Machine Learning is a branch of artificial intelligence. It is an algorithm of the computer that enhances the decision-making process and allows users to take the advantage of prompt decision-making. Enhanced with experience and use of data, it solves problems, hence, a lot of people depend on Artificial Intelligence and Machines for their work.

Machine Learning is used for organizing data, based on the learning model and for future prediction. The machine has been developed to perform complex tasks and make decisions. Technologies based on machine learning are used daily like speech recognition, Google, self-driving cars, Siri, etc.

Importance: it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products.

TYPES OF MACHINE LEARNING

Supervised learning: Here, data scientists supply algorithms with labeled training data and define the variables they want the algorithm to assess for correlations. Both the input and the output of the algorithm are specified.

Unsupervised learning: This type of machine learning involves algorithms that train on unlabelled data. The algorithm scans through data sets looking for any meaningful connection. The data that algorithms train on as well as the predictions or recommendations they output are predetermined.

Semi-supervised learning: This approach to machine learning involves a mix of the two preceding types. Data scientists may feed an algorithm mostly labelled training data, but the model is free to explore the data on its own and develop its understanding of the data set.

Reinforcement learning: Data scientists typically use reinforcement learning to teach a machine to complete a multistep process for which there are clearly defined rules. Data scientists program an algorithm to complete a task and give it positive or negative cues as it works out how to complete a task. But for the most part, the algorithm decides on its own what steps to take along the way.

Here are some of the common machine learning algorithms

•Neutral network •Logistic regression •Linear regression •Clustering •Random forest… Etc

ADVANTAGES OF MACHINE LEARNING

•Automation for everything: Machine Learning is responsible for cutting the workload and time and allowing developers more time for other work. During automation, we let the algorithm do the hard work. It lets them predicts and improves algorithm on their own. By allowing the machine to learn, you don't need to stay with the project.

•Continuous advancement: when humans get knowledge, we become experienced and have a tendency to become more accurate in doing things, a machine also dos that, they gain experience, they keep improving in their accuracy and efficiency, and it leads to better decisions. Machine Learning keeps evolving There is a lot of scope for machine learning to become the top technology in the future. It has a lot of research areas, it improves hard and software. They are capable of learning from the data we provide.

•Identifies trends and pattern: It reviews large volumes of data and discover particular trends and patterns that would not be obvious to humans. The machine learns more when it gets more data.

•Handles varieties of data: In an uncertain and dynamic environment, Machine learning is good at handling a variety of multidimensional data. It analysis and processes data in ways that a normal system can't. Also, a multi-tasker

•Wide range of applications: Machine learning has a wide range of applications. Mean8bg we can apply machine learning in any of the major fields or industries. It helps create opportunities. Can be used in education, business, medicine, banking, tech, etc.

DISADVANTAGE OF MACHINE LEARNING.

•Data acquisition: It needs massive data for training and testing. This can cause data inconsistency. Collecting data comes with a cost. When we collect data from surveys it may be large volumes of fake and incorrect data, therefore data should be unbiased and of good quality. Here data requirement is more, the more the data the more the accuracy. It requires more data for better forecasting. And Data acquisition is difficult.

•Prone to high error: Machine Learning is self-reliant but is greatly susceptible to errors, it can be highly vulnerable. We select an algorithm based on the results. If the data provided to the machine may be biased, the data is used to make another prediction, there will be a chain of error and would become almost impossible to remove, especially if the data is huge. It would be detected, but it takes time to resolve. The data pushed in must be clean and accurate.

•Algorithm selection: Machine learning problems can implement various algorithms to find a solution. We have to run and test our data in all the algorithms we want, we choose them based on the resulting algorithm, this is manual and tedious.

•Consumes Time and resources: It takes a lot of time because it has to be effective and efficient and it comes through experience which takes time.

CONCLUSION

Machine learning Now you've seen that machine learning has a lot of advantages that help us for better predictions, but it also isn't perfect, it has a lot of disadvantages. A lot of time, money and resources, and data are needed for this.