Machine learning is a fiedl of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.

Machine learning algorithms are used in a wide variety of applications, such as search engine algorithms, self driving cars, speech recognition, and computer vision where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

Machine Learning vs. Deep Learning vs. Neural Networks

Machine learning, deep learning, and neural networks are all sub-fields of artificial intelligence. However, deep learning is actually a sub-field of machine learning, and neural networks is a sub-field of deep learning.

Deep learning and machine learning differ in how each algorithm learns. Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required and enabling the data sets. In a way, Deep Learning is scaled up type of Machine Learning

Neural networks, or artificial neural networks (ANNs), are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network. The “deep” in deep learning is just referring to the depth of layers in a neural network.

How does Machine Learning work

For simplification we can bread down Machine Learning algorithm into three main parts:

• A Decision Process In general, machine learning algorithms are used to make a prediction or classification. Based on some input data, which can be labelled or unlabeled, your algorithm will produce an estimate about a pattern in the data;
• An Error Function An error function serves to evaluate the prediction of the model. If there are known examples, an error function can make a comparison to assess the accuracy of the model;
• An Model Optimization Process If the model can fit better to the data points in the training set, then weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will repeat this evaluate and optimize process, updating weights autonomously until a threshold of accuracy has been met.

Real-world machine learning use cases

Examples of Machine Learning can be found in many systems we use:

Speech recognition

Automatic Speech Recognition (ASR) or more common referred as speech-to-text, is the capability which uses natural language processing (NLP) to process human speech into a written format. Many mobile devices incorporate speech recognition into their systems to conduct voice search, e.g. Siri, or provide more accessibility around texting.

Online Customer service

Online chatbots are replacing human agents along the customer journey. They answer frequently asked questions (FAQs) around topics, like shipping, or provide personalized advice, cross-selling products or suggesting sizes for users, changing the way we think about customer engagement across websites and social media platforms. Examples include messaging bots on e-commerce sites with virtual agents, messaging apps, such as Slack and Facebook Messenger, and tasks usually done by virtual assistants and voice assistants.

Computer vision

This AI technology enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and based on those inputs, it can take action. This ability to provide recommendations distinguishes it from image recognition tasks. Powered by convolutional neural networks, computer vision has applications within photo tagging in social media, radiology imaging in healthcare, and self-driving cars within the automotive industry.

Recommendation engines

Using past consumption behavior data, AI algorithms can help to discover data trends that can be used to develop more effective cross-selling strategies. This is used to make relevant add-on recommendations to customers during the checkout process for online retailers.

Automated stock trading

Designed to optimize stock portfolios, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention.

Content writing

Machine learning models such as GPT-3 can be used to autimatixcally generate content for blog posts, articles, and other forms of content. An AI content generator can produce various forms of text, based on the user's input. Machine learning models are trained by accessing parts of the internet and can, by using statisticals algorithms, generate content that is most relevant to the user's input.

Medical diagnostics

AI technologies are making great strides in medical imaging. AI programs are can enable earlier disease detection, while also enhancing the workflows by accelerating reading time and automatically prioritizing urgent cases. By accessing vas quantities of medical information, which has already been validated, Machine learning algorithms can detect compex platterns that are impossible to be predicted by humans, helping earlier disease detection.