Howdy! This is an abrupt interruption to our regularly scheduled programming of SQL lessons, Amazon Alexa Skill development and Algorithmic trading. For those readers who don’t personally know me, I’m on a quest/streak to level up as a technologist.
Getting around to the point, I’ve been taking self-paced courses in varying forms to learn, apply and share new skills. However, I’ve heard the Coursera Stanford class in Machine Learning taught by Andrew Ng recommended so widely, I’m just going to doggedly sprint a marathon. I’m starting almost a week behind, working a busy job, trying to have a social life, and many other things…but darnit, I’m going to give this class my best shot. Hopefully I finish. 🙂
Okay, over-sharing complete. Let’s jump into a brief introduction of Machine Learning.
Machine learning, according to Andrew Ng (Chief Scientist at Baidu), is the science of getting computers to learn without being explicitly programmed.
Where is machine learning used in our lives?
Machine learning is employed a large number of actors. Here are a few examples:
- Search engines, such as Bing, use machine learning to process MASSIVE amounts of data to quickly rank web pages in order of relevance, with limited human intervention.
- Social networks, such as Facebook, use machine learning to recognize your friend’s faces for auto-tagging capabilities.
- Email providers such as Apple Mail may employ spam filters that continuously learn to protect your inbox, your computer, and most importantly, your sanity.
- Tech companies such as Amazon use natural language processing (NLP) to create conversational experiences and transactions with skills and services.
- Entertainment companies such as Netflix use self-learning algorithms to recommend compelling new films and TV shows for those of you who binge watch
Where did machine learning come from?
Machine learning originated from a computer science field known as artificial intelligence. Long seen as a pipe dream from Star Trek (crass and careless reference, I know), machine learning is a practical and attainable segue to artificial intelligence, or machines and programs that contain some degree of self awareness.
This capability is a relatively new, yet a rapidly exploding field that grows as mathematical, statistical, hardware and software capabilities continue to compound and improve.
What follows is a better question still.
Where is machine learning headed?
Machine Learning in the future could look like a few different things (but not limited to this list, obviously!):
- Predictive and preventative applications in engineering, medicine, and security
- “Load bearing” performance in complex tasks, such as architecting, coding and programming self-driving cars
- Coordinate machines and programs that study our behavior at our request and perform tasks, such as performing spring cleaning
- Assistants or programs that are intelligent – able to optimize and independently solve problems on our behalf
Wrap-Up on the Machine Learning Introduction
That wasn’t so bad was it? We’ll follow soon with a more formal definition of Machine Learning and its various tranches of study. Cheers all.