The 10 Algorithm Machine Learning Engineers should know about it
What’s The Buzz Around “Machine Learning”?
Machine Learning - the term which almost everyone in this generation have been acquainted with. So what is it? And is it really that awesome people hype it to be?
No matter, wherever you are currently in the world, accessing application softwares or any web application - there’s a cent percent chance of it being based on Machine Learning algorithms or AI dependent.
If you are a budding developer, somewhere down the lane in your software development lifecycle, you would definitely need to implement Machine Learning algorithms to mark your work as technology relevant.
Big Data is a booming technology that has emerged and easened up the process of tackling gigantuous chunks of data, which seemed to be unthinkable back in the day. With the advent of big data, machine learning and artificial intelligence posed to be extremely crucial for predicticting results based on those datasets.
How do your Netflix and Amazon Prime accounts recommend films and series based on your prior watch history? From where does Flipkart get to know about your shopping plans and suggest those very items which were on your mind before opening the application?
All these are some of the most common beauties of Machine Learning and how effective it can be for developers to use this weapon for your next development project!
Types of Machine Learning Algorithms
Primarily, Machine Learning Algorithms can be classified into four types.
The next top 10 machine algorithms that we would be discussing below are more or less dependent on the first two types.
Top 10 Algorithm ML Engineers Should Consider Learning
One of the most simple yet widely used ML algorithms that we can talk of is Linear Regression.
Here, a relationship is constructed between a dependent variable and one or more independent variable(s) by plotting them over a coordinate system. The best fit line is termed as the regression line.
While linear regression works perfectly for continuous dependent variables, logistic regression is best suited for classification problems.
It is used to estimate discrete values like 0/1, for example the answer to whether a customer will buy the product or not, i.e. a YES or a NO.
Support Vector Machines -
In this algorithm, classification problems can be solved by constructing a hyperplane between two sets of data classified as two or more classes, with the use of “support vectors”.
Various kernel functions are implemented during evaluation. The IRIS classification problem can be solved using SVMs.
This is one of the most famous supervised learning ML algorithms, working well with both continuous and categorical dependent variables.
Decision Trees use a tree-like graph or model of decision and their possible consequences. Various variables of decision trees include Boosted Decision Tree and Random Forest.
Clustering is the task of grouping a set of objects such that objects in the same group (cluster) are more similar to each other than to those in other groups.
The types of clustering algorithms are as follows -
Centroid based Algorithms
Connectivity based Algorithms
Neural Networks/Deep Learning
KNN(K-Nearest Neighbour) Algorithm -
KNN algorithms have much similarity with real life. It stores available cases and classifies any new case on the basis of a majority vote by its neighbours.
KNN is primarily used in classification problems.
KNN is a bit computationally expensive and the data needs to be pre-processed.
A Naive Bayes classifier assumes that the presence of a particular feature class is unrelated to the presence of any other feature.
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.
An unsupervised machine learning algorithm that solves clustering problems.
In this algorithm, k number of points called centroids for each cluster. Each data point forms a cluster with the closest centroids. The nearest distance for each data point is determined. This process is repeated.
Random Forest Algorithm -
All algorithms are error prone. Collective wisdom is generally higher than individual wisdom. Ensemble Learning generates a group of base learners and combined results gives higher accuracy.
Two major ensemble learning methods include bagging and boosting. Random Forest Algorithm is an ensemble learning technique.
Gradient Boosting Algorithm -
Gradient Boosting Algorithm is significantly useful in huge datasets demanding predictions with high accuracy.
In the Boosting algorithm, the decision trees are trained in a sequence and learn from the previous tree by focusing on incorrect observations.
Efficient Data scientists and machine learning engineers can land a job anywhere above 20-30 lpa easily. The scope for the subject is huge so too its depth.
These machine learning algorithms have been designed to solve complex real life problems. Moreover, they are automated and keep updating with time. Some basic knowledge of a programming language like python or R, and the intent to study machine learning passionately are enough to land yourself an established data scientist!