Understanding Machine Learning
Ons het die term 'masjienleer' gehoor,,en,Dit bestaan uit verskillende algoritmes soos neurale netwerke,,en,besluit bome,,en,Bayesiese netwerke ens,,en,Masjienleer gebruik hierdie algoritmes om uit data te leer en verborge insigte uit data te herwin,,en,Die leerproses is iteratief,,en,dus word die nuwe data ook sonder toesig hanteer,,en,Die wetenskap,,en,om uit vorige data te leer en dit vir toekomstige data te gebruik, is nie nuut nie,,en,maar dit raak deesdae meer gewild,,en,Wat is masjienleer,,en,Sommige mense glo dit,,en,is nie beter as die ou metode van rekenaarprogrammering wat ons nou nog gebruik nie,,en,baie beskou dit as 'n rewolusie op die gebied van,,en,Hulle glo dat die gebruik van hierdie tegnologie,,en,masjiene sal dinge kan leer en dinge met hul eie ervaring kan doen,,en,eerder as om die menslike instruksies stomme te volg,,en’ in different discussions and forums, but what does it exactly mean? Machine learning can be defined as a method for data analysis, based on pattern recognition and computational learning. It comprises of different algorithms like neural networks, decision trees, Bayesian networks etc. Machine learning uses these algorithms to learn from data and recover hidden insights from data. The learning process is iterative, so the new data is also handled without any supervision. The science (machine learning) to learn from previous data and use it for future data is not new, but it is gaining more popularity nowadays.
What is machine learning?
By definition from IBM – “Machine learning is a branch 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.”
While some people believe that machine learning is no better than the old method of computer programming that we still use now, many consider it as a revolution the field of artificial intelligence. They believe that using this technology, machines will be able to learn things and do things with their own experience, rather than dumbly follow human instructions.
Om meer te verstaan oor die betekenis van masjienleer,,en,ons kan dit vergelyk met normale rekenaarprogrammering,,en,Die volgende gedeeltes sal meer oor die masjienleer en die verskil daarvan van tradisionele programmering bespreek,,en,Wat is tradisionele programmering,,en,As ons 'n rekenaar programmeer,,en,ons gee dit eintlik in 'n taal wat dit verstaan,,en,wanneer ons dit insette lewer,,en,dit lewer 'n redelike weergawe gebaseer op die instruksies wat ons daaraan gegee het,,en,kom ons dink dat u 'n inset gegee het om aansoek te doen vir 'n kredietkaart,,en,Terwyl u u insette verwerk,,en,die stelsel sal na al die belangrike dele van u aansoek kyk,,en,neem die nodige inligting en verwerk dit,,en,dit sal die resultaat van aanvaarding of verwerping lewer op grond van die program wat daarvoor gevoer is,,en, we can compare it to normal computer programming. The following sections will discuss more about the machine learning and its difference from traditional programming.
Must Read – Types of Artificial Intelligence – Let’s Explore
What is traditional programming?
When we program a computer, we actually give directions to it in a language that it understands. Then, when we give it an input, it gives a reasonable output based on the instructions that we have given to it.
Now, let’s imagine that you have given an input to apply for a credit card. While processing your input, the system will look at all the important parts of your application, take the necessary information and process it. After that, it’ll produce the output of acceptance or rejection based on the program that was fed to it.
Must Read – Data Science and R – Let’s Explore
Hoe masjienleer anders is,,en,As u gebruik,,en,in die plek van tradisionele programmeringsmetodes in die kredietkaartscenario,,en,dan sou die resultaat ietwat anders gewees het,,en,Die resultaat sal eintlik gebaseer wees op die invoerdata en die stelsel sal ervaring opdoen deur die invoerdata te verwerk,,en,Daar sal geen spesiale program daarvoor wees nie,,en,Aangesien dit al hoe meer ervaring opdoen,,en,sy prestasie sal mettertyd beter word,,en,masjienleer is eintlik deel van AI of,,en,Dit leer deur die groot hoeveelheid datalêers wat met elke gebruik van die stelsel gemaak word, te ontleed,,en,Die data sal ontleed word,,en,dit sal die programmering verander volgens nuwe vereistes,,en,Dit lei ook tot verbetering in die akkuraatheid daarvan,,en,Ons kan ook sê dat masjienleer soos 'n lineêre regressie is,,en?
If you use machine learning in the place of traditional programming methods in the credit card scenario, then the result would have been somewhat different. The result will actually be based on the input data and the system will gain experience by processing that input data. There won’t be any special program for it. As it’ll gain more and more experience, its performance will get better with time.
So, actually machine learning is a part of AI or Artificial Intelligence. It learns by analyzing the large quantity of data files made with each usage of the system. As it’ll analyse the data, it’ll change its programming according to newer demands. This leads to improvement in its accuracy too. We can also say that machine learning is like a linear regression, waar die veranderlikes en parameters verander word om beter by die gegewe inset te pas,,en,Wat is die gewilde masjienleermetodes?,,en,Die gewildste metodes vir masjienleer is leermetodes wat nie onder toesig is nie,,en,die metode word gereeld gebruik,,en,word onder toesig gehou en,,en,persent word nie onder toesig gehou nie,,en,In baie gevalle word ook semi-toesighoudende en versterkingsleer gebruik,,en,Toesig oor leer,,en,die algoritmes word bygevoeg met benoemde voorbeelde,,en,waar gemerkte data beteken dat die data 'n beskrywing kry,,en,masjienleerstelsel,,en,sal beide insette sowel as die ooreenstemmende uitsette ontvang,,en,die stelsel kan meer ervaring opdoen deur die uitsette met die regte uitsette te vergelyk om die foute te vind,,en,Nadat u die uitsette ontleed het en die foute uitgevind het,,en,die stelsel sal die programmering dienooreenkomstig verander,,en.
Must read –Major Benefits of Artificial Intelligence in Education
What are the popular machine learning methods?
The most popular methods of machine learning are unsupervised and supervised learning methods. Among these, the supervised method is most commonly used. Oor 70% is supervised and 10-20 percent is unsupervised. Semi-supervised and reinforcement learning is also used in many cases.
Must read- Convolutional Neural Network (CNN) & its implementation in Python
Supervised learning
In this method, the algorithms are included with labelled examples, where labelled data means that the data is given a description. The machine learning system will receive both some inputs and their corresponding outputs. Now, the system can get more experience by comparing the outputs with the correct outputs to find the errors.
After analyzing the outputs and finding out the errors, the system will change its programming accordingly. Die stelsel kan verskillende metodes gebruik om die etiket op ongemerkte data te voorspel,,en,Hierdie metode word gebruik om toekomstige voorspellings op grond van data uit die verlede te doen,,en,Semi-begeleide leer,,en,Hierdie soort masjienleermetode word op soortgelyke plekke gebruik,,en,maar dit gebruik ook ongemerkte data tydens opleiding,,en,dit werk met ongemerkte data meer as gemerkte gegewens,,en,maar dit gebruik ook die gemerkte,,en,Dit is omdat ongemerkte data maklik versamel kan word,,en,ongemerkte data verwys na die gegewens wat hoegenaamd nie 'n beskrywing bevat nie,,en,Hierdie metode het ook dieselfde faktore van leer,,en,klassifikasie en regressie,,en,Dit is die beste wanneer die koste daaraan verbonde is,,en,toesighoudende leer,,en,is te hoog,,en,Onbewaakte leer,,en,Hierdie metode word gebruik vir die data wat glad nie oor etikette beskik nie,,en,ongeëtiketteerde data,,en. This method is used to do future event predictions based on past data.
Also read –Apache olifanten drijver en masjienleer
Semi-supervised learning
This kind of machine learning method is used in similar places, but it also uses unlabeled data while training. gewoonlik, it works with unlabeled data more than labelled ones, but it uses the labelled ones too. This is because unlabelled data can be gathered easily. Here, unlabelled data refers to those data which lack any description at all.
This method also has the same factors of learning, d.w.z. voorspelling, classification and regression. This is the best when the cost involved with supervised learning is too high.
Must read –Learn Advantages and Disadvantages Of Artificial Intelligence
Unsupervised learning
This method is used for those data which doesn’t have any labels at all, d.w.z. unlabelled data. Thus, die stelsel weet niks van die regte afvoer nie,,en,die algoritme moet self die korrekte uitvoer bepaal,,en,Dit kan gedoen word deur strukture binne die data te vind,,en,Hierdie tipe masjienleermetode is ideaal vir transaksiedata,,en,Die faktore om hier te leer is die kartering van naaste en selforganiserende kartering,,en,saam met enkelvoudige waarde-ontbinding en k-middel clustering,,en,Versterking leer,,en,Hierdie masjienleermetode word op plekke soos speletjies gebruik,,en,voertuignavigasie en robotika,,en,die stelsel leer volgens 'n toets- en foutmetode,,en,Die hoofdoel van hierdie metode is om die uitset in die minste tyd uit te vind,,en,wat gedoen kan word deur 'n toepaslike beleid te volg,,en,Die drie komponente in hierdie leermetode is die agent wat die omgewing leer om mee te kommunikeer en die aksies om te doen,,en, the algorithm has to figure out the correct output itself. It can do this by finding some structures within the data. This type of machine learning method is perfect for transactional data. The factors of learning here are nearest-neighbour and self-organizing mapping, along with singular value decomposition and k-means clustering.
Reinforcement learning
This machine learning method is used in places like gaming, vehicle navigation and robotics. In this method, the system learns by a trial and error method. The main goal in this method is to find out the output in the least time, which can be done by following a suitable policy. The three components in this learning method are the agent which learns the environment to interact with and the actions to do.
Also read- Drone Cops: The Future of Police Surveillance
Wat is die belangrikheid van masjienleer,,en,Die belangrikheid van masjienleer groei dag vir dag vanweë dieselfde redes wat die konsep van data-ontginning so belangrik gemaak het,,en,Hierdie redes sluit in goedkoop en kragtige rekenaarkrag,,en,groot hoeveelhede data word elke dag geskep en goedkoop en hoëkapasiteitsmetode vir data-opberging,,en,Hierdie faktore verseker dat kragtige en akkurate modelle baie vinnig gemaak kan word,,en,wat gebruik kan word in die ontleding van grootdatareserwes in grootmaat,,en,om beter te word,,en,baie akkurate uitsette,,en,Dit beteken dat beter modelle gebruik kan word om minder korporatiewe en sakebesluite op minder tyd te neem,,en,en dit ook sonder enige menslike werk,,en,Een manier om sulke akkurate modelle vinnig te maak, is outomatiese modellevervaardiging,,en?
The importance of machine learning is growing day by day due to the same reasons that have made the concept of data mining so important. These reasons include cheap and powerful computational power, large amounts of data being created every day and inexpensive and high-capacity data storage methods. These factors ensure that powerful and accurate models can be made very quickly, which can be used in analysis big data reserves in bulks, in order to get better, highly accurate outputs. This means that better models can be used for making better corporate and business decisions at lesser time, and that too without any human work.
One way to make such accurate models quickly is automated model making. Also, hierdie model moet dinamies genoeg wees om tred te hou met die veranderende tye,,en,Terwyl die mens twee modelle per week kan skep,,en,masjienleer kan duisende sulke akkurate modelle skep deur data vinnig te ontleed,,en,daarom is masjienleer deesdae so belangrik vir ondernemings en ander velde,,en,Wat is die masjienleer-algoritmes en -prosesse?,,en,Masjienleer-algoritmes help om die meeste uit big data te put,,en,deur die stelsel te help om die data vinnig te ontleed en akkurate resultate te lewer,,en,Hierdie algoritmes help om 'n model te skep wat gebruik kan word om besluite te neem,,en,Sommige algoritmesoorte is neurale netwerke,,en,ewekansige woude,,en,k-beteken groepering,,en,selforganiserende kaarte en kartering van naaste buurman,,en,om net algoritmes te gebruik, is nie alles nie,,en,Die beste model kan slegs gemaak word deur 'n geskikte proses te volg,,en. While humans can create two models a week, machine learning can create thousands of such accurate models by analyzing data quickly. So, that is why machine learning is so important for businesses and other fields nowadays.
Also read – Exploring The Future of Artificial intelligence
What are the machine learning algorithms and processes?
Machine learning algorithms help in getting the most out of big data, by helping the system to quickly analyse the data and produce accurate results. These algorithms help in creating a model which can be used for making business decisions. Some algorithm types are neural networks, random forests, k-means clustering, self-organizing maps and nearest-neighbour mapping.
However, using just algorithms is not all. The best model can be only made by following a suitable process. Voorbeelde van sulke prosesse is uitgebreide databestuur,,en,interaktiewe verkenning van data en visualisering van die resultate wat deur die model gevind is,,en,Wat is die impak van masjienleer op besigheid?,,en,Die impak van masjienleer op besigheid is geweldig,,en,Masjienleer het nuwe moontlikhede vir ondernemings geopen,,en,Dit het gehelp met die maak van akkurate modelle wat vinnig help om beter en slimmer besluite te neem,,en,In die volgende afdeling word enkele gebruike van masjienleer genoem,,en,Masjienleer word deesdae op baie plekke gebruik,,en,Kom ons kyk na sommige van hulle en wat hulle regtig doen,,en,Jy belowe,,es,mediese sagteware of 'n “desktop doctor”,,en, interactive exploration of data and visualization of the results found by the model.
What is the impact of machine learning on business?
The impact of machine learning on business is tremendous. Machine learning has opened new possibilities for businesses. It has helped in accurate model making which have in turn helped in making better and smarter decisions quickly.
Some uses of machine learning have been mentioned in the next section.
Some practical use cases
Machine learning is being used in many places these days. Let’s have a look at some of them and what they actually do.
Promedas, medical software or a “desktop doctor”, is 'n masjienleerprogram wat honderde jare mediese kennis gebruik om die dokters te help om die siekte op te spoor,,en,Amazon het die toekenning en herroeping van werknemers vir die werknemer geoutomatiseer deur middel van 'n rekenaaralgoritme wat die werknemer se toegang tot hulpbronne kan voorspel,,en,Masjienleer-algoritmes word ook deur die Cornell Universiteit gebruik om walvisse in die oseaan op te spoor deur klankopnames,,en,sodat skepe hulle kan vermy,,en,Soos data dag vir dag groei,,en,die belangrikheid van effektiewe verwerking neem ook toe,,en,masjienleermetodes is ontwerp,,en,wat die stelsel help om slim besluite te neem,,en,byna sonder die betrokkenheid van mense,,en,Die huidige impak van hierdie tegnologie op die IT-sektor was geweldig,,en.
Also, Amazon has automated its employee access granting and revocation through a computer algorithm which can predict the resource access for each employee.
Machine learning algorithms are also being used by Cornell University to detect whales in the ocean through sound recordings, so that ships can avoid them.
Must read – How Artificial Intelligence can help in recruitment process?
Gevolgtrekking
As data is growing day by day, the importance of effective processing is also growing. For this, machine learning methods have been devised, which helps the system in making smart decisions, almost without the involvement of humans. The present impact of this technology on the IT sector has been tremendous, daarom is dit maklik om voor te stel dat dit ook in die toekoms baie belangrik sal wees,,en,techalpine.com/understanding-machine-learning,,en.
