepts of machine learning
- The history of ML
- ML and fairness
- The definition of "Classical Machine Learning"
- The definition of "Classical machine learning"
- Regression
- Classification
- Clustering
@ -59,16 +65,11 @@ In this course you will learn:
## What we will not cover
- deep learning
- Neural Networks
- 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)
## Why study Machine Learning?
## Why study machine learning?
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.
@ -76,7 +77,7 @@ This motivation is loosely inspired by how the human brain learns certain things
✅ 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
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.
@ -93,6 +94,7 @@ Machine learning automates the process of pattern-discovery by finding meaningfu
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.
In this lesson, we will walk through the major milestones in the history of Machine Learning and Artificial Intelligence.
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 [algorithmical, 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.'
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 [algorithmical, 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.'
## Notable Discoveries
@ -27,7 +27,7 @@ Alan Turing, a truly remarkable person who was voted [by the public in 2019](htt
## 1956: Dartmouth Summer Research Project
"The Dartmouth Summer Research Project on Artificial Intelligence was a seminal event for artificial intelligence as a field," and it was here that the term 'Artificial Intelligence' was coined ([source](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)).
"The Dartmouth Summer Research Project on artificial intelligence was a seminal event for artificial intelligence as a field," and it was here that the term 'artificial intelligence' was coined ([source](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)).
> Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.
@ -89,7 +89,7 @@ This epoch saw a new era for ML and AI to be able to solve some of the problems
## Now
Today, Machine Learning and AI touch almost every part of our lives. This era calls for careful understanding of the risks and potentials effects of these algorithms on human lives. As Microsoft's Brad Smith has stated, "Information technology raises issues that go to the heart of fundamental human-rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products. In our view, they also call for thoughtful government regulation and for the development of norms around acceptable uses" ([source](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
Today, machine learning and AI touch almost every part of our lives. This era calls for careful understanding of the risks and potentials effects of these algorithms on human lives. As Microsoft's Brad Smith has stated, "Information technology raises issues that go to the heart of fundamental human-rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products. In our view, they also call for thoughtful government regulation and for the development of norms around acceptable uses" ([source](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
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.
The history of trying to make computers understand human language goes back decades, and one of the earliest scientists to consider natural language processing was *Alan Turing*. When Turing was researching *Artificial Intelligence* in the 1950's, he considered if a conversational test could be given to a human and computer (via typed correspondence) where the human in the conversation was not sure if they were conversing with another human or a computer. If, after a certain length of conversation, the human could not determine that the answers were from a computer or not, then could the computer be said to be *thinking*?
The history of trying to make computers understand human language goes back decades, and one of the earliest scientists to consider natural language processing was *Alan Turing*. When Turing was researching *artificial intelligence* in the 1950's, he considered if a conversational test could be given to a human and computer (via typed correspondence) where the human in the conversation was not sure if they were conversing with another human or a computer. If, after a certain length of conversation, the human could not determine that the answers were from a computer or not, then could the computer be said to be *thinking*?
[](https://youtu.be/QD8mQXaUFG4 "Chatting with Eliza")
## Regional topic: European literature and Romantic Hotels of Europe ❤️
In this section of the curriculum, you will be introduced to one of the most widespread uses of machine learning: Natural Language Processing (NLP). Derived from Computational Linguistics, this category of Artificial Intelligence is the bridge between humans and machines via voice or textual communication.
In this section of the curriculum, you will be introduced to one of the most widespread uses of machine learning: Natural Language Processing (NLP). Derived from Computational Linguistics, this category of artificial intelligence is the bridge between humans and machines via voice or textual communication.
In these lessons we'll learn the basics of NLP by building small conversational bots to learn how Machine Learning aids in making these conversations more and more 'smart'. You'll travel back in time, chatting with Elizabeth Bennett and Mr. Darcy from Jane Austen's classic novel, **Pride and Prejudice**, published in 1813. Then, you'll further your knowledge by learning about sentiment analysis via hotel reviews in Europe.
Jen Looper
3 years ago