Some significant stats related to Machine Learning:

- Machine learning tops AI funds globally and has reached USD 28.5 billion.
- The top machine learning use cases include risk management (82%), trading (63%), performance analysis and reporting (74%), and automation (61%).
- The estimated value of the voice assistant market is estimated to reach USD18 billion in the next five years.
- Around ⅓ of IT, leaders are adopting ML for business analytics.
- Around 25% of IT pros are currently using ML for security purposes and 16% are applying ML in sales and marketing.
- Netflix saved about USD 1 billion by adopting a machine learning algorithm.

So, Machine Learning is around us in our daily lives and we don’t even realize its presence around us. Indeed, we realize the importance of Machine Learning Certification as you will need to make your career future-proof and will place you in the most innovative and high-paying jobs.

## What is Machine Learning?

Machine Learning is commonly used interchangeably with AI (Artificial Intelligence) and is a subset of AI. Machine Learning enables the system to learn and improve automatically from experience and there is no need to explicitly programming it. Machine Learning is intended to develop computer programs that can access data and utilize it to learn for itself. To run these machine learning algorithms, make sure you have a powerful computer or gaming laptop.

## What is a Machine Learning Algorithm?

The series of properly defined steps that make your program go smarter by enabling them to learn automatically from the data provided and experience, is referred to as a machine learning algorithm. Machine learning goes through two phases, the training phase, and the testing phase.

The three types of Machine Learning are:

- Supervised Learning: an algorithm is trained with input attributes and expected output.
- Unsupervised Learning: there is no expected output and no training.
- Reinforcement Learning: based on hit and trial methods.

The general mapping for the machine learning algorithm is described as learning a target function that exactly maps input variable x to an output variable ‘y’: **y=f(x)**.

Let us see the most common machine learning algorithm in application these days:

### 1.Linear Regression

One of the most well-known algorithms, Linear Regression is used to estimate real values based on continuous variables. The relationship between independent and dependant variables is established by fitting the regression line; and is represented by a linear equation **Y=a*X+b.**

Here Y is the dependent variable, a is the slope, X is the independent variable, and b is the intercept.

Linear regression is of two types: Simple Linear Regression and Multiple Linear Regression. There is one independent variable in the former and multiple independent variables in the latter. You can go with polynomial or curvilinear regression while finding the appropriate fit line.

### 2.Logistic Regression

It is used to estimate discrete values (such as yes/no, 0/1, true/false) based on a given set of independent variables, so it is not a regression algorithm but a classification algorithm. It projects the probability of an event that is going to occur by fitting the data into the logit algorithm, and that’s the reason its output lies between 0 and 1. This algorithm is also known as the logit algorithm.

To improve the algorithm, you can take some steps such as removing features, regularization techniques, including interaction terms, or using a non-linear model.

### 3.Decision Tree

The type of supervised learning algorithm which is used for classification problems is referred to as Decision Tree. This algorithm works for both categorical and continuous dependent variables. We are well acquainted with the algorithm from our school days itself. For example, the grading system of our exam which grades A1 for 91 to 100%, grade A2 for 81 to 90%, and so on.

To make distinct groups, the most significant attributes/independent variables are processed.

### 4.Support Vector Machine or SVM

This is a classification method which maps each data item as a point in the n-dimensional space (where n is the number of features) where each feature represents the value of a particular coordinate.

SVM can be one of the most powerful out-of-the-box classifiers which you should give a try on your datasets.

### 5.Naive Bayes

A simple yet surprisingly powerful algorithm is Naive Bayes which is used for predictive modeling. This classification method is based on Bayes’ theorem which assumes the independence between predictors. This model is useful particularly for large datasets and is easy to build. Apart from being simple, it is known for outperformance when it comes to highly sophisticated classification methods.

### 6.kNN or k Nearest Neighbors

A simple and effective algorithm, kNN can be used for both classification and regression problems. It stores all available cases and by taking the help of majority votes of its k neighbors, classifies the new cases. An example of kNN can be easily seen in our daily life. If we wish to know about a person who is unknown to us, we can inquire about their neighbors and the places he visits.

### 7.Linear Discriminant Analysis

Unlike logistic regression, which is limited to two-class classification problems, Linear Discriminant Analysis can be used when you have more than two classes. It consists of statistical properties of your data which is calculated for each class. The discriminant value for each class is calculated and prediction is made with the class having the largest value. It is a powerful algorithm when it comes to classification predictive modeling problems.

### 8.K- Means

A type of unsupervised algorithm, K-means is used to solve clustering problems. It involves an easy and simple process of classifying a given data set through k number of clusters. Inside a cluster, there are data points that are homogeneous and heterogeneous to peer groups. K means involves the number of points for each cluster that are known as centroids.

### 9.Random Forest

Random Forest, trademark term for an ensemble of decision trees, so the collection of decision trees is considered as ‘Forest’. The new objects are classified based on the attributes, which are given by each tree for classification. These attributes are referred to as ‘votes’. The classification that receives the most votes is chosen by the forest.

### 10.Dimensionality Reduction Algorithms

Data capturing has seen an exponential increase in the last few years. Government agencies, research organizations, corporate sectors require capturing data in depth. Dimensionality Reduction Algorithms prove to be powerful when it is required to build robust models that can identify significant variables.

## Conclusion

After coming across the machine learning algorithms in brief, you may find that it is an interesting and innovative field. Moreover, it can help you in giving your career a great boost. To get certified, the most sensible way is to take up an online training course. The online training course makes your learning hassle-free and ensures that you clear the exam in the first attempt itself.

Get yourself enrolled now!