Everything You Need To Know About Machine Learning
Artificial intelligence has been one of the most revolutionary innovations today that will eventually evolve with time. People who are unaware of this concept will probably find themselves left out.
As we progress in the world of technology, each new development feels like magic and unrealistic. Some of the concepts even leave us in awe, how could this even be possible!
In an era where machines are taking over humanly roles, some years down the line, don’t be surprised if your housekeeping delivery bot comes to serve you instead of a human.
While it is impossible to predict what the future holds- now is the perfect time to start understanding how machines think. To pace beyond the roadmaps and policies of AI, we must pay attention to the details of how machines work- what they want, their failure mode, their way of reacting and acting- just as we study the human nature to understand how they behave.
Difference between AI and Machine learning
AI and Machine learning are both two hot buzzwords. Those who have just started making themselves aware of Artificial intelligence and machine learning would be confused between the two.
Artificial intelligence is the umbrella and machine learning is a sub-part of that umbrella. ML is rather an approach to AI
AI- It is a broader concept, where machines are able to carry out humanly tasks faster and hopefully better. It is an agent that absorbs its environment and makes decisions to achieve its goals. For example, AI can transcribe notes from meetings, highlight follow up tasks and even negotiate.
Machine learning is an application of AI based around the idea that we should just provide the machine access to data and let the machine learn from itself.
ML is the idea that there are generic algorithms that can give you interesting information about a set of data without having to write codes. You avoid the traditional hand-coded ruled based decision trees and instead use algorithms for it to build its own logic.
Example- This algorithm is used for recommendation of similar products, finding promising trends on the stock market, language translation services, etc.
Machine Learning Types
Supervised
Training your machine learning task for every input with a corresponding result is called supervised learning, which will then be able to provide result for any new input after sufficient training.
It is trained to identify patterns and works out a relationship for the process of classification.
Unsupervised
In unsupervised learning, on training your machine learning task with a set of inputs, it will be able to find the relationship and structure between the different inputs. It looks for patterns in unlabeled and unstructured data.
Unsupervised learning is clustering, it creates different clusters of input and if you feed the machine with new data, it will be able to allocate it to the respective cluster.
Semi-Supervised
A blend of supervised and unsupervised learning, semi-supervised makes use of unlabeled data for training- mostly uses a small amount of labelled data and a large amount of unlabeled data. This combination unequal labelled data has led to considerable improvement in learning accuracy.
Reinforcement Learning
Reinforcement learning is inspired by behaviorist psychology, concerned with how software agents ought to take action in a particular environment to maximize the cycle of consistent rewards. Unlike other learning types that functions on input/output pairs, reinforcement learning provides feedback to the algorithm.
It works on the principle of finding a balance between ‘exploration’ of uncharted territory and ‘exploitation’ of current knowledge, basically, it works on experience-based decision making.
Techniques of Machine Learning
Deep Learning
Deep learning involves layers of neural nets, it is an algorithm that is based on the model of the structure of the brain. It is a broader approach that is valuable when the function is complicated and the database is large.
This technique is robust, generalizable and scalable as it does not require a pre-determined feature and can be used across different applications.
The application of deep learning is done mainly in three fields- natural language processing, computer vision, and robotics.
Shallow Learning
On the other hand, shallow learning has very few layers of algorithms and can be applied to smaller and fewer complex data. Shallow learning is based on the concept that the user already has prior knowledge of which specific features of the input may be needed.
Machine Learning In Action
Whether you spend most of your time on Pinterest or have never used the app before, Pinterest occupies a special position in the social media ecosystem. Its core function is to curate creative content which is why it makes sense for them to invest in ML technologies.
Machine learning touches every element of Pinterest’s business operations, from content discovery to the advertisement and even prompting other functions.
Instagram and Facebook
Both Instagram and Facebook are have been hot favorite apps since years but one of the biggest changes that we have seen is the move towards an algorithm feed. On the basis of the user’s likes, shares and preferences, machine learning curates content that the user is most likely to engage in.
Both the companies are managing complex data on a large scale, that too in real-time. The algorithm is developed by converting user happiness into metrics to provide a likable feed,
Hubspot
Hubspot may be covering a niche segment, but those who are aware of the company know how it adopts emerging technologies. They are in the process of implementing machine learning and natural language processing in their internal content management system.
The will allow Hubspot to identify ‘Trigger events’ like changes in the company’s structure, management or anything related to the day-to-day operations so that they can pitch to new clients and serve existing ones.
Conclusion
Companies across the world are spending heavily on research and development of AI and Machine Learning. Some of the advancements that we’ve made so far would have been unimaginable about a decade ago, and yet the speed at which researchers, techies and scientists are progressing is truly commendable.
Let’s wait and watch the next trends that come up in this field.
