In-depth study of Machine Learning Algorithms

Many of us do not know that there is a proper list of machine learning algorithms. So here in this article, we will see some methods of using these algorithms. Through these Machine learning algorithm, you also get to know more about Artificial intelligence and designing machine learning system.

The Machine Learning Algorithm list includes:

Linear Regression
Logistic Regression
Support Vector Machines
Random Forest
Naïve Bayes Classification
Ordinary Least Square Regression
K-means
Ensemble Methods
Apriori Algorithm
Principal Component Analysis
Singular Value Decomposition
Reinforcement or Semi-Supervised Machine Learning
Independent Component Analysis

These are the most important Algorithms in Machine Learning. If you are aware of these Algorithms then you can use them well to apply in almost any Data Problem. Data Scientists and the Machine Learning Enthusiasts use these Algorithms for creating various Functional Machine Learning Projects. Then comes the 3 types of Machine Learning Technique or Category which are used in these Machine Learning Algorithms.

The three categories of these Machine Learning algorithms are:

Supervised Learning
Unsupervised Learning
Reinforcement Learning

To understand it better, you would need to understand each algorithm which will let you pick the right one which will match your Problem and Learning Requirement.

Types of Machine Learning �?? At a Glance - List of Machine Learning Algorithms

Supervised Learning

The supervised Learning method is used by maximum Machine Learning Users. There is a basic Fundamental on why it is called Supervised Learning. It is called Supervised Learning because the way an Algorithm's Learning Process is done, it is a training DataSet. And while using Training dataset, the process can be thought of as a teacher Supervising the Learning Process. The correct answer is known and stored in the system already. The algorithm helps in making Predictions about the Data that is in Training Process and gets the correction done by the Teacher itself. There is an end to learning only when the Algorithm has achieved an acceptable degree or level of Performance.

There are two types of Supervised learning problems. These Supervised problems can be further grouped into regression and classification problems.

Classification Problems: Classification problem can be defined as the problem that brings output variable which falls just in particular categories, such as the "red" or "blue" or it could be "disease" and "no disease".

Regression: A regression problem is when the output variable is a real value, such as �??dollars�?? or it could be "weight".

There are some problems which you get to observe in the Data Type. The common problems which occur or gets built on the head of the Classification Problems and the Regression Problem. The common Problems include the Time-series Prediction and Recommendation respectively.

There are a few really Popular supervised machine learning algorithms, such as:

Decision Trees
Naive Bayes Classification
Support vector machines for classification problems
Random forest for classification and regression problems
Linear regression for regression problems
Ordinary Least Squares Regression
Logistic Regression
Ensemble Methods

Unsupervised Learning

Unsupervised learning is that algorithm where you only have to insert/put the input data (X) and no corresponding output variables are to be put.

The major goal for unsupervised learning is to help model the underlying structure or maybe in the distribution of the data in order to help the learners learn more about the data.

These are termed as unsupervised learning because unlike supervised learning which is shown above there are no correct answers and there is no teacher to this. Algorithms are left to their own devices to help discover and present the interesting structure that is present in the data.

Unsupervised learning problems can even be grouped ahead into clustering and association problems.

Clustering: A clustering is that problem which indicates what you want to discover and this helps in the inherent groupings of the data, such as grouping the customers based on their purchasing behavior.

Association: An association rule is termed to be the learning problem. This is where you would be discovering the exact rules that will describe the large portions of your data. Example: People who buy X are also the one who tends to buy Y.

Some popular examples of unsupervised learning algorithms are:

K-means for clustering problems
Apriori algorithm for association rule learning problems
Principal Component Analysis
Singular Value Decomposition
Independent Component Analysis

Reinforcement or Semi-Supervised Machine Learning

There are Problems where you'll find yourself that you've found a large amount of input data. Let's consider it as (X) and then later some of the data is labeled as (Y). These are termed as semi-supervised learning problems.

These problems will actually sit in between supervised learning and then the unsupervised learning.

Many of the realistic-world machine learning related problems fall into this category. This is because it could be really expensive or maybe time-consuming. To label this data as it may require the access to get through the domain experts. The unlabeled data is cheap and comparatively easy to collect and store.

You can use these unsupervised learning techniques to do wonders. This can help you discover and learn the various valid structures that are in the input variables.

You can also use the supervised learning techniques to make the best of the guess predictions which would be belonging to the unlabeled data. You can then feed that data back into the supervised learning algorithm as training data does and then later use the model to make predictions based on new unseen data.

Source: HOB

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