Choosing the right machine learning algorithm for training a model is one of the biggest challenge for the AI engineers to make sure their efforts become successful. Actually, ML algorithm depends on various factors like process of model training and availability of the training data used to train the model.

Choosing the suitable algorithm for machine learning improves the chances model success and also provide a appropriate environment for AI developers to show their skills and build a right ML model while ensuring its accuracy in various scenario. Find here the different types of machine learning tasks describing the algorithm suitable for such projects.

**Understanding the Different Types Of Machine Learning Tasks**

### Supervised Learning

Supervised learning is the task of interpreting a function from labeled training data. In supervised learning by fitting to the labeled training set, we try to find out the most suitable model parameters to predict unknown labels on the other objects (test set). If the label is a real number, it is known as regression and if the label is from the limited number of values, where these values are unordered, then its called classification.

### Unsupervised Learning

Unsupervised learning is another machine learning algorithm that you can follow while training your model. In this learning process we have little information about objects or you can say the training data is unlabeled. Our goal is now, to observe same similarities between groups of objects and include them in the right clusters, to consider these objects to be anomalies making understandable for machines too.

### Semi-supervised Learning

If you are following the Semi-supervised learning to train your model you need to choose the another algorithm that can use labeled and unlabeled data. This type of machine learning process is suitable for algorithms that can not be supplied the labeled data. Such method allows to significantly improve the accuracy of model, as we can use unlabeled data in the training set with small amount of labeled data used along with that which is possible with **data annotation**.

### Reinforcement Learning

This is another machine learning method, you can choose with your algorithm. If we don’t have labeled or unlabeled datasets here reinforcement learning is used right here. This is an area of machine learning concerned with how AI and ML developers take actions in some environment to maximize some concept of cumulative reward.

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For an example, envisage that if you are installed as a robot at an unknown place you can perform the activities and the rewards from the environment for them. And after each action, your behavior is becoming more complex and ingenious, but you are well-trained to act the most effective way on each step. In scientific language it is called adaption in natural environment, that makes machines to learn with new data and perform accordingly.

**Most Common Algorithms used in Machine Learning**

Depending on the project feasibility and training data availability, there are many machine learning algorithms used by the developers in model building. Here below we will discuss about most of the popular algorithms and know which machine learning algorithm to use.

### Linear Regression and Linear Classifier

This is one of the most simple types of algorithms in machine learning you can choose. If you have features x1,…xn of objects on matrix A and labels on vector b. Now your aim is to find the most optimal weights w1,…wn and bias for these features according to some failure functions.

Practically, it is easier to optimize it with gradient descent, which is much more computationally efficient is performing the tasks. However, this algorithm is simple and works well features, while a complex algorithms suffer from overfitting many features and not huge datasets, but linear regression provides the best quality.

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Here to prevent the overfitting problem, we can often use the regularization techniques like ridge and lasso. This idea helps to add the sum of modules of weights and the sum of square of weights, respectively to our failure function.

### Logistic Regression Algorithm

As appears in name, you don’t need to confuse this type of algorithm with regression methods, as logistic regression performs binary classification resulting label outputs are binary. So, lets specify P(y=1|x) as the conditional probability that the output *y *is 1 under the condition that there is given the input feature vector *x*.

Here the coefficients “*w” *are the weights that the model wants to learn. As this algorithm computes the probability of belonging to each class, you need to take into account how much the probability differs from 0 or 1 and average it over all objects as we did with linear regression.

To understand this method more throughly you need to have the statistical and arithmetic knowledge. The best part of this logistic regression it takes linear combination of features and applies non-linear function to it.

### Decision Trees Algorithm

Decision tree is another easy to understand popular algorithm where simple graphics can help you to see what you are thinking and their engine requires a systematic, documented thought process. This algorithm is very simple and in every node we can choose the best split among all features and all possible splits points.

In this algorithms, each split is selected in such a way that helps to maximize some functional. However, in classification trees, we use cross entropy and Gini index, while in regression trees, we minimize the sum of a squared error between the predictive variable of the target values of the points that fall in that region and the one we assign to it.

We, make this procedure recursively for each node and complete when we meet a stopping criteria. They can vary from minimum number of leafs in a node to tree height. Though, single trees are used very rarely, but in composition with various others, more efficient algorithms like Random Forest and Gradient Tree Boosting are also available for machine learning.

### K-means

This is another type of algorithm used in machine learning that allows to assign labels as per the features of objects, which is also called “*clusterization“*. If you want to divide all data-objects into k clusters, you have to choose random k points from your data and name them centers of clusters. Here the cluster of other objects are defined by the closet cluster center.

And then, centers of the cluster are converted and the process repeats until occurrence. It is most clear clusterization technique which also have some drawbacks. Firstly, to use this technique you need to know the amount of clusters which is not possible. Secondly, the result depends on the points randomly selected at the beginning and the algorithm doesn’t assure that we will achieve the global minimum of the functional.

### Principal Component Analysis (PCA)

PCA is the algorithm that offers a dimensional reduction in machine learning model training. You can can apply this method, when you have a wide ranging features, which highly correlated between each other, and models can easily overfit on a huge amount of data.

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But under this method, you need to calculate the projection on some variables to maximize the variance of your data and lose as little information as possible. Amazingly, these vectors are eigenvectors of correlation matrix of features from a dataset.

### Neural Networks

While talking about logistic regression we have already mentioned neural networks. Actually, there are different types of architectures that are valuable in very specific tasks and more often it’s a range of layers or factors with linear connections among them and nonlinearities the following.

If you are using images to train your model using the deep learning neural network it will show you amazing results. And Nonlinearities are represented by convolutional and pooling layers, capable of capturing the characteristic features of images.

However, while working with texts and sequences its better to choose *recurrent neural networks. *RNNs contain LSTM or GRU modules and can work with data for which dimension is well-known in advance. And machine translation is the one of the best known applications for RNNs.

### Summing-up

Hope this article will help you to choose the most suitable algorithm for machine learning. What ever the algorithm you choose, but make sure train your model with **high-quality training data** to propel your model for accurate results.

Also check the data compatibility to ML algorithm as much the data available for machine learning algorithms training the model will learn with variations making easier to do the predictions faster and accurate in different scenario.

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