What I learned in my first step in Machine Learning
Machine Learning is used more and more inside Google. For example, when Google Music or YouTube suggests what other title or video you might be interested in. If you use Google Inbox for your emails, Inbox provides “smart replies” which devises possible replies you might be sending back. The new Google Translate premium used a neural machine translation system to make native-sounding translations of text. And more…
Netflix Using Machine Learning to Improve Streaming Quality
What is Machine Learning?
“Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”
For example :
Suppose your email program watches which emails you do or do not mark as spam, and based on that learns how to better filter spam. So we will start thinking about :
- Classifying emails as spam or not spam.
- Watching your labels emails as spam or not spam.
- The number of emails correctly classified as spam or not spam.
Machine Learning may have a different definition, depending on whom you ask. From the Internet you can find five practical definitions from reputable sources:
“Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.” – Nvidia
“Machine learning is the science of getting computers to act without being explicitly programmed.” – Stanford
“Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”- McKinsey & Co.
“Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.” – University of Washington
“The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?” – Carnegie Mellon University
What machine learning does ?
- Finds patterns in data
- Uses those patterns to predict the future
How We Get Machines to Learn?
There are different approaches to getting machines to learn, from using basic decision trees to clustering to layers of artificial neural networks (deep learning topic), depending on what task you’re trying to accomplish and the type and amount of data that you have available.
Doing machine learning well requires
- Lots of data
- Lots of compute power
- Effective machine learning algorithms
And all of those things are now more available than ever , So it’s Machine Learning time 🙂
Who’s Interested in Machine Learning?
- Business Owners: want solutions to business and data problems
- Software Engineers: want to create better applications
- Data Scientists: want powerful and easy-to-use tools
Is there a specific programming Language to start ?
Yes , R is an open source programming language and environment:
- Supports machine learning, statistical computing
- Has many available packages R is very popular
- Many commercial machine learning offerings support R
But it’s not the only choice:
– Python is also increasingly popular
The Opportunities of Machine Learning
- Image recognition
- Voice recognition
- Intelligent data analysis
- Self-driving Cars
- Machine learning lets us find patterns in existing data, then create and use a model that recognizes those patterns in new data
- Machine learning can probably help your organization
Good luck and Keep learning 🙂