It is 2022, and machine learning has reached a stage where it affects and improves our
daily lives.
From getting Netflix show recommendations to getting ads based on your internet behaviour,
almost all the algorithms today keep learning based on actions and inputs.
Hey guys, I’m Alex and welcome back to AI News Daily!
Today we’ll be taking a look at the top 10 Machine Learning Algorithms.
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Starting off our list at Number 10 is Gradient Boosting Machines and AdaBoosting.
First of all, if you want to understand what Gradient Boosting is, you’ll need to know
what the term “boosting” refers to in the world of machine learning.
Boosting is a technique that is used to convert weaker learning algorithms.
Boosting is popularly used when a massive amount of data is to be classified with high
accuracy.
Let’s take the example of AdaBoost to explain this.
The AdaBoost algorithm starts off by assigning an equal weight to each observation and trains
a decision tree.
After the first tree is evaluated, the algorithm increases the importance for the observations
that were difficult to classify and decreases the weights for the easily organised observations.
This process is repeated for 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.
Recently, researchers over at the Centre for Applied Research in Remote Sensing in Vietnam
used these three machines to classify world land cover.
At number 9 on our list, we have Convolutional Neural Networks, also known and CNN.
CNN is a deep learning algorithm that is used to classify and categorise images into various
classes.
A CNN works by taking an input of images by using computer vision, assigning a certain
weight or an 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 classifying it, thus predicting the
traffic flow of the road.
This algorithm can also be used to classify pictures in your gallery, just like how Google
photos does.
Coming in at number 8 is a machine learning algorithm of significant importance- Recurrent
Neural Networks or RNN.
Used by the likes of Google Assistant and Apple’s Siri, RNNs are a 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.
What makes RNN stand out from other algorithms is the fact that RNNs have an internal memory,
which 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 a loop.
This means that when the algorithm makes a decision, it takes in the current input and
considers the learnings from previous inputs, making it extremely useful in any application
involving human communication.
Speaking of virtual assistants, what do you think of Google, Apple and Microsoft using
personal assistants to collect data?
Let us know your thoughts about this in the comments below.
Now let’s move on with our list.
Generative Adversarial Networks take the number 7 spot on our list.
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 and 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 by human beings.
A real-world application of this neural network is image to image translation, as daytime
images can be converted into nighttime photos and vice versa.
K-Nearest Neighbour, or KNN as it is abbreviated, comes in at number 6.
What makes the KNN algorithm stand out from the rest of the algorithms in this list is
that KNN can be used for solving both regression and classification problems.
This algorithm works by first storing all the available data.
When a new data is given to the algorithm as an input, the algorithm compares the new
data with the existing data, checks the similarities and then classifies it into a well-suited
category.
Although KNN is computationally expensive and resource-intensive, it is still widely
used and is popular due to its simplicity and its usefulness in both regression and
classification tasks.
The KNN algorithm is also considered a “lazy learner” by machine learning enthusiasts,
as it doesn’t learn from the training set and instead stores data.
When it’s time to classify, it performs 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.
At number 5 on our list, we have Naive Bayes Classifiers.
As the name suggests, this class of algorithms are based on the mathematical concept of Bayes’
Theorem.
Bayes classifies each feature or characteristic in a category as independent and doesn’t
relate one feature of a class to any other feature.
If one feature in a class is similar to another feature, the Bayes classifier considers all
of the features independent to each other while predicting an outcome.
What makes a Naive Bayesian algorithm attractive is that it is comparatively easy to build,
and it is highly scalable.
This means that this kind of algorithm can be used in large scale applications with enormous
datasets and still produce highly accurate results.
Infact, the Bayesian method is known to outperform more complex classification algorithms in
specific scenarios.
Coming in at number 4 on our list, we have Support Vector Machine algorithm or SVM for
short.
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 of 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 the classes.
Support Vector Machine algorithm also requires minimal computational power and can produce
accurate results as well in both regression and classification tasks.
And now, ladies and gentlemen, we move onto our top 3 machine learning algorithms.
What do you think of our list so far?
We really hope that our evaluation so far has been fair, and we’d like to know your
thoughts in the comments below.
Now, let’s get to our top 3.
In the third place on our list, we have Decision Trees or Random Forests.
As the name implies, the arrangement of data in a decision tree and random forest algorithms
are made in a way that it represents a tree structure.
Like the different branches of a tree, the data is separated at various points or nodes
based on the different features of the dataset.
This results in the data being split into many homogenous classes and causes a lot of
variance, and is not highly accurate.
Now you might be wondering how this algorithm ended up on our top 3 list.
To solve this problem of high variance, you can build many decision trees by using correlated
data when you’re training the algorithm, and now you’ll have what is known as a “random
forest”, which is generally very accurate even on large scale applications.
At number 2 on this list, we have a machine learning method known as Logistic Regression.
Logistic Regression is used to estimate binary values like zero or one from a set of independent
variables.
Essentially, logistic Regression is best used when the problem takes two values, like 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.
This type of machine learning algorithm can make a massive difference in decision-making
applications.
For example, in a manufacturing factory, a model built using Logistic Regression will
be able to predict the probability of a part failing with its age.
On the other side of the spectrum, logistic regression models can be used in the medical
field to predict if a given population is prone to a particular disease.
Having this huge of an impact when it comes to predicting outcomes using statistics, Logistic
Regression is worthy of ending up number two on our list.
Finally, at number 1 on our list is the machine learning model using Linear Regression.
The concept of Linear Regression isn’t new.
In fact, it was first discovered some odd 200 years ago.
However, using Linear Regression to create a self-learning algorithm can be very effective,
as it estimates relationships between independent and dependent variables.
This is executed using the linear mathematical expression of y=ax+b.
To give you an example, a machine learning algorithm using Linear Regression will predict
whether a person is inclined to purchase a product if given two independent variables
like age and income.
Now that is pretty cool and just shows a glimpse of how much machine learning algorithms can
do!
Linear Regression is the most popular machine learning algorithm among beginners as well
as professional co-corporations and hence take our first place on today’s list.
Well folks, with that, we’ve come to the end of today’s video.
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