Demystifying Machine Learning
“Computers are able to see, hear and learn. Welcome to the future.” — Dave Waters
Machine Learning (ML) is all around us in our contemporary world. It brings out the potential of data in a new way. Working on the development of computer programs that can access data and perform tasks automatically through predictions and detections, Machine Learning enables computer systems to learn and improve from experience continuously.
As you feed the machine with more data, thus enabling the algorithms that cause it to “learn”, you improve on the results. When you ask Alexa to play your favorite music station on the Amazon Echo, she will go to the one you have played the most. The station is made better by telling Alexa to skip a song, increase volume, and other various inputs. All of this occurred because of the rapid advance of Machine Learning.
Definition of ML
Machine Learning is defined as the science of getting computers to learn and act as humans do, improve their learning over time in an autonomous way by feeding them data and information in the form of observations and real-world interactions.
Doesn’t this sound similar to AI?
For many, Machine Learning and Artificial Intelligence mean the same. But there are some distinct differences between these two. Artificial intelligence is a technology that enables a machine to mimic human behavior. Whereas Machine Learning is a subset of AI which allows a machine to learn automatically from past data without programming explicitly.
What is the objective of Machine Learning?
“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
The purpose of Machine Learning is to discover patterns in your data and then make predictions based on often complex patterns to answer business queries, detect and analyse trends and help solve problems. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.
Visual Representations of Machine Learning models
Components of ML Algorithms
1. Representation — Refers to a set of classifiers or the language that the computer understands.
2. Evaluation — Refers to the objective/scoring function
3. Optimization — Refers to the search method, often the highest scoring classifier
How do we make machines learn?
There are different strategies to make machines learn, from using basic decision trees to layers of artificial neural networks (which has given way to deep learning), depending on what task you are trying to accomplish and the type and amount of data that is available. This dynamic sees itself played out in applications varying from medical diagnostics to self-driving cars.
Usually, emphasis is placed on choosing the best learning algorithm. Listed below are some of the most commonly used algorithm types:
- Supervised Machine Learning algorithms
These algorithms can apply what has been learned in the past to new data using labelled examples to predict future events. A known dataset is analyzed and the learning algorithm produces an inferred function to make predictions about the output values. The system can provide objectives for any new input after sufficient training. This learning algorithm can also compare its output with the correct, intended output and find errors to revise the model accordingly.
2.Unsupervised Machine Learning algorithms
In contrast to Supervised Machine Learning algorithms, these algorithms are used when the information required to train is neither classified nor labelled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the correct output, but it explores the data and can draw inferences from datasets to describe unknown structures from unlabeled data.
3.Semi-supervised Machine Learning algorithms
These algorithms fall somewhere in between Supervised and Unsupervised learning since they use both labelled and unlabeled data for training — typically a small amount of labelled data and a large amount of unlabeled data. The systems that use this method can considerably improve learning accuracy. Usually, Semi-supervised learning is preferred when the acquired labelled data requires skilled and relevant resources to train it or learn from it. Otherwise, procuring unlabeled data usually doesn’t require additional resources.
4.Reinforcement Machine Learning algorithms
It is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most pertinent characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is called the reinforcement signal.
Challenges and Limitations
“Machine learning can’t get something from nothing…what it does is get more from less.” — Dr. Pedro Domingo, University of Washington
The two biggest problems in machine learning are:
(i) Overfitting — the model exhibits bias towards the training data and does not generalize to new data or variance i.e., it learns random things when trained on new data.
(ii) Dimensionality — algorithms with more features work in higher or multiple dimensions, making understanding the data more difficult.
One of the most common mistakes among Machine Learning beginners is testing training data successfully and having the delusion of success. When a learning algorithm is not working, usually the quickest path to success is to feed the machine more data. However, this can lead to issues with scalability, in which we have more data but the time to learn that data remains an issue.
In terms of purpose, machine learning is not an end or a solution in and of itself.
Some applications of ML
Machine Learning is used everywhere from automating mundane tasks to offering intelligent insights, industries in every sector try to benefit from it. You may already be using a device that utilizes it. For example, a wearable fitness tracker like Fitbit, or an intelligent home assistant like Google Home. But there are much more examples of ML in use.
1.Prediction
ML can be used in prediction systems. A clinical practitioner may use available data about a particular disease to train the machine to make predictions about the diagnosis or prognosis of patients. ML is trained to recognize cancerous tissues.
2.Image Recognition
ML can be used for face detection as well. With face detection, you can get the information you need to perform tasks like embellishing selfies and portraits or generating avatars from a user’s photo.
3.Speech Recognition
It is the translation of spoken words into text. It is used in voice searches and more. Voice user interfaces include voice dialing, call routing, and appliance control. It can also be used for simple data entry and the preparation of structured documents.
4.Financial Industry and Trading
Companies use ML in fraud investigations and credit checks.
Conclusion
So, as you might have seen now, Machine Learning is everywhere. From research and development to automation, ML has its branches laid out in every field. So now with ML in action, that day is not far away when machines learn and do everything in the world!
