Before diving directly into the machine learning algorithms making a difference in the current day technology, it is vital to understand the categorizations of the myriad of ML algorithms and how they are classified on what parameters. Let’s take a look at the list of contents that will help you understand the systematic distinction of these algorithms:

**Understanding Machine Learning****Types of Machine Learning Algorithms****List of Popular Machine Learning Algorithms****Linear Regression****Logistic Regression****Decision Tree****Classification in Machine Learning****SVM (Support Vector Machine) algorithm****Naive Bayes Algorithm****KNN – (K Nearest Neighbor) Algorithm****K- means****Random Forest Algorithm****Dimensionality Reduction Algorithm****Gradient Boosting Algorithm and AdaBoosting Algorithm****Conclusion**

**Understanding Machine Learning**

The term ‘Machine Learning’ seems to be a hot cake these days. So, what exactly is it?

Well, simply put, Machine Learning is the sub-field of Artificial Intelligence, where we teach a machine how to learn, with the help of input data.

Now that we know what exactly is machine learning, let’s have a look at the types of Machine Learning algorithms.

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Machine Learning algorithms run on various programming languages and techniques. However, these algorithms are trained using various methods, out of which three main types of Machine learning are:

- Supervised Learning
- Unsupervised Learning
- Semi Supervised Learning
- Reinforcement Learning

**Supervised Learning**

Supervised Learning is the most basic type of Machine Learning, where labeled data is used for training the machine learning algorithms. A dataset is given to the ML model for understanding and solving the problem. This dataset is a smaller version of a larger dataset and conveys the basic idea of the problem to the machine learning algorithm.

So in simple terms, supervised learning means a guide assigned to the algorithm teaches the model how and what to be done in a set problem environment. The algorithm establishes a cause and effect relationship between the variables based on the given parameters. Gradually, the algorithm learns and gets a fair idea of how to solve the problem and what data points to be dealt with.

**Unsupervised Learning**

Unsupervised Learning is the type of Machine Learning where no human intervention is required to make the data machine-readable and train the algorithm. Also, contrary to supervised learning, unlabeled data is used in the case of unsupervised learning.

Since there is no human intervention and unlabeled data is used, the algorithm can work on a larger data set. Unlike supervised learning, unsupervised learning does not require labels to establish relationships between two data points.

The algorithms are able to establish a cause-and-effect relationship without any manual interference. One of the major advantages of unsupervised learning is that the data sets used need not be defined since unsupervised machine learning algorithms are able to identify hidden structures within the data set.

**Semi Supervised Learning**

Semi-supervised learning algorithms represent a middle ground between supervised and unsupervised algorithms. precisely, the semi-supervised model combines some aspects of both supervised and unsupervised algorithms.

**Reinforcement Learning**

Reinforcement Learning is the type of Machine Learning where the algorithm works upon itself and learns from new situations by using a trial-and-error method. Whether the output is favorable or not is decided based on the output result already fed to each iteration.

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**Linear Regression**

Let’s understand the working functionality of this algorithm with a practical example. Say you have a stack of wooden logs and you need to arrange random logs of wood in increasing order of their weight. However, you cannot weigh each log.

You have to guess its weight just by looking at the height and girth of the log (visual analysis) and arrange them using a combination of these visible parameters. This is the basic idea behind linear regression.

In this process, a relationship is established between independent and dependent variables by fitting them to a line.

**This is the regression line and is represented by a linear equation Y = a*X + b. In this equation:**

- Y – Dependent Variable
- a – Slope
- X – Independent variable
- b – Intercept

The coefficients a & b are derived by minimizing the sum of the squared difference of distance between data points and the regression line.

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**Logistic Regression**

Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps predict the probability of an event by fitting data to a logit function. It is also called logistic regression.

**These methods listed below are often used to help improve logistic regression models:**

- include interaction terms
- eliminate features
- regularize techniques
- use a non-linear model

**Decision Tree**

Decision Tree algorithm in machine learning is one of the most popular algorithms in use today; this is a supervised learning algorithm that is used for classifying problems. It works well classifying for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets based on the most significant attributes/ independent variables.

**Classification in Machine Learning**

The Classification algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. In Classification, a program learns from the given dataset or observations and then classifies new observations into a number of classes or groups. Such as, Yes or No, 0 or 1, Spam or Not Spam, cat or dog, etc. Classes can be called as targets/labels or categories.

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**SVM (Support Vector Machine) Algorithm**

SVM algorithm is a method of classification algorithm in which you plot raw data as points in an n-dimensional space (where n is the number of features you have). The value of each feature is then tied to a particular coordinate, making it easy to classify the data. Lines called classifiers can be used to split the data and plot them on a graph.

**Naive Bayes Algorithm**

A Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

Even if these features are related to each other, a Naive Bayes classifier would consider all of these properties independently when calculating the probability of a particular outcome.

A Naive Bayesian model is easy to build and useful for massive datasets. It’s simple and is known to outperform even highly sophisticated classification methods.

**KNN (K- Nearest Neighbors) Algorithm**

This algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it’s more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors.

The case is then assigned to the class with which it has the most in common. A distance function performs this measurement.

KNN can be easily understood by comparing it to real life. For example, if you want information about a person, it makes sense to talk to his or her friends and colleagues!

**Things to consider before selecting K Nearest Neighbors Algorithm:**

- KNN is computationally expensive
- Variables should be normalized, or else higher range variables can bias the algorithm
- Data still needs to be pre-processed.

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It is an unsupervised learning algorithm that solves clustering problems. Data sets are classified into a particular number of clusters (let’s call that number K) in such a way that all the data points within a cluster are homogeneous and heterogeneous from the data in other clusters.

**How K-means forms clusters:**

- The K-means algorithm picks k number of points, called centroids, for each cluster.
- Each data point forms a cluster with the closest centroids, i.e., K clusters.
- It now creates new centroids based on the existing cluster members.
- With these new centroids, the closest distance for each data point is determined. This process is repeated until the centroids do not change.

**Random Forest Algorithm**

A collective of decision trees is called a Random Forest. To classify a new object based on its attributes, each tree is classified, and the tree “votes” for that class. The forest chooses the classification having the most votes (over all the trees in the forest).

**Each tree is planted & grown as follows:**

- If the number of cases in the training set is N, then a sample of N cases is taken at random. This sample will be the training set for growing the tree.
- If there are M input variables, a number m<<M is specified such that at each node, m variables are selected at random out of the M, and the best split on this m is used to split the node. The value of m is held constant during this process.
- Each tree is grown to the most substantial extent possible. There is no pruning.

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**Dimensionality Reduction Algorithms**

In today’s world, vast amounts of data are being stored and analyzed by corporations, government agencies, and research organizations. As a data scientist, you know that this raw data contains a lot of information – the challenge is in identifying significant patterns and variables.

Dimensionality reduction algorithms like Decision Tree, Factor Analysis, Missing Value Ratio, and Random Forest can help you find relevant details.

**Gradient Boosting Algorithm and AdaBoosting Algorithm**

These are boosting algorithms used when massive loads of data have to be handled to make predictions with high accuracy. Boosting is an ensemble learning algorithm that combines the predictive power of several base estimators to improve robustness.

In short, it combines multiple weak or average predictors to build a strong predictor. These boosting algorithms always work well in data science competitions like Kaggle, AV Hackathon, CrowdAnalytix. These are the most preferred machine learning algorithms today. Use them, along with Python and R Codes, to achieve accurate outcomes.

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**Conclusion**

If you want to build a career in machine learning, it is the right time to step up and get into the market. The field is constantly expanding with increasing prospects each day, and the sooner you understand the scope of machine learning tools, the sooner you’ll be able to provide solutions to complex work problems.

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