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# Machine Learning

### The concept of machine learning

Machine Learning is a branch of Artificial Intelligence that focuses on building applications that learn from previous experiences and improve their accuracy in predicting or making decisions over time.

In data science, an algorithm is actually a sequence of statistical processing steps. In machine learning, these algorithms are trained to find patterns and properties of data in large volumes, so that the decision-making and forecasting process against new data is performed intelligently. The better the proposed algorithm in terms of architecture and performance, the better the decisions will be made against the new processing data.

### The main steps of machine learning

To build an application or model based on machine learning, the following four basic steps can be suggested.

**Step 1: Select and prepare educational data**

Training data is a collection of data that plays a vital role in the implementation of the algorithm, because the final model is created based on this data. Sometimes the training data is used as labeled data to finally complete the classification process. In other cases, the training data may not be labeled, and the model will have to find the properties and characteristics of the data on its own and eventually perform the clustering process.

**Step 2: Select and implement the appropriate algorithm**

The type of algorithm you choose for modeling is highly dependent on the type of data (labeled or unlabeled), the amount of data, and the type of problem you are trying to solve. There are different algorithms for machine learning, such as regression algorithms, decision trees, instance-based algorithms, clustering algorithms, neural network algorithms, Reinforcement Learning algorithms, Deep Learning algorithms, etc.

**Step 3: Learn the algorithm to create the model**

Algorithm Learning Machine learning is an iterative process involving the execution of variables throughout the algorithm, constantly comparing the output with the original result, adjusting the weights to increase the accuracy of the algorithm, and then re-executing the variables in the algorithm. This repetition lasts until the algorithm succeeds in returning the correct result in most cases (Not all times!). The final output of this process is a trained model of machine learning.

**Step 4: Use and improve the model**

The final step is to use the model built in the third step on the new data in a higher step, improving the accuracy and efficiency of the algorithm over time. Where new data is generated depends on the problem we are trying to solve.