Important algorithms you should know for machine learning

Important algorithms you should know for machine learning

In a world where everything is mostly run by machine learning or its algorithms. Manual tasks that needed a lot of time to complete but now it’s just happening in the blink of an eye. We are witnessing an era of constant technological progress and living in the advanced computing process over the years, now it’s easy for us to predict things before they happened with the help of machine learning.

In this blog, I am going to explain machine learning and its top 10 algorithms you should know if you are learning or want to get started in the machine learning journey.

One of the reasons for this rapid machine-learning revolution is how exactly computing has been democratized. Data engineers like Data Scientists have built complex data-crunching machines in the last 7 years and they can seamlessly execute advanced techniques.

Without Machine Learning

Many people believe that machine learning is something like robots walking and talking to us or some encrypted top-tier machine learning model that helps you to secure things more safely. But this is not the end of the boundary of machine learning in fact it is just a glimpse of what machine learning is capable of doing.

Let’s say you need information on any random topic like Codedamn , the first place you would check will definitely be Google. It would collect all the information on the word you search for and present it to you according to your relevance.

If there is no google then you have to do it in a very hard way that is by going to many books or relevant places where you will find the information you are looking for. Google also uses machine learning to provide the relevant information that people are seeking.

With Machine Learning

Now that we know life would be a lot more difficult without machine learning. Let’s look into things that are run by machine learning and we are using them on a daily basis without even knowing it.

Machine learning helps us in many ways like using an e-commerce site where it gives you some pre-defined suggestion that is based on your shopping and provides a better way to buy and sell things without any trouble.

Netflix uses a suggestion system that is based on your interest in movie types and their genre with all this information it predicts the shows and movies which you will like and if you don’t it will keep it in mind and don’t show you that type of movie or series. Like this many companies are also using machine learning in their products to make user interaction more comfortable.

What is machine learning?

To put it in simple terms, let’s take one ordinary system it can’t do much apart from simple tasks but now add artificial intelligence from a layman’s point of view let’s give the same machine the power to think on its own.

That is what machine learning basically is It is an application of Artificial intelligence (Ai) that provides systems the ability to learn on their own and improve from the experience without being programmed externally. If your computer had machine learning maybe it will able to solve a complicated mathematical equation.

Types of Machine Learning

Machine learning is primarily divided into three types and they are as follows:

1. Supervised Learning

As the name suggests in supervised machine learning you have to supervise your machine learning while you train it to work on its own. It requires labeled training data which is data should be in a structured format. Some examples of supervised learning algorithms are Linear Regression,  Logistic Regression, and KNN i.e K-Nearest Neighbour.

A real-life example of supervised learning is Predicting Batsman performance – For this, you need the data about the batsman, how he plays in tough overs, or how many times he gets bold, these all are things that are needed in the form of proper data and this data can be used to predict the batsman performance.

2. Unsupervised Learning

In this learning model, there will be training data but it won’t be labeled and you don’t need to supervise this machine learning it will learn on the data provided. Examples of unsupervised learning algorithms are KNN (k-nearest neighbors), Neural Networks.

A real-life example of unsupervised learning is Understanding the Consumer base – To understand the consumer market of a particular product, for that, you don’t have any labeled or proper data, in this scenario you will use an unsupervised learning model which takes your data and learn it accordingly customer liking or disliking the product.

3. Reinforcement Learning

Reinforcement learning is where the system learns on its own, it doesn’t require to give any labeled data like supervised learning.

A real-life example of reinforcement learning is Google Assistant – In an android phone, google uses an assistant which runs on a reinforcement machine learning algorithm. It understands your behavior and very often it asks for feedback which helps the assistant to take action in your favor.

Important Machine Learning Algorithms

All these algorithms are based on the three most popular machine learning algorithms i.e – supervised learning, unsupervised learning, and reinforcement learning.

Linear Regression

The concept of linear regression isn’t new. In fact, it was discovered some odd 200 years ago. However, using linear regression to create a self-learning algorithm can be very effective, as its estimates relationships between independent and dependent variables. This is executed using the linear mathematical expression of y=ax+b.

To give an example, a machine learning algorithm using linear regression will predict where a person is inclined to purchase a product if given two independent variables like age and income.

Linear Regression is divided into two types

  • Single Linear Regression
  • Multiple Linear Regression

Logistic Regression

Logistic regression is used to estimate binary values like 0 or 1 from a set of independent variables. Essentially, logistics is best used when the problem takes two values true/false or yes/no. It is effective in scenarios where the algorithm predicts the possibility of an event taking place or making a certain choice.

Logistic Regression has many methods to improve the models:

  • use a non-linear model
  • feature elimination
  • interaction terms
  • regularize techniques

Decision Tree

As the name implies, the arrangements of data in a decision tree algorithm are made in a way that represents a tree structure. Like the different branches of a tree, the data is separated into various points or nodes based on the different features of the dataset. This result in the data being split into many homogenous classes and causes a lot of variances.

SVM (Support Vector Machine) Algorithm

SVM is a unique method of data classification where the data is plotted as a point in a space that is n-dimensional, where n is the number of features or properties of the data. Each feature is then assigned a specific coordinate, and this allows us to classify the data.

Being one of the most popular supervised machine learning algorithms, the main advantage of an SVM is that it becomes effortless to enter data points into the n-dimensional space once it is segregated into classes.

Support Vector algorithm also requires minimal computational power and can produce accurate results as well in both regression and classification tasks.

Naive Bayes Classifier

As the name suggests, this class of algorithms is based on the mathematical concepts of Bayes’s Theorem. It classifies each feature or characteristic in a category as independent and doesn’t relate one feature of a class to any other feature. In fact, the Bayesian method is known to outperform more complex classification algorithms in specific scenarios

K-Nearest Neighbour or KNN

The KNN algorithm stands out from the rest of the algorithms in this list in that KNN can be used for solving both regression and classification problems. This algorithm is also considered a “lazy learner” by machine learning enthusiasts. As it doesn’t learn from the training set instead it stores the data and when it’s time to classify it performs the actions on the data.

Since KNN stores data, it becomes very effective when a large amount of data is used to train this machine-learning algorithm

Generative Adversarial Networks

Generative Adversarial Networks, or GAN for short, consists of two neural networks – The “Generator” and the “Discriminator“. The Generator is trained to generate new data sets, while the discriminator validates the data sets generated and classifies them as real or fake.

GANs are powerful as they make the concept of generative models a reality and can create realistic examples from a given input, which could be indistinguishable from human beings. A real-world application of this neural network is image-to-image translation, as daytime images can be converted to nighttime photos and vice versa.

Recurrent Neural Networks

It is a machine learning algorithm of significant importance i.e Recurrent Neural Network or RNN used by the likes of Google Assistant and Apple’s Siri. RNNs are high-end deep learning algorithms that use sequential data feeding.

RNNs are a branch of Artificial Neural Networks and are used in most day-to-day applications like Voice Recognition, Text Generation, Machine Translations, and creating Image Descriptions, among others. RNNs have an internal memory that allows them to store input, which results in the algorithm being able to solve machine learning problems that involve sequential data.

In a Recurrent Neural Network, the information is made to cycle in the loop. This means that when the algorithm makes a decision, it takes in the current input and considers the learning from the previous input, making it extremely useful in any application involving human communication.

Convolutional Neural Networks

Convolutional Neural Networks, also known as CNN, is a deep learning algorithm that is used to classify and categorize images into various classes. A CNN works by taking an input of images by using computer vision, assigning a certain weight or importance to the image, and differentiating an image from another.

With enough training, a CNN can have the ability to learn different characteristics of an image and classify it based on that. A real-world application of a Convolutional Neural Network can be demonstrated by using a CNN to count the number of vehicles on a road and classify it, thus predicting the traffic flow of the road. This algorithm can also be used to classify pictures in your gallery, just like Google Photo does.

Gradient Boosting Algorithm and AdaBoosting Algorithm

The AdaBoost algorithm starts off by assigning equal weight to each observation and trains the decision tree. After the first tree is evaluated, the algorithm increases the importance of the observations that were difficult to classify and decreases the weight for the easily organized observations.

This process is repeated a specified number of times, resulting in a tree of well-classified data, as any observation which wasn’t correctly classified by the previous tree will be classified by the sequential tree.

Gradient Boosting trains machine learning algorithms in a similar additive and sequential manner. Some popular gradient boosting machines are XGBoost, LightGBM, and Catboost.

Conclusion

Machine learning algorithm plays an important role in today’s scenario where a most thing is dependent on algorithms. Learning machine learning and how it actually works will benefit one’s career. Try to explore these algorithms and get more familiar with them.

Keep learning!😊

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