“Netflix saved $1 billion this year as a result of its machine learning algorithm”
The Machine Learning algorithm used by Netflix allows it to recommend personalized TV shows and movies to subscribers.
What is Machine Learning?
Machine learning is an application of Artificial Intelligence (AI) that enables systems to automatically learn and improve from experience without human intervention through manual programming. Much like future-forward movies in the 90s, machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
In machine learning, computers learn through observations or data, such as examples, direct experience, or instruction. They use this information to extract patterns in data and make better decisions in the future based on the examples that are provided. The main aim is to allow computers to learn automatically without any human intervention or assistance and to adapt accordingly.
So how does ML differ from AI?
To put it simply, Machine Learning is a branch of Artificial Intelligence, whereas AI deals with the broader concept of machines being able to carry out tasks in a way that we would consider “smart”. Machine Learning is a current application of AI, based around the concept that we should really just be able to give machines access to data and let them learn for themselves.
One of the core concepts of ML as a part of AI, is that rather than teaching computers everything they need to know about the world and how to carry out tasks, it might be possible to teach them to learn for themselves. The emergence of the internet led to a huge increase in the amount of digital information being generated, stored, and made available for analysis.
“AutoML, Google’s AI that helps the company create other AIs for new projects, learned to replicate itself in October of 2017”
What this means is that essentially, Artificial Intelligence is better at ML than humans.
FAQs about Machine Learning
What is deep learning and how is it different from machine learning?
Deep learning, also known as deep neural networks are set algorithms inspired by working principals of the human brain where it learns to identify patterns in data for decision making.
Deep learning is a subfield of representation learning, which in fact, is a subfield of machine learning.
What are the different types of Machine learning algorithms?
Typically, there are 4 types of Machine Learning:
- Supervised algorithms: Set of algorithms to learn from labelled data, e.g. images labelled with whether a human face exists in an image or not.
- Non-supervised algorithms: Set of algorithms to learn from data without labels or classes, e.g. set of images given to group similar images.
- Semi-supervised algorithms: algorithms that fall somewhere between above and uses both labelled and non-labelled data.
- Reinforcement learning algorithms: Set of algorithms to learn best actions to take given a current scenario that maximizes overall reward.
Why has Machine Learning or Cognitive Computing become such a hot topic?
The reason for the increasing interest is due to the significant increase in data that is now available, which makes machine learning more relevant, accurate and more effective for more businesses than ever before. Machine Learning automates decisions by analysing large and diverse datasets at lightning speeds, predicting what would lead to a positive outcome and making or taking the recommended action.
What are the potential benefits of creating machines that can be programmed to learn?
How does ML learn from data?
The training data is composed of two parts: features and labels. Using models (usually the classifier is named after the statistical model it uses, but some models don’t use statistical
models, like neural nets,), the classifier learns what features (or combinations of features) are
associated with which labels.
methods of Machine Learning
A look at some methods of Machine Learning
Machine learning algorithms are often categorized as supervised or unsupervised
Supervised machine learning algorithms
Supervised machine learning algorithms can apply what has been learned in the past to new data using labelled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
Unsupervised machine learning algorithms
Unline supervise algorithms, unsupervised machine learning algorithms are used when the information used 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 right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
Semi-supervised machine learning algorithms
Semi-supervised machine learning 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 are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labelled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.
Reinforcement machine learning algorithms
Reinforcement machine learning algorithms 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 relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behaviour within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.
The ngX Framework
Based on our deep design and technical experience across industries we’ve developed a proprietary digital framework, the ngX framework, which is leveraged across all our projects.
The ngX framework consists of the following steps:
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