7.1 KiB
Introduction to Machine Learning
🎥 Click the image above for a video discussing the difference between Machine Learning, AI, and Deep Learning.
Pre-lecture quiz
Introduction
outline I What is ML II What this course covers and doesn't cover III What you can expect to learn
Welcome to this course on classic machine learning for beginners! If you are totally new to this topic, you're very welcome. If you are an experienced ML practitioner looking to brush up on an area, you're equally welcome. We want to create a friendly launching spot for your ML learning and would be happy to evaluate, respond to, and incorporate your feedback.
🎥 Click the image above for a video: MIT's John Guttag introduces Machine Learning
Getting Started with Machine Learning
Before embarking on this curriculum, you need to have your computer set up and ready to run notebooks locally.
- Learn more about how to do this in this set of videos
- It's also recommended to grasp the basics of Python, 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 and npm installed and Visual Studio Code available for both Python and JavaScript development.
- Since you are here on GitHub, working with this courseware, you might already have an account, but if not, create one and then fork this curriculum to use on your own. (Give us a star, too, please!)
- Familiarize yourself with Scikit-Learn, which we reference in these lessons, as well.
What is Machine Learning?
The term "Machine Learning" is one of the most popular and frequently used terms used today. There is a nontrivial possibility that you have heard this term at least once if you have some sort of familiarity with technology irrespective of your working domain. The mechanics of Machine Learning, however, is a mystery to most people. For a beginner in Machine Learning, the subject can sometimes feel overwhelming. Therefore, it is important to understand what Machine Learning actually and to learn about it step by step, through practical examples.
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.
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 enable us to make ourselves better and better throughout our lifetime. This learning capacity and evolving capability is related to a concept called brain plasticity. Superficially, we can draw some motivational similarities between the learning process of the human brain and the concepts of machine learning.
Human Brain 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 artificially program this whole intelligent behavioral process to a machine, it is called Artificial Intelligence. Whereas, Machine Learning, an important subset of Artificial Intelligence, is concerned with fetching meaningful information and finding hidden patterns from perceived data to corroborate the rational decision-making process.
In this curriculum, we are going to cover only the core concepts of Machine Learning that a beginner must know. However, Artificial Intelligence or Deep Neural Networks are broader concepts that are out of the scope of this learning module. But to understand broader concepts of Artificial Intelligence or Deep Learning, a strong fundamental knowledge of Machine Learning is indispensable.
The major motivation of Machine Learning is to create automated systems that can learn hidden patterns from data sophisticatedly to infer intelligent decisions which seem to be loosely inspired by how Human Brain learns certain things based on the data it perceives from the outside world.
The applications of Machine Learning are now almost everywhere. 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. Natural Language Processing (NLP) and Computer Vision (CV) are two extensions of Machine Learning, accordingly concerned with processing text/writings or sound/speech data to understand the Languages of Humans, and perusing images perceived from the real world.
Diagnosing a disease like Breast Cancer from a patient's medical history or reports, understanding Climate Change from historical weather data to predict a natural calamity, identifying a person from an image automatically, understanding the sentiment of a text, or detecting fake news to stop spreading propaganda, etc are some common use case of Machine Learning in recent times. Finance, economics, Earth science, Space exploration, biomedical, cognitive science, and even fields in the humanities have adapted Machine Learning to solve arduous problems of their domain.
Machine Learning automates the process of automation by finding meaningful insights from real-world or generated data. In this context, a bright future for machine learning is not so far. In near future, the knowledge of Machine Learning is going to be a must for people from any domain due to the wide usage of this great technology in almost every single domain.
conclusion
🚀Challenge
?
Post-lecture quiz
Review & Self Study
include this path: https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/ to warm up to these topics