What is machine learning?

In terms of technology machine learning is typically one of the fastest-growing edifices when it comes to machine operation, even though it takes a lot for one to understand specifically what machine learning is all about.

Machine learning deals with a lot of things and therefore can be applied to different (concepts and techniques).

For one to really understand what machine learning is then you must be familiar to Algorithms,model analysis and variables.Now let me beat it down for a better understanding of what a machine learning really means.

Difference between Artificial Intelligence, Machine learning, and deep learning

Machine learning

In a real sense this term deals with the enablement of a computer to carry out tasks without receiving explicit line by line instructions to do so, in other words, it goes beyond been given certain instructions before carrying out its activities. What we mean is that a specialist in machine learning doesn’t have to note down all the necessary steps to rectify an issue because the computer itself has the capability of learning by analysing patterns within the data and generalising these patterns to new data.

Three basic parts in machine learning

1) Algorithms

2) Inputs

3) Outputs

As for the inputs data,they are data that are stored in the machine learning system.The input data can be categorized into features and labels.

Labels/Features

These are description given to the personal instances of data while features are variables that are used in drawing conclusions and learning of patterns.These terms label and features can be used in two different types of machine learning issues which are supervised and unsupervised learning.Now let’s take a look at supervised learning.

Supervised learning: These inputted data is followed by the exact truth.The supervised learning issues have the exact output as part of the data-set,so the expected variables are already known. These gives the data scientist the possibility of checking the performances of the algorithms by testing the data on a testing data-set and seeing the percentage of items that were correctly classified.

Unsupervised learning: Issues on this option do not have the exact labels accorded to them,so therefore a machine learning algorithm that is trained to carry out unsupervised learning tasks should be able to know the relevant patterns in the data.

Machine Learning works in almost the same way with exact human learning. For example, if a child is shown images with specific objects on them, they can learn to identify and differentiate between them. So machine Learning works in the same way: Through data input and certain commands, the computer is enabled to “learn” in other to identify person’s or objects and to distinguish between them. For this reason, the software is supplied with data. For instance, the programmer can tell the system that a particular object is a human being (=”human”) and another object is not a human being (=”no human”). The software receives continuous results from the programmer. These results signals are used by the algorithm to adapt and optimize the model. With each new data set fed into the system, the model is further optimized so that it can clearly different  “humans” and “objects”.

Advantages of Machine Learning

 This without any atom of doubt helps people to work more creatively and efficiently. Basically, you too can delegate some complex or monotonous work to the computer through Machine Learning – starting with scanning, saving and filing paper documents such as invoices up to organizing and editing of images.

In addition to the aforementioned rather simple tasks, self-learning machines can also perform complex tasks. These include the detection of errors and patterns. This is a major advantage, especially in areas such as the manufacturing industry: the industry relies on continuous and error-free production.

While even experts often cannot be sure where and by which correlation a production error in a plant fleet arises, Machine Learning offers the possibility to identify the error early – this saves time and funds.

Self-learning programs are now also used in the medical field. In the future, after “consuming” huge amounts of data (medical publications, studies, etc.), apps will be able to warn in case his doctor wants to prescribe a drug that he cannot tolerate.

This “knowledge” also means that the app can propose alternative options which for example also take into account the genetic requirements of the respective patient.

These methods are used in Machine Learning

In Machine Learning, statistical and mathematical methods are used to learn from data sets. Dozens of different methods exist for this, whereby a general distinction can be made between two systems, namely symbolic approaches on the one hand and sub-symbolic approaches on the other. While symbolic systems are, for example, propositional systems in which the knowledge content, i.e. the induced rules and the examples are explicitly represented, sub-symbolic systems are artificial neuronal networks. These work on the principle of the human brain, whereby the knowledge contents are perfectly stated.

The different types of Machine Learning that we have

For instance the, algorithms play an important role in Machine Learning: but on the other hand, they are responsible for recognizing patterns and they can generate solutions. Algorithms can be divided into different categories namely:

Supervised learning: In terms of supervised learning the course of monitored learning e.g models have refined ahead just to ensure adequate allocation of the information to the respective model groups of the algorithms, these then have to be specified. In other words, the system learns on the basis of given input and output pairs. In the course of monitored learning, a programmer, who acts as a kind of teacher, provides the appropriate values for a particular input. The aim is to train the system in the context of successive calculations with different inputs and outputs and to establish their connection in different aspects.

Unsupervised learning: In unsupervised learning, artificial intelligence learns without already defined target values and without rewards. It is mainly used for learning clustering. The machine tries to structure and sort the data opted in according to certain namings.E.g a machine could learn that coins of different colours combinations can be sorted according to the characteristic “colour” in order for them to be restructured.

Partially supervised learning: Partially supervised learning is the addition of supervised and unsupervised learning coming together as one.

Reinforcing learning: like in Skinner’s classical conditioning which is based on rewards and punishments. The algorithm is taught by either a positive or negative interaction which reacts to a certain situation if it should occur.

Active learning: Within the framework of active learning, an algorithm is given the opportunity to ask results for specific input data on the basis of already defined questions that are considered important. Usually, the algorithm itself chooses questions with high importance.

In general, the data basis can be either offline or online, depending on the corresponding system. In addition, it can be available only once or repeatedly for Machine Learning. Another distinguishing feature is the either staggered development of the input and output pairs or their simultaneous presence. On the basis of this aspect, a distinction is made between so-called sequential learning and so-called batch learning.

Another application of Machine Learning that is collectively integrated into everyday life is the automatic detection of spam that is integrated into almost all e-mail programs. Within the scope of spam detection, the data contained in the e-mails are analysed and categorised.

 The “spam” and “non-spam” patterns are used in this aspect. If an e-mail is recognized as junk mail, the program learns to identify spam mails even more efficiently. Other areas of application for Machine Learning include search engine ranking, combating cybercrime and preventing computer attacks.

For example a programmer uses machine learning to beat the popular Game Super Mario World:

The commercial application of Machine Learning

With the help of Machine Learning, economic data can be turned into monetary funds. Companies that rely on Machine Learning are not only able to increase the satisfaction of their customers but also reduces cost rate at the same time.

 Through Machine Learning, customer wishes and needs can be evaluated and the following marketing measures can be personalized. This optimizes the customer experience and increases customer belief’s.

In addition, Machine Learning can also help companies to find out whether there is a threat of customer migration in future but presently occurrences.

Importance of machine learning

Machine learning has different application methods that can enhance the real business result,for example Money savings and time….this has the capability to improve the future of any organization. In discussing this we particularly look at some tremendous impact Happening within the customer care industry while machine learning is enabling individuals to get things done more easily and efficiently.

Through the visual assistant initiative, machine learning automates different tasks that need to be done by a live agent, this includes Changing of password and checking of account balances. This enables them to have more time to focus well on the kind of customer care that humans best performs.

Deep Learning

This is a machine learning method that lectures computers to do what comes to humans naturally, Instance a deep Learning is a key technology behind driveless cars, enabling them to recognize a stop sign or the ability to know the difference between a lamppost and a pedestrian. It is also the key to voice control in consumer devices like phones, tablets,handsfree speaker’s,TVs e.t.c. Deep learning is getting lots of attention lately and for good reason. It’s the process of achieving results that were impossible in the initial.

In deep learning,a computer model learns to perform different classification tasks directly from sound, Tex, images etcetera. Deep learning models can also achieve an art state accuracy, opting out errors and sometimes exceeding human-level performances. Models are trained by using a large set of labelled data and neural network architectures that consist of many layers.

Importance of Deep learning

Talking about the importance inaccuracy at higher levels than now. It assists consumers electronically to meet users expectations,it is also crucial for safety critical applications like driveless cars. in recent advancement deep learning have really improved to the point where deep learning outshines humans in terms of performances in carrying out various tasks like naming and differentiating the images of objects. Deep learning was first theorized in the early 1980s in old times.

Uses of Deep Learning

1) It requires enough computing power in terms of high performances GPUS having a parallel architecture that corresponds with deep learning. When merged with clusters or clouds computing,this enables production teams to reduce the time of training.A deep learning network from hours to either weeks or even less than that depending on the time factor.

2) In deep learning a large amount of labelled data is required,for example  an undriven car development needs thousand hours of videos and millions of images to utilize it’s function.

Different examples of Deep Learning

1) Medical research

2) Electronics

3) Automated driving

4) Industrial Automation

5) Aerospace and Defense.

Medical research: In terms of medical research cancer researchers use deep learning to detect cancer cells. UCLA teams built an advanced microscope that yields a high dimensional data set used to train a deep learning application in other to identify cancer cells accurately.

Electronics: Deep learning aids in the assistance of automated hearing and translation of speech e.g Home application devices that responds to voices and detects preferences is as a result of deep learning utilization.

Automated driving: This is a process whereby automotive finders uses deep learning to automatically detects objects such as traffic lights and stop signs.Also when it comes to reduction in accidental occurrences we use deep learning to detect pedestrians.

Industrial Automation: In this case deep learning helps to improve the safety of workers around heavy machinery objects by detecting automatically if people are within an unsafe distance of the machines or if they are closeby.

Aerospace and Defense: Deep learning is used to identify satellite objects in locating different areas of interests and to identify unsafe or safe areas of contingents.

Artificial intelligence (AI): Deals with the replica of human knowledge processed by machines precisely in computer systems. These processes include learning “to acquire rules and information”, reasoning by “applying rules to obtain a valid conclusion and correction of one-self. The particular application of Artificial intelligence includes speech recognition, machine version and expert systems.

Artificial intelligence is based on two categories viz Strong and weak AI.

Strong AI: Also referred to as an AI system with human comprehension abilities when shown with an unknown task. it’s believed that a strong Ai system can profer a solution without any human assistance.

Weak AI: These category of AI also referred to as a narrow AI is (intrinsically),(device) and (skilled), particularly for a certain task e.g virtual personal assistant such as Apple’s Siri, are an example of a weak AI.

The different types of Artificial intelligence

1)Theory of the mind.

2)Self awareness.

3) Reactive machines.

4) Limited memory.

Theory of the mind:In this      term we likely deduce an understanding which others have as their own desires beliefs and    that makes impacts in the choices which they made,it’s not in

Self awareness: It have a reason of self consciousness.Its a machine with inward-awareness which knows their current state and uses the gathered information to interfere in the feeling of others.This also is barely in existence.

Reactive machines:

This is the most basic type of AI system that are purely reactive, and have the ability neither to form memories or to use past experiences to inform current decisions. Deep Blue, IBM’s chess-playing supercomputer, which beat international grandmaster Garry Kasparov in the late 1990s, is the perfect example of this type of machine.

Limited memory: This type of memory goes a long way by using past or old occurrences in effecting future choices. Some of the choice-making function in self driven cars are designated using this way.These are notables in form of actions in early time to come (nearest future) and this observation are permanently not kept.

Various areas where AI can be applied

1)AI in education

2)AI in law

3)AI in finance

4)AI in production

5)AI in business

6)AI in healthcare

1)AI in education: This helps students in the education sector in providing their needs, grading them, rendering adequate supports, ensuring they are on the right track. Automated jobs Among academicians e.t.c.

2)AI in law: This deals with a more efficient use of time whereby startups building questions and answers in computer Assistants and can also sift programs to answer questions in any database that are associated with the examination of the ontology and taxonomy.

3)AI in finance:

4)AI in production: Beyond every reasonable doubt the production sector is leading the way when it comes to the application of Artificial intelligence technology. Through this medium producers are applying Ai-powered  data to improve efficiency, safety of the employees and quality of the products.

5)AI in business: It’s popularly recognized by all AI in business application include neural language, automation and analysation of data.

6)AI in healthcare: Complex algorithm and software play an important role here when used in the emulation of human comprehensiveness in the analysis of difficult or complicated medical data inotherwords AI in healthcare is the process whereby computers algorithms have the capability to approximate conclusions without the direct input of humans.

Conclusion

We advice you understand both sect before going for the one that best suits your knowledge inother to have ideas on how they are being used….in differentiating them now first let’s go for

Machines learning which we deduce as a process whereby a computer acts on its own without programming while Deep learning is reffered to as the subset of machine learning that in a very simple term can be thought of as the automation of predictive analytics……. lastly An Ai deals with the simulation of human knowledge processed by machines precisely computer systems

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