@ -18,11 +18,11 @@ Welcome to this course on classical machine learning for beginners! Whether you'
Before starting with this curriculum, you need to have your computer set up and ready to run notebooks locally.
- Learn more about how to set up your machine in this [set of videos](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6).
- 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.
- 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.
- 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 :))
- Familiarize yourself with [Scikit-Learn]([https://scikit-learn.org/stable/user_guide.html), a set of ML libraries that we reference in these lessons.
- **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 :))
- **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?
@ -40,21 +40,56 @@ The [human brain](https://www.livescience.com/29365-human-brain.html) perceives
## 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', avoiding the complexities of neural networks, 'Deep Learning' - many-layered model-building - and AI, which we will discuss in a different curriculum. But 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'. 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 course you will learn:
- Core concepts of Machine Learning
- 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
### 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
- AI
> 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)
The major motivation behind leveraging Machine Learning is to create automated systems that can learn hidden patterns from data to infer intelligent decisions which seem to be loosely inspired by how the human brain learns certain things based on the data it perceives from the outside world.
## Why learn Machine Learning
Machine Learning is defined as the creation of automated systems that can learn hidden patterns from data to infer 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.
✅ 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 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. In this curriculum, you will 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.
### 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.
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.
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.
Eventually, you can use your learning to help 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, or 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.
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.
chris
3 years ago
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GitHub