What is machine learning(ml)?

    What is Machine learning?

Machine learning is a type of method in which data analysis is done by automatically building a model. Machine learning is a branch of artificial intelligence if you want to know detailed information about Artificial intelligence you can click on it. Machine learning is based on the idea that a particular system can acquire data and learn from that data which will help identify certain patterns and help in making decisions without or minimum human efforts.  

Evolution of machine learning

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As technology is evolving as we evolve, machine learning has also revolutionized all these days. Now it is totally different from machine learning in the past. It was invented or created from pattern recognition and from the theory that computers can learn without being programmed to perform a specific task. 

The scientist and researchers interested in artificial intelligence were eager to see if computers could acquire knowledge from data and use it in the right way. 

The important aspect of machine learning is that they could handle situations individually from the data they acquire and find a solution using the previous same sort of problems and also could make complex decisions and solutions. As machine learning has been evolved they have now gained new and fresh momentum.

Application of Machine learning

China has also used the machine learning concept in their recent Rocket launch and made history by landing the rover on the Martian surface. the rover is made with the machine learning concept.

• Image recognition

Image recognition is one of the most common applications of machine learning it is used to identify objects persons places digital images photos and the popular use case of image recognition and face detection is automatic friend suggestion tagging the popular social media Facebook provides a feature of auto friend attacking suggestion whenever we upload a photo with our Facebook friends then we automatically get a tagging suggestion with name and Technology behind this is machine learning face detection and recognition algorithm

• Speech recognition

It is also one of the useful features of machine learning for instance when we use Google or other search engines we have an option of search using voice this feature is operated by machine learning and almost the whole world uses this feature.

Searching through voice mode is a popular feature among people where they control their mobile using voice and give commands using their voice to make smartphones work in their own way.

• Future traffic prediction

We always use google maps to find a way for our destination. most of the time Google provides the best and traffic-reduced route for reaching our destination fast. How this is possible? It is also a feature of machine learning. Using the sensors the machine can find the real-time traffic present in that area and also that the average time taken for the vehicles in this area to arrive at the destination in the past. It shows a sub-division in the type of traffic in that area. For instance, whether the traffic is slow-moving, all cleared or jammed or restricted place, etc.

• Favorite product ad recommendations

The most popular e-commerce websites use machine learning for finding out the favorite product of each individual and displaying the same type of advertisements so that the chances of clicking those ads and buying the products are always kept high.

• Self-driving cars

                                                

It is one of the most useful fields where machine learning plays a vital role. Car manufacturing has been increasing due to the demand of the growing population but the environment gets polluted if each individual has a separate car. To reduce the pollution caused by cars, electric car manufacturing has been increased. One of the leading car manufacturing companies, Tesla, has now created many self-driving electric cars which made people go crazy.

Machine learning is used to identify people and objects while the car is moving. This will help reduce the accidents caused during the long travel. The car models are being trained in such a way that they can detect and differentiate people and objects.

Methods of Machine Learning

The Machine learning algorithms are categorized into supervised or unsupervised methods.

• Supervised machine learning methods use previous solutions and ideas along with newly acquired data to predict the events that can happen in the future. Starting from the analysis of a known training dataset, the learning algorithm produces a function that helps to make a prediction about the output values. The algorithm can also help to compare its output with the correct output and find errors to modify the model accordingly.

• unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabelled data. The system doesn't figure out the right output but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.

                                                      

• Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning since they use both labeled and unlabeled data for training typically a small amount of labeled and a large amount of unlabeled data. The systems that use this method can considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the labeled data requires skilled and relevant resources to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn't require additional resources.

• Reinforcement machine learning algorithms are a learning method that interacts with its environment by producing actions and discovering errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best this is known as the reinforcement signal.

                                            

challenges faced while adopting machine learning

1. Inaccessible data and data security
2. infrastructure requirements for testing and experimentation
3. rigid business Models
4. lack of talent
5. time-consuming implementation
6. affordability

There are many challenges faced by us in this world. Artificial intelligence and Machine learning help us to overcome these types of challenges. There are both advantages and disadvantages present in adopting machine learning. Although the disadvantages are present, we try to reduce these and implement machine learning for the benefit of human life.