Over recent years, we have seen a strong impact on Google Machine Learning, as an explanation for this could be the high volume of data generated by applications.The boost in computing power over the last few years and the development of future algorithms have significantly improved the growth rate. Machine learning algorithms use different computational methods to learn information directly from data without relying on a standard equation as a model. With its algorithms allow computers to learn and improve from data without being specifically programmed.
Google Machine Learning can be applied anywhere to provide intelligence to different types of tasks as well as the automation of different sectors. Intelligent home assistant, wearable health equipment is the best example of machine learning devices in use. Machine learning is commonly used in fields such as prediction systems, image recognition, speech recognition, medical diagnosis, financial industry, and trading, etc.
Use of Machine Learning
Machine Learning generally use two types of techniques, that is supervised learning and unsupervised learning. In supervised learning trains model, input and output data can predict the future output. Data scientists decide which variables, or attributes, the model should be evaluated and used to create predictions. After the training process has been completed, the algorithm can quickly update newly learned information.
Where in unsupervised learning will find hidden patterns or intrinsic structures in input data. Unsupervised algorithms should not have to be programmed with the desired output data. Alternatively, they use an adaptive technique called deep learning to analyse data and reach a conclusion. It has neural networks that are used to perform complex processes than supervised learning, such as image recognition, speech-to-text and the generation of natural languages.
ML Examples
Today’s world depends largely on applications of machine learning. Instagram’s news feed is one of the best examples. Each member’s feed is customised using machine learning if the user keeps scrolling to read a particular friend’s feed, then the new feed delivers more of that friend’s feed activity. The methodology behind is simple, using statistical analysis and predictive analysis, it will recognise the trend of user data and produce the related news feeds. And based on user behaviour, it changes and produces news feeds accordingly.
It is also used in enterprise applications, customer relationship management systems to evaluate and respond to relevant and important emails or messages. With the advanced version of the machine learning software, Business Intelligence and Analytics are used to automatically identify potentially important data points. The use of HR system learning models will easily identify the features of employees and identify the best candidates to open positions. The leading role of machine learning is in self-driving cars. Neural networks are used to identify different objects and to decide which action is required for safe driving on the roads.
Machine learning algorithm
As an emerging technology, there is no shortage of machine learning algorithms. As normal, there are fairly simple to very complex algorithms available. Let’s discuss a few widely used ones.
Supervised learning algorithms:
Attempts to create relationships and dependencies between the performance of outcome prediction and input features. This helps to estimate the output quality of new data based on previous data sets.
- Nearest Neighbour
- Naive Bayes
- Decision Trees
- Linear Regression
- Support Vector Machines (SVM)
- Neural Networks
Unsupervised Learning algorithms:
The format is intended to use strategies to summarise and group data points which help to provide useful feedback and better data for users.
- k-means clustering
- Association Rules
Reinforcement Learning algorithms:
This is a branch of Artificial Intelligence, helps machines and software agents to automatically find the best behavior based on the context to improve the performance of the system. In this approach, the agent continuously learns through an iterative process. As an agent comes to know and learn from it to obtain the full potential states. Many implementations of reinforcement learning algorithms include computer-played board games like Chess robotic arms, and self-driving cars, etc.
- Q-Learning
- Temporal Difference (TD)
- Deep Adversarial Networks
Future of Machine Learning
Future for Machine learning has increased a great deal with the rise of artificial intelligence (AI) in IT Infrastructure Management. Advanced AI applications are powered by deep learning models.The google machine learning revolution would stay with us for a long time. The up-coming phenomenon will be the competitive advantage for MNC to be on the top based on machine learning.In the coming days, innovations such as Fine-Tuned Customisation, Improved Search Engine Experiences, Development of Data Teams, No-Code Environments and Growth of Quantum Computing will be the leading trends in machine learning.
