Going by the title, one could easily speculate that it is about Machine Learning. Some readers might be alien to this word so I would try to first get you acquainted to this term. How many of you use Facebook? Almost everyone, right? Have you ever wondered how a face gets detected in an image by this social network or the likes of it? In simple words, we know it as face detection which is also featured in some smartphones these days. All of it is possible just because of machine learning. Before I give you a formal definition of Machine Learning let me quote few more general examples of it:
- Spam filtering
- Topic spotting
- Fraud detection
- Weather prediction
- Medical diagnosis
- Customer segmentation
- Optical character recognition
- Spoken language understanding
Now let me present you with a clear definition – “Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.”
Machine learning is the subfield of computer science that “gives computers the ability to learn without being explicitly programmed” (Arthur Samuel, 1959).
You can say that the methodology of it is same as of data mining because in both the cases, data is searched to find out patterns. The basic difference between these two is that while in data mining, data is presented for human knowledge in case of machine learning the same is used for adjustment of program actions as per the patterns. This also means that ML algorithms may or may not be supervised.
Once again let us clear this with an example of most popular social network Facebook. In it the News Feed for any particular user is personalized using machine learning programs. When a Facebook user stops to like, share or comment on a friend’s or page’s post, he or she is shown increased number of activity from that particular friend or page. The News Feeds keep on adjusting using predictive and statistical analysis.
Artificial Intelligence and Machine Learning encompasses on the deep learning, neural networks, and natural language processing technologies. The advanced processes and algorithms are developed to predict, adapt, learn, and understand the systems to adapt the automation techniques.
Benefits of Machine Learning:
- Fraud detection
- Web search results
- Real-time ads on web pages and mobile devices
- Text-based sentiment analysis
- Credit scoring and next-best offers
- Prediction of equipment failures
- New pricing models
- Network intrusion detection
- Pattern and image recognition
- Email spam filtering
It lays down important benefits to different areas of working including marketing where it helps in:
- Identifying most relevant variables
- Focused data feeds
- Allows massive data inputs from multiple sources
- Acting in real time
- Fast-paced processing, analysis and predictions
- Reducing monotony by presenting new things in front of customers
- Learning from past behaviour
Just like marketing, machine learning is applied to various other fields too.
WHY IS IT GETTING SO POPULAR?
Machine learning concentrates on the advancement of PC projects that can show themselves to develop and change when presented to new information.
According to Gartner’s 2016 Hype Cycle for Emerging Technologies, machine intelligence will be the most disruptive force in the next decade due to requirements of heavy computational power, endless data that will require machines to harness their power to solve complex problems.
Machine learning algorithms are generally known as supervised or unsupervised. Supervised algorithms use the past learning and applies it to the new data whereas Unsupervised algorithms mainly draw inferences from datasets.
How can machines learn autonomously?
The best performing systems in AI form excellent neural networks which look for training data to yield predictions and can be trained respectively.
Collecting & preparing data: An excellent form of data is collected, perhaps exploratory analysis is one such method to get the refined data details.
Training & evaluating model: An appropriate algorithm is chosen and data is represented in the form of model thereby splitting the data into 2 parts-train and test. The former part is used for model development and the latter used as a reference which determines the accuracy and the outcome.
Performance Improvement: This is an area of making it more smart with either the same variables or introducing more with another model to augment the efficiency.
Did you know?
The concept of machine learning application is quite interesting. The behemoths like Google and Facebook uses Machine Learning to improve their advertisements and effective search results whereas the applications are widespread to other industries like Banking and Finance, Healthcare, Education, Retail etc. In the future, we may see an AI and machine learning techniques while banking which will keep a track of real time transactions by applying certain statistics and techniques to prevent fraudulent activities
It might also be a part of our daily lives as we may see a robot helping to clean our house or a virtual assistant which will remind us of our daily lists. The Machine Learning skills requirement is rapidly growing with the emergence of technologies.
The key skills that a person should possess for associating with Machine Learning are:
- Probability and Statistics
- Applied Math and Algorithms
- Distributed Computing
- Expertise in Unix Tools
- Advanced Signal Processing techniques
The chart depicts the core skills and types of roles.
- The world is changing and the demand for Machine Learning professionals is increasing exponentially.
- The machine learning will be able to identify complex problems and will notify us on the current challenges and how to resolve them, all of this will be built into the structures of Machine Learning.
- Those having interest in this field must read papers like Google Map-Reduce, Google File System, Google Big Table, The Unreasonable Effectiveness of Data.
- There are great free machine learning books online that can also be followed to get more knowledge.
- If this is the future that you might be considering, then mastering and developing the skills along with the mindset will get you going long way.
Watch a short video interview shared below in which IBM Watson inventor David Ferrucci discusses five ways machine learning will impact the world in the next ten years with Kellogg professor Brian Uzzi.
Video Credits: Kellogg School of Management