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AI and Machine Self-Learning


Machine self-learning is a subfield of computer science related to the ability of computers to recognize patterns using AI without being programmed to do it. Its major goal is to identify how a computer can make data-driven predictions without reprogramming.

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While machine self-learning was out of the reach of the majority of businesses in the past, cloud computing has made it much more affordable. Since it allows extracting information out of raw data, it has become a perfect solution for complex business problems that cannot be solved by software engineering or human judgment.

Areas of Application

Machine learning is now applied in order to create defensible competitive advantages in the following areas:

  • Cognitive computing. This refers to the ways of doing business online, using pattern recognition, data mining, and natural language processing. The major purpose is to integrate intelligent computing seamlessly with the help of AI engines that make it possible to use face and emotion detection, visual recognition, and video analytics.
  • Personal assistants and chatbots. These technologies are meant to simulate interactions with real users. They learn from past patterns of conversation to offer a life-like interactive experience.
  • Internet of things. Data-driven cloud platforms made it possible to construct a seamless virtual environment using IoT, which is made intelligent with the help of machine learning.
  • Business intelligence. When cloud technology and machine learning were too expensive for the majority of businesses, organizations had to collect data connected with the habits of their clients manually and locally. Machine learning made it possible to find underlying patterns and eliminate the necessity of manual input.
  • Security and data hosting. Cybersecurity has become smarter with the introduction of machine learning since its complex algorithms easily detect anomalous patterns, allowing users to pinpoint intruding malware and prevent attacks before they damage the system. Data hosting is also affected by machine learning since it supports faster data flow.

Machine Learning and Cloud Computing

Cloud computing makes it possible to use machine learning pre-trained patterns in order to generate personalized tailored models. These services are scalable, fast, and easy to apply. Businesses use it to build large-scale, highly sophisticated regression models that are too demanding to rely on hardware. In the past, they used systems that required a huge amount of processing power in order to be effective. Nowadays, the same functions are easily performed by the cloud.

Over a certain period of time, machine learning techniques will become intelligent enough to be able to make decisions without any human presence whatsoever. Both users of the cloud and cloud providers generate new algorithms. The cloud has all the essential components for this. It features abundant storage, computational power, and a massive amount of data, which allows it to use scalable machine learning platforms.

Ways of Improvement

Although the concept of machine learning is rather old (it was first defined already in 1959), its major algorithm remains basically the same: The system analyzes the human feedback in order to calculate, which answer is the most probable. Due to the increasing popularity of machines coupled with cloud computing in business, the concept requires further development to provide more diversified and efficient algorithms.

Significant improvement can be achieved with changes introduced to problem definition and data. Data tactics include:

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  • Getting more data. If it is possible to obtain more data, machine self-learning techniques will demonstrate better performance.
  • Inventing more data. If is it impossible to get more data, it can be generated using a probabilistic model.
  • Cleaning data. Sometimes machine learning is hindered owing to missing or corrupt observations. They must be removed or fixed if possible in order to improve the quality of the input.
  • Resampling data. Using a smaller sample will help speed up the process and improve representation.
  • Reframing the problem. Changing the types of prediction may bring about better results.
  • Rescaling data. A lift of performance can also be achieved by normalization or standardization of input variables.
  • Transforming data. In order to better expose features in the data, it is necessary to reshape its distribution.
  • Projecting data. Lower dimensional space allows creating a compressed representation of the dataset.
  • Selecting features. Feature importance methods make it possible to identify whether all input variables are equally significant.
  • Engineering features. New data features can be created to signify important events.

Another method of leveraging cloud computing with the help of data sets is to improve the algorithms applied. First and foremost, it is essential to identify those data representations and algorithms that demonstrate performance above average. After that, it will be much easier to develop the most efficient combinations. The following tactics can be used to achieve this:

  • Resampling method. The user should identify what resampling strategy is applied and find out the configuration that allows the best application of available data.
  • Evaluation metric. Machine self-learning uses different metrics to evaluate the skill of predictions. Thus, it is necessary to select the one that best captures the peculiarities of the problem that has to be solved.
  • Baseline performance. It is better to use a zero rule or a random algorithm for establishing a baseline, which is required for being able to rank all evaluated algorithms.
  • Spot-checking linear algorithms. Since linear methods are frequently effective, fast to train, and easy to comprehend, they are preferable when one wants to achieve better results. A diverse suite of them should be evaluated to identify the best possible combination.
  • Spot-checking nonlinear algorithms. These algorithms are much more complex and require more data to work properly. That is why it is necessary to perform a thorough analysis to be able to evaluate their performance.
  • Borrowing methods from the literature on the topic. If the problem of machine learning is too difficult to solve, one must address the literature on the topic to get ideas concerning algorithm types or extensions of traditionally applied methods.
  • Changing standard configurations. Before deciding to change an algorithm, the user must give it an opportunity to show its best performance. For this purpose, he/she should try to change the configuration.

Combined techniques can also be rather effective:

  • Blending model predictions. Different algorithms can be applied simultaneously to make multiple models.
  • Blending data representations. Predictions can be combined from models that are trained on different projections of one and the same problem.
  • Blending data samples. The user can create subsamples of training data and create an effective algorithm.
  • Correcting predictions. Prediction errors should be eliminated to achieve better performance.


With the development of online operations, machine learning has become a focus of attention of a number of businesses since it allows personalizing data and achieving higher customer satisfaction. Coupled with cloud computing, it makes it possible for organizations to process huge amounts of information and make valuable predictions. Yet, despite its growing popularity, machine learning still uses old algorithms based on user feedback. It is now necessary to apply tactics to improve and update them in order to leverage cloud computing datasets.

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"AI and Machine Self-Learning." StudyCorgi, 29 Dec. 2021,

1. StudyCorgi. "AI and Machine Self-Learning." December 29, 2021.


StudyCorgi. "AI and Machine Self-Learning." December 29, 2021.


StudyCorgi. 2021. "AI and Machine Self-Learning." December 29, 2021.


StudyCorgi. (2021) 'AI and Machine Self-Learning'. 29 December.

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