Machine Learning is a sub-branch of Artificial Intelligence, used for the analysis of data. It learns from the data that is input and predicts the output from the data rather than being explicitly programmed. Machine Learning is among the fastest evolving trends in the IT industry. It has found tremendous use in sectors across industries, with its ability to solve complex problems which humans are not able to solve using traditional techniques. ML is now being used in IT, retail, insurance, government and the military. There is no end to what can be achieved with the right ML algorithm.
Machine Learning is comprised of different types of algorithms, each of which performs a unique task. Users deploy these algorithms based on the problem statement and complexity of the problem they deal with. Generally, ML algorithms are a combination of both “Mathematics and Logic” that help write new algorithms for the problem statements or to implement existing algorithms with slight modifications. If ML algorithms consume more amounts of data while solving a problem, it results in better performance. Thus, Machine Learning works on the strategy of:
“The more the data that is fed as input, the better is the performance of the model at the end”
Machine Learning follows a systematic way of solving problems to get the desired results in the end. Initially, it collects data from different sources. Then it is subjected to an Exploratory Data Analysis step to get rid of unwanted data or noise hidden in the data, and to replace or remove Null values from the data to convert it into a structured format. The whole data is subjected to the ML algorithm as input to draw insights and get the desired results at the end of the given problem statement. The accuracy of the model decides the best algorithm for the data we take in at the end.
Styles of ML Algorithms
Most Machine Learning algorithms are categorized into three types. This categorization is based on the kind of problem the specific algorithm deals with. The categories are:
- SEMI – SUPERVISED
In supervised ML algorithms, the user knows both the Input and Output data before applying any algorithm on the data. In simple terms, this can be said to be an Input-Output pair of data. Here all the data is called training data, and the information consists of labels. The training process of the data is continued till the model achieves the desired accuracy. These sets of algorithms are used to predict the results from the input data.
E.g.: - Classification & Regression algorithms
In unsupervised ML algorithms, the data is not labelled, and the output is also not known. In this case, an ML model is prepared by performing some strategies like Data selection, cleaning, preprocessing, and transformation on the data to get a perfect structure to apply algorithms on the data.
E.g.: - Clustering, Dimensionality Reduction and Association Rule Learning
In semi-supervised ML algorithms, the data consists of both labelled and unlabeled examples. Here the model must learn the structures to organize the data and make predictions from the data at the end.
Overview of Machine Learning Algorithms
Apart from the Learning styles, algorithms are further grouped by similarity. Each group possesses different learning capabilities of algorithms to solve the given problem statements and produce good results at the end. A grouping of machine learning algorithms is basically done using the area of problems they deal with, in order to increase their accuracy and reliability.
ML algorithms are grouped based on whether they are numerical, categorical, boolean, grouping, classification, video, audio, images etc. The grouping brings out a clear picture of the particular type of data taken, that addresses the specific problems faced by the user. The grouping is done to ensure that the accuracy increases while using the algorithms, as compared to the previous ones. A developer can negotiate that his/her model is the best model, when he/she compares the accuracy of his model with that of the other algorithms. This comparison helps us in finding the algorithm that best suits the given problem statement.
In today’s scenario, Machine Learning algorithms have gained great significance in many industries and can be used to advantage in day-to-day life. Machine Learning is a boon for all types of industries, as it solves many difficult problems, and helps to mitigate risk and loss of human power. This technology is used in robotic process automation, satellites, space research centres, underwater areas, self-driving cars, the automotive industry, the health industry and many others.
ML can be used to solve problems of any scale—big or small. Its advantage lies in its intuitive power. The more you teach a machine learning model, the more it learns from the data and the more perfect it becomes, helping you get better output with higher accuracy. ML is now moving towards automation and this potent combination of machine learning and automation can change our lives like we have never imagined.
The application of “Re-engineering” has been evolving at present with Machine Learning. The older applications are complex and require a large amount of code to be written, whereas ML helps make applications with fewer code, written in a universal ML language that can be better understood by developers who can make the application richer by adding more to it.
It tells the relationship between two variables, like an input variable that takes data into the model with some parameters and predicts the output variable. Regression algorithms are used to predict continuous values.
They are mainly used to predict the linear data in the output, from the input data we take from a user and apply it to the model. These are the simplest algorithms to implement in real-world projects as they deal with Numerical data in preparing the model. In ML, the types of regression algorithms are as follows:
- Linear Regression
- Logistic Regression
- Stepwise Regression
Instance Based Algorithms
These algorithms are based on data and training data because the model learns from activities to predict the new cases of data given by the user. They are used for decision-based problems with instances.
Here, the model learns from the training data. The new data is compared against the trained data with a similarity measure in the data to find the best match and make predictions according to the model performance in the data classification. Generally, these types of algorithms build up a database of data and are applied upon large databases. They are also called memory-based algorithms. In machine learning, the various kinds of Instance-Based Algorithms are as follows:
These algorithms are used to make slight changes to the model prepared by a developer. Regularization algorithms help to overcome the problem of Over-fitting of data during the training of the model. Over-fitting learns the unwanted areas from the data, its noise, etc., and at the end, it may lead to the wrong prediction in the new data given by the user. To overcome such types of problems during the development of the model, the regularization concept is used. In machine learning, there are two types of Regularization Algorithms, and they are:
- L1 Regularization or Lasso Regularization
- L2 Regularization or Ridge Regularization
- Elastic Net
Decision Tree Algorithm
These algorithms are best used for decision-based problem solving on the data's attributes and are represented using a tree-like structure consisting of leaves and nodes. It consists of a root node that describes the problem we are dealing with, and it is further split into leaves, with the leaf node indicating the end to the tree. The tree structure is extended with the number of nodes till a perfect prediction is made from the given data using the model.
Decision tree algorithms are best used in classification as well as regression problems. These can operate on both small amounts and large amounts of data. Decisions taken using this algorithm are often fast and accurate. In machine learning the different types of Decision Tree algorithms include
- Classification and Regression Tree (CART)
- Decision stump
- Chi-squared automatic interaction detection (CHAID)
These types of algorithms are built based on the Bayes theorem. All the algorithms in this category have a probabilistic approach in solving the problems. They tell about the probability of prediction of the outcome at the end using the given data. We can draw probabilistic insights from the data.
These are the most used algorithms in stock predictions or weather predictions, done by taking in a lot of data and examining them to predict the best outcome. They can solve both the classification and regression types of problems. In machine learning, some of the different types of these algorithms are:
- Naïve Bayes
- Bayesian Belief Networks
- Bayesian Networks
- Gaussian Naïve Bayes
The word clustering indicates a grouping of something. Clustering algorithms contain similar types grouped or clustered together with common similarities between the data. The models in this algorithm are generally approached in a Centroid based and a Hierarchal way. They inherit structures from the older structures and find out the new ones. The data is mostly organized into groups, based on the similarity present in the structures. In ML, the different types of clustering algorithms are:
- K-means clustering
- Hierarchal clustering
- K-medians Clustering
Association Rule Learning Algorithms
These algorithms explain the relationship between the data, with the help of some rules that are predefined by them. They contain a minimum threshold predefined by the user, and this process is carried out until we get the desired results. This process is held in a sequence of steps satisfying the threshold value given by the user. If the operation fails to satisfy the minimum threshold value, the given function's data is eliminated further. The rules defined by these types of algorithms help to discover commercially useful and important associations among large datasets. In ML, the different types of Association algorithms are:
- Apriori Algorithm
- Eclat Algorithm
Artificial Neural Networks Algorithms
Artificial neural network algorithms are similar to the functioning of the neural networks in our brain. Generally, these algorithms fall under the category of Deep Learning, which is a core field in Machine Learning. The ANN also has a little significance in Machine Learning as it can deal with solving the problems related to Classification and Regression. In ML, the different types of algorithms in this category are:
- Stochastic Gradient Descent
- Multilayer Perceptron etc
Deep Learning Algorithms
They are the core part of Machine Learning algorithms and deal with large amounts of data to get better accuracy and results. These represent an updated version of Artificial Neural Networks, used to perform better computation on the given data. Deep Learning Algorithms consists of various algorithms that can solve many real-time problems, even with huge amounts of data present. These algorithms can be used in Speech Recognition, Video analysis & Recognition, Audio, and in Text formats. The different types of Deep Learning algorithms are:
- Convolution Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Long Short-Term memory networks (LSTMs)
Dimensionality Reduction Algorithms
These types of algorithms are quite similar to clustering algorithms in inheriting structures from the data. They are Unsupervised machine learning algorithms, and draw insights from data in an Unsupervised manner. They follow a special technique in describing data using lesser information, i.e., they can describe whole data using some pictorial representations, graphs, pie charts etc.
Even though it is an Unsupervised ML algorithm, by presenting the data in a visual format we can simplify the tasks and can solve the given problems using a Supervised learning approach. These algorithms are mainly helpful in solving classification and Regression Problems. In ML the algorithms under this category are:
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Quadratic Discriminant Analysis (QDA)
- Multidimensional Scaling (MS)
These are unique algorithms from the machine learning perspective. These models comprise multiple weaker models that are independently trained. The predictions given by these weaker models are combined to form an overall prediction from the data. It requires a lot of effort to combine the weak models and form an effective model among the weaker models to generate great accuracy.
These are very popular and powerful algorithms in ML. In real-world scenarios, we use this type of algorithm a lot, as we cannot get good accuracy from the input data. These algorithms are used to boost the accuracy of the model for the better prediction of the given problem compared to other algorithms. In ML the different types of algorithms in this category are:
- Bagging (Bootstrapped Aggregation)
- Random Forest
- Gradient Boosting Mechanism
- Weighted Average (Blending)
Popularity of Machine Learning Algorithms
Machine Learning Algorithms are going to revolutionize our way of life—by helping us solve not just critical problems but offering solutions to our everyday needs. Almost all industries today use the techniques of ML to find solutions to their problems. ML algorithms are constantly evolving, by learning and improving themselves. The algorithm’s ability to learn from the data on its own further helps in predicting better outcomes with improved accuracy.
ML deals with many problems related to Audio, Video, Text, Classification of species or items and so on. These algorithms are further extending their applications into the fields of Robotics, Health care, Automation and more. Many industries are getting automated with the use of latest technologies like Machine Learning, Artificial Intelligence, and Data science. The main use of ML algorithms is to predict the outcomes based on the given input data by creating a model that in turn produce better results. Some of the popular applications of ML are found in sales prediction, weather prediction, stocks exchange detection etc.
How to study Machine Learning Algorithms
Algorithms are at the heart of machine learning. There is no predefined structure or format indicating how you should study algorithms. You should, however, have a clear picture of what the algorithm is, how it works, and where and how it should be implemented.
It is important to know the Mathematics and logic behind the algorithm, as Mathematics is key to the right implementation of the algorithm to solve real-world problems. After learning math concepts, ML enthusiasts must implement the algorithm in a programming language to know how it works. At present PYTHON and R are the two programming languages that are most widely used to implement Machine Learning in the real world. You can refer to websites, blogs, or enrol in trainings, to get a clear picture of the world of ML and algorithms.
How to Run Machine Learning Algorithms
To run Machine Learning algorithms, we need to have a platform to write the Script and run the script to see the outcome. We can run ML algorithms in Python IDLE, Jupyter Notebooks, Anaconda, Google Colab, Kaggle etc., but the most widely used tool by many people is Jupyter Notebooks, due to the ease of writing and running code. When writing and running ML algorithms, a series of steps are involved such as importing the required Libraries, loading the dataset from the system or any other sources, defining the model, testing the model, and predicting the outcome with better accuracy.
This blog provides an overview of different types of ML Algorithms, where they are used, how to study the algorithms and how they can be run, the steps involved and so on. We hope this article has helped you to get a basic idea of machine learning, if you want to pursue a career in this field.