Trending December 2023 # Top 10 Machine Learning Courses For 2023 # Suggested January 2024 # Top 12 Popular

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Chatbots, spam filtering, ad serving, search engines, and fraud detection, are among only a couple of instances of how machine learning models support regular day to day life. Machine Learning is the thing that lets us discover patterns and make mathematical models for things that would sometimes be unthinkable for people to do. Not at all like data science courses, which contain subjects like exploratory data analysis, statistics, communication, and visualization techniques, machine learning courses concentrate on teaching just the machine learning algorithms, how they work numerically, and how to use them in a programming language. Let’s look at some of the top courses giving the best machine learning training.  

This Machine adapting course provided by SuperDataScience Team encourages a student to make Machine Learning Algorithms in Python, and R. This course comprises of ten distinct segments. It covers themes like Data processing, Regression, classification, clustering, Association Rule Learning, Natural Language Processing, Deep Learning, Dimensionality Reduction, etc. The course includes 40.5 hours of on-demand video, 19 Articles, two supplemental resources, and enables free access to mobile and TV. A certificate is given after the effective completion of the course.  

This is a beginner-level course that presents successful machine learning methods. You will likewise figure out how to execute these procedures in your everyday existence and use them to determine issues. Core topics canvassed in the course incorporate linear regression, linear algebra, logistic regression, regularization, neural networks and support vector machines. You’ll likewise study dimensionality reduction, anomaly detection and recommender systems. A seat in this 10-module course is free. Expect to go through 56 hours working through the course material, which incorporates videos, reading and tests.  

The course utilizes the open-source programming language Octave rather than Python or R for the assignments. This may be a major issue for a few, yet in case you’re a complete beginner, Octave is really an easy method to gain proficiency with the basics of ML.  

Offered by the University of Washington, this free course is a segment of the Machine Learning Specialization. It is intended for people who need to figure out how machine learning can help analyze information and improve business operations. At the point when you arrive at the end goal, you’ll have the right skills to apply the techniques learned for each case study in the field. You will likewise have the option to utilize Python to execute your new range of abilities. Educator Carlos Guestrin is an Amazon teacher of machine learning in computer science and engineering department and Emily Fox is an Amazon professor of machine learning in statistics.  

These courses spread points like Introduction to Deep Learning, How to Win a Data Science Competition – Learn from Top Kagglers, Bayesian Methods for Machine Learning, Practical Reinforcement Learning, Deep Learning in Computer Vision, Natural Language Processing and Addressing Large Hadron Collider Challenges by Machine Learning. After the end of the course, students get a certificate to feature their recently procured ability on their resume.  

In a little over 2.5 hours, you can gain proficiency with the fundamentals of machine learning in this beginner-level course from LinkedIn Learning. Driven by Data Scientist Derk Jedamski, this class explored different machine learning algorithms and approaches to take care of any issues that emerge. The course starts with an introduction on the essentials of machine learning, trailed by an exercise on exploratory data analysis and data cleaning. You will likewise become familiar with the prescribed procedures for estimating success and optimising a model. The last exercise covers the end-to-end pipeline process. Enrollment is included for the $29.99 month to month LinkedIn membership or you can get a free seat by enrolling for a 1-month trial. Learn to compose essential Python before you join.  

It is safe to state that machine learning is actually everywhere today. A large number of us take various courses to become familiar with the different concepts in these points however shockingly, one of the vital pieces of this field is frequently neglected. This specialization expects to bridge that gap and helps you to manufacture a strong establishment in fundamental mathematics, its natural comprehension and use it with regards to machine learning and data science. Start with Linear Algebra and Multivariate Calculus before moving onward to progressively complex ideas. Before the end of the classes, you will have a solid mathematical balance to take further developed exercises in ML and become an expert.  

This Udacity Nanodegree Program that will assist you with picking up the must-have aptitudes for every aspiring data analysts and data scientists. Explore the end to end process of researching information through a machine learning lens. Figure out how to extract and distinguish valuable highlights that can be utilized to speak to your data in the best structure. Likewise, you will also go over probably the most significant ML algorithms and assess their performance. You will find out about supervised learning, deep learning, unsupervised learning among a host of other topics. You likewise get a one on one mentor, personal career coaching along with access to the student community.  

Offered by Packt Publishing, this course shows you the best way to utilize artificial intelligence to perform predictive analysis and solve real-world problems. It’s intended for data scientists and software developers who need to improve their range of abilities to improve machine learning projects.

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10 Reasons To Study Machine Learning In 2023

If you read about the future of Machine Learning, it will definitely motivate you in pursuing it and help you with the learning process. This article talks about the ten reasons why you should start studying it right now. It talks about the short-term, long-term, financial, developmental, and socioeconomic impacts of Machine Learning.

What is Machine Learning?

Machine Learning is the field of study that allows computers to execute specific functions and modify them with time without being explicitly programmed. Machine Learning programs aim to allow the code to run and adapt with time without the need for human intervention.

Machine Learning works completely differently than traditional programming while still sharing the same attributes. It opens up unlimited options and endless opportunities for an individual. There are infinite reasons why you should study AI and machine learning. Here are the top ten reasons that you must dive into for understanding the world of Artificial Intelligence with a simple machine learning course.

1. The career Opportunities

Being a modern-day science with unlimited applications, AI and Machine Learning opens up a plethora of career opportunities for everyone. As it’s a new field in technology, you will face less competition while choosing a career path because there are fewer experts in the industry of Machine Learning and Artificial intelligence. At the same time, its vast usability and applications ensure dynamic and unlimited options while providing a safe future.

According to Datamation, the Machine Learning market crossed the one billion landmark in 2023 with a value of $1.41 billion. And it is expected to exponentially grow to a massive $8.1 billion in an unbelievably smaller time frame, i.e., before 2025.

Hence, it is safe to assume that a Machine Learning course will grant you a better and brighter future. And even if you don’t bet on the future, you will have plenty of amazingly high-paying jobs in the top companies in the world now.

Also read:

Top 10 Helpful GitHub Storage For Web Developers

2. Freelancing Opportunities

With a Machine Learning course at your disposal, you’re not limited to traditional jobs. You can freelance and earn money from the best employers in the world while sitting in the comfort of your home next to your loved ones.

3. Machine Learning and Future

With an increasing number of companies focusing on automating the operations and increasing the use of artificial intelligence in every section of the processes, it is easy to predict that the future for Machine Learning engineers will be secured.

Machine Learning comes with a wide range of applications for companies. From simple chat boxes on websites to complicated operations such as performing critical surgeries on humans and animals, it is helping the world and making a positive impact. It is also ensuring a well-balanced future and creating many opportunities for the engineers in the process.

4. Automation with AI and AutoML

A dynamic code that modifies and improves itself always has the edge over a static one. Machine Learning revolutionizes automation with modern solutions to the variables of business and non-business processes.

AutoML with Azure is a great starting point for learning automation. It offers great facilities for students with coding and non-coding backgrounds.

Also read:

How to choose The Perfect Domain Name

5. Infinite Learning Possibilities

There are a million things to learn about in Machine Learning. And it opens infinite learning possibilities by helping you continue learning while also working on it.

And having a powerful impact on you, your company, your country, and the world, Machine Learning brings tremendous value with it. Your curiosity is the only limit, as there are infinite things you can do with Machine Learning. The easiest way to start is with a simple Machine Learning course.

6. Eases up the Entry in Other Fields

Machine Learning also forces you to learn the core subjects such as applied maths, physics, data analytics, natural language and audio/video processing, neural networks, Python Machine Learning, and many more. These ensure a clear and fundamental understanding of computers. You can apply these in multiple fields.

Thus, if you decide to gather knowledge in other computer science fields and change career paths, the transition will be relatively easier with Machine Learning as a base. There is always a cushioning with this field as you do not need to start something else from scratch.

7. The Entry Barrier 8. The Hot Topic in The Market

If you took part or watched any smartphone or tech launch event from the last year or two, you must have been greeted with a separate section of experts talking about how they utilized AI and Machine Learning in their product. After all, AI and Machine Learning is hot topic in all the current computer science conversations!

Companies are using it to minimize unnecessary hindrances and technological limitations by leveraging Machine Learning capabilities to scale their businesses. Individuals are talking about it as it is an obvious step towards an AI-driven future. And common people are talking about it because of the stories of the machine learning professionals getting high salary jobs floating in every other day.

9. The Impact

The huge impact that Machine Learning has on the modern world is almost impossible to articulate. Have you ever wondered how YouTube recommends exactly the video you want to watch every single time? The answer is simple. YouTube does it with the help of a Machine Learning-based algorithm.

10. A Competitive Edge

Machine Learning can add to your existing skills and help you build an unignorable resume. It can complement your skills to make for a better appraisal pitch, initial pitch for a new job, elevate your experience with computers and make you a better computer scientist and programmer. With the right Machine Learning skills at your disposal, you become unstoppable with the course name stamped on your resume.

Bottom Line Saikumar Talari

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Machine Learning Course In Bangalore (33 Courses With Certification)

Course Name Online Machine Learning Course in Bangalore

Deal You get access to all 80 courses, Projects bundle. You do not need to purchase each course separately.

Hours 1500+ Video Hours

Core Coverage Machine learning using Python, Deep Learning, Data Science with R, Face Detection in Python, Bayesian Machine Learning, Business Intelligence, Artificial Intelligence, Projects on Machine learning.

Course Validity Lifetime Access

Eligibility Anyone who is serious about learning Machine Learning and wants to make a career in this Field

Pre-Requisites Familiarity with at least one programming language is recommended

What do you get? Certificate of Completion for each of the 80 courses, Projects

Certification Type Course Completion Certificates

Verifiable Certificates? Yes, you get verifiable certificates for each course with a unique link. These link can be included in your Resume/Linkedin profile to showcase your enhanced Machine Learning Skills

Type of Training Video Course – Self Paced Learning

Software Required None

System Requirement 1 GB RAM or higher

Other Requirement Speaker / Headphone

Machine Learning Course in Bangalore Curriculum

The purpose of this course to entertain everyone who wants to learn machine learning or willing to grow their career in this technology. You will be learning about statistics essential in machine learning that is considered as the main part of this technology. You will be able to understand statistics that will be leveraged to master this technology. The first unit of this Machine Learning Course in Bangalore is mainly dedicated to making you understand all the concepts of statistics that are used frequently in machine learning. The next and important topic, machine learning with Tensorflow will be covered in the second unit. You will need to learn this in order to expand the functionalities that you can bring to your machine learning project. The course will train you on how this free library can be used to represent the data flow. Tensorflow is the math library that is mainly used in machine learning applications and you will actually see how this library is used in the real application. After completing this unit you will be able to work with Tensorflow.

As this Machine Learning Course in Bangalore is primarily focused on offering hands-on practice, you will get to learn about the deep learning and major part of this module will be hands-on. You will get the opportunity to solve the problem by yourself in order to fortify your machine learning skills. All the modules covered here comprised of various examples that are scenario-based. These examples will help you to understand how things work in the real production environment. R programming language is the other crucial module of machine learning and hence we will be practicing R with the perspective of this technology. In the module machine learning with R, we will see how the programs are written in R that endorses the machine learning. To make the learning easier for you, things will be explained by the hell of easy examples and the complexity will increase as time passes.

In this Machine Learning Course in Bangalore, there are several projects that you will be supposed to understand, learn and execute. But before that, you will need to finish the last module of the unit named machine learning with Scikit. Scikit is the free library for python that is used for machine learning. In order to be the functionalities of machine learning in the application developed in python, you can leverage this free library. You will learn how to work on it so that you can find it easy to finish the projects that are included in this course.

Machine Learning Course – Certificate of Completion

What is Machine Learning?

Machine learning can be considered as a technology that enables the application to absorb the user’s input in order to enhance the functionality or the offerings of the application. This technology is comprised of several other technologies that are programming languages and the approaches to process data. This also enables the software to learn things on their own and do not entirely rely on the programmed data to generate the outcome. Programming languages like Python and R are used to implement Machine learning. Data science is the other important module covered under Machine learning and it’s also essential for the application to work with huge data. In order to implement it on any application, there are specific libraries developed to serve the purpose.

Which Skills will you learn in this Course?

This Machine Learning Course in Bangalore is intended to train the folks who are willing to master these skills required in machine learning. After completing this Machine Learning Course in Bangalore, you will be cognizant of all the skills that are considered to be proficient in machine learning. This course is comprised of the python module that you improve your hand on skills on python specific libraries that are used to implement machine learning. You will be programming in R that will improve your skills or command on that particular programming language. In addition to that, there are several other tools or technologies like Tensorflow, that you will be learning in this course. Once the course is completed, you will be able to work proficiently in this technology.


In order to learn machine learning, there are some of the technologies that can be very helpful to you. Though those are not considered mandatory when they will let you understand machine learning very easily. The first among them is python. You should be proficient in python programming language and regards programming language as these are used very frequently to develop the application that possesses the features provided by machine learning. In addition to the programming languages, you need a strong understanding of data science to endorse your learning. You will need to process the huge data that can also be possible through data science. So these are some of the technologies that are considered as a prerequisite when we talk about mastering machine learning.

Target Audience

Anyone who is willing to choose machine learning as their career or wants to learn this is the best target audience for this course. The professionals who are already working as developers in python and Regards and wants to extend the domain of your knowledge can be the target audience for this Machine Learning Course in Bangalore. They will get to learn various new things that come under the domain of machine learning. The students who are in their graduation or pursuing masters can also be the best target audience for this course. They will be learning new things and those were very different from what they learn in academics. The major focus of this course is to improve hands-on experience in machine learning and anyone who wants to master this technology can be the best target audience.

Machine Learning Course – FAQ’s Why should you take up the Machine Learning Course in Bangalore?

The IT industry is booming in India and this fact could not be belied. When we talk about It in India, Bangalore is the city that comes first in our mind. There are numerous organizations that are working on machine learning and need professionals who are cognizant of this technology. Taking this Machine Learning Course in Bangalore will not only help you to get the job but will also help you to secure good compensation. In addition to Bangalore, there are various cities that had a huge demand for professionals working in machine learning and the demand is sustained from growing.

What is the Machine Learning market trend in Bangalore? 

Bangalore has always been the best destination for job seekers who are in the IT industry and the reason was an increasing number of IT organizations. The city has always been on top in terms of opportunities and was luring professionals for a decade. With the requirement of more and more professionals, the city had welcomed more professionals in several technologies and the same happened in the case of machine learning. Since the time machine learning started to boom, the requirement for this technology witnessed exponential growth in Bangalore. In the current time, there is huge number of openings on this technology in this city and the positions are expected to grow with the passing of time.

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Career Benefits

Machine learning is the hot technology at this time and the requirements for this technology is going to increase massively in the near future. This Training will help you to understand each and every single concept of machine learning. The most important and best thing about this technology is, folks working in this usually get high compensation due to high requirements. Anyone who is a master in this technology, it is very easy for them to secure a good job. If you are looking to grow your career in one of the most demanding technology, you might want to learn machine learning. After finishing this Machine Learning Course in Bangalore, you will become eligible for all the positions that require the skilled professional who has experience in machine learning.




Ammar Khan

Awesome Course!


Rakesh Mohan Mohapatra

Introduction to Machine Learning


Meenal Kohad

Machine Learning with R


Keerthi Vasan

Introduction to Machine Learning

Great course, concepts are very well explained and so very easy to grasp and understand the general concepts or ideas revolving around machine learning algorithms. I think you can easily apply some of these skills directly to various problems, even in your personal research, and can be a great addition to your personal skills.

Ajay Ashok

10 Amazing Open Source Projects For Machine Learning Enthusiasts

This article was published as a part of the Data Science Blogathon

Open source refers to something people can modify and share because they are accessible to everyone. You can use the work in new ways, integrate it into a larger project, or find a new work based on the original. Open source promotes the free exchange of ideas within a community to build creative and technological innovations or ideas. So, programmers should consider contributing to open source projects because of the following reasons:

1. It helps you to write cleaner code.

2. You gain a better understanding of technology.

3. Contributing to open source projects helps you gain attention, popularity and can leverage your career.

4. Adding an open-source project to your resume increases its weight.

5. Improves coding skills

6. Improve Software on a User and Business Level.

Source: Google Images

To start contributing to open source projects there are some prerequisites:

1. Learn a programming language:  Since in open source contribution you need to write code to get involved in the development, you need to learn a programming language. That can be of any choice. It’s easy to learn another language at a later stage depending upon the needs of the project.

2. Get yourself familiar with Version Control Systems: These are the software tools that help in keeping all the changes in one place that are being made to recall them at a later stage if needed. Basically, they keep track of every modification done by you over time in the source code. Some popular Version Control Systems are Git, Mercurial, CVS, etc. Out of all these Git is the most popular and widely used in the industry.

Now we will look at some of the amazing Open Source Projects you can contribute to.

So, let’s get started!

1. Caliban

Source: Google Images

This is a machine learning project from tech giant Google.  It is used for developing machine learning research workflows and notebooks in an isolated and reproducible computing environment. It solves a big problem. When developers are building data science projects, it is many times difficult to build a test environment that can show your project in a real-life situation. It is not possible to predict all edge cases. So, Caliban is a potential solution for this problem. Caliban makes it easy to develop any ML models locally, run code on your machine then try out that exact same code in a Cloud environment for execution on big machines. So, Dockerized research workflows are made easy, locally as well as in the cloud.

2. Kornia

                                                               Source: Google Images

Kornia is a computer vision library for PyTorch. It is used to solve some generic computer vision problems. Kornia is built on PyTorch and depends on its efficiency and CPU power so that it can compute complex functions.  Kornia is a pack of libraries used to train neural network models and perform image transformation, image filtering, edge detection, epipolar geometry, depth estimation, etc.

3. Analytics Zoo

                                                                 Source: Google Images

Analytics Zoo is a unified data analytics and AI platform that unites TensorFlow, Keras, PyTorch, Spark, Flink, and Ray programs into an integrated pipeline. This can efficiently scale from a laptop to a large cluster to process the production of big data. This project is maintained by Intel-analytics.

Analytics Zoo helps an AI solution in the following ways:

Helps you easily prototype AI models.

Scaling is efficiently managed.

Helps to add automation processes to your ML pipeline like feature engineering, model selection, etc.

4. MLJAR Automated Machine Learning for Humans

                                                           Source: Google Images

Mljar is a platform to create prototype models and deployment services. To find the best model, Mljar searches different algorithms and performs hyper-parameters tuning. It provides interesting quick results by running all computation in the cloud and finally creating ensemble models. Then it builds a report for you from AutoML training. Isn’t this cool?

Mljar efficiently trains models for binary classification, multi-class classification, regression.

It provides two kinds of interfaces:

It can run ML models on your web browser

Provides Python wrapper over Mljar API.

The report received from Mljar contains the table with information about each model score and the time needed to train every model. Performance is shown as scatter and box plots so it’s easy to check visually which algorithms perform best amongst all. See this:


                                                            Source: Google Images

DeepDetect is a Machine Learning API and server written in C++. If you want to work with the state of art machine learning algorithms and want to integrate them into existing applications DeepDetect is for you. DeepDetect supports a wide variety of tasks like classification, segmentation, regression, object detection, autoencoders. It supports both supervised and unsupervised deep learning of images, time series, text, and some more types of data. But DeepDetect depends on external machine learning libraries like:

Deep Learning libraries: Tensorflow, Caffe2, Torch.

Gradient Boosting Library: XGBoost.

Clustering with T-SNE.

6. Dopamine

                                                               Source: Google Images

Dopamine is an open-source project from tech giant Google. It’s written in Python. It is a research framework for fast prototyping reinforcement learning algorithms.

Dopamine’s design principles are:

Easy Experiment: Dopamine makes it easy for new users to run experiments.

It is compact and reliable.

It also facilitates reproducibility in results.

It is flexible hence makes it easy for new users to try out new research ideas.

Note: Check these Colaboratory Notebooks to learn how to use Dopamine.

7. TensorFlow

                                                                 Source: Google Images

Tensorflow is the most famous, popular, and one of the best Machine Learning Open Source projects on GitHub. It is an open-source software library for numerical computation using data flow graphs. It has a very easy-to-use python interface and no unwanted interfaces in other languages to build and execute computational graphs. TensorFlow provides stable Python and C++ APIs. Tensorflow has some amazing use cases like:

In voice/sound recognition

Text Bases Applications

Image Recognition

Video Detection

…and many more!

8. PredictionIO

                                                                   Source: Google Images

It is built on top of a state-of-the-art open-source stack. This machine learning server is designed for data scientists to create predictive engines for any ML tasks. It’s some amazing features are:

It helps to quickly build and deploy an engine as a web service on production templates that are customizable.

Once deployed as a web service, respond to dynamic queries in real-time.

It supports machine learning and data processing libraries like OpenNLP, Spark MLLib.

It also simplifies data infrastructure management


                                                             Source: Google Images

It is a Python-based free software machine learning library of tools. It provides various algorithms for classification, regression, clustering algorithms including random forests, gradient boosting, DBSCAN. This is built upon SciPy that must be pre-installed so that you can use sci-kit learn. It also provides models for:

Ensemble methods

Feature extraction

Parameter tuning

Manifold learning

Feature selection

Dimensionality reduction

Pylearn2 is the most prevalent machine learning library among all Python developers.  It is based on Theano. You can use mathematical expressions to write its plugin while Theano takes or optimization and stabilization. It has some awesome features like:

A “default training algorithm” to train the model itself

Model Estimation Criteria

Score Matching



Dataset pre-processing

Contrast normalization

ZCA whitening

Patch extraction (for implementing convolution-like algorithms)

End Notes:

Contributing to open source comes with too many pros. So, these are some good open-source projects to contribute.

Thanks for reading if you reached here 🙂

Let’s connect on LinkedIn.

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Ensemble Methods In Machine Learning

Introduction to Ensemble Methods in Machine Learning

Hadoop, Data Science, Statistics & others

Types of Ensemble Methods in Machine Learning

Ensemble Methods help to create multiple models and then combine them to produce improved results, some ensemble methods are categorized into the following groups:

1. Sequential Methods

In this kind of Ensemble method, there are sequentially generated base learners in which data dependency resides. Every other data in the base learner is having some dependency on previous data. So, the previous mislabeled data are tuned based on its weight to get the performance of the overall system improved.

Example: Boosting

2. Parallel Method

In this kind of Ensemble method,  the base learner is generated in parallel order in which data dependency is not there. Every data in the base learner is generated independently.

Example: Stacking

3. Homogeneous Ensemble

Such an ensemble method is a combination of the same types of classifiers. But the dataset is different for each classifier. This will make the combined model work more precisely after the aggregation of results from each model. This type of ensemble method works with a large number of datasets. In the homogeneous method, the feature selection method is the same for different training data. It is computationally expensive.

4. Heterogeneous Ensemble

Such an ensemble method is the combination of different types of classifiers or machine learning models in which each classifier built upon the same data. Such a method works for small datasets. In heterogeneous, the feature selection method is different for the same training data. The overall result of this ensemble method is carried out by averaging all the results of each combined model.

Example: Stacking

Technical Classification of Ensemble Methods

Below are the technical classification of Ensemble Methods:

1. Bagging 2. Boosting

The boosting ensemble also combines different same type of classifier. Boosting is one of the sequential ensemble methods in which each model or classifier run based on features that will utilize by the next model. In this way, the boosting method makes out a stronger learner model from weak learner models by averaging their weights. In other words, a stronger trained model depends on the multiple weak trained models. A weak learner or a wear trained model is one that is very less correlated with true classification. But the next weak learner is slightly more correlated with true classification. The combination of such different weak learners gives a strong learner which is well-correlated with the true classification.

3. Stacking

This method also combines multiple classifications or regression techniques using a meta-classifier or meta-model. The lower levels models are trained with the complete training dataset and then the combined model is trained with the outcomes of lower-level models. Unlike boosting, each lower-level model is undergone into parallel training. The prediction from the lower level models is used as input for the next model as the training dataset and form a stack in which the top layer of the model is more trained than the bottom layer of the model. The top layer model has good prediction accuracy and they built based on lower-level models. The stack goes on increasing until the best prediction is carried out with a minimum error. The prediction of the combined model or meta-model is based on the prediction of the different weak models or lower layer models. It focuses to produce less bias model.

4. Random Forest

The random forest is slightly different from bagging as it uses deep trees that are fitted on bootstrap samples. The output of each tress is combined to reduce variance. While growing each tree, rather than generating a bootstrap sample based on observation in the dataset, we also sample the dataset based on features and use only a random subset of such a sample to build the tree. In other words, sampling of the dataset is done based on features that reduce the correlation of different outputs. The random forest is good for deciding for missing data. Random forest means random selection of a subset of a sample which reduces the chances of getting related prediction values. Each tree has a different structure. Random forest results in an increase in the bias of the forest slightly, but due to the averaging all the less related prediction from different trees the resultant variance decreases and give overall better performance.


The multi-model approach of ensemble is realized by deep learning models in which complex data have studied and processed through such different combinations of the classifier to get better prediction or classification. The prediction of each model in ensemble learning must be more uncorrelated. This will keep the bias and variance of the model as low as possible. The model will be more efficient and predict the output under minimum error. The ensemble is a supervised learning algorithm as the model is trained previously with the set of data to make the prediction. In ensemble learning, the number of component classifiers should be the same as class labels to achieve high accuracy.

Recommended Articles

This is a guide to Ensemble Methods in Machine Learning. Here we discuss the Important Types of Ensemble Methods in Machine Learning along with Technical classification. You can also go through our other suggested articles to learn more –

Personalized Medicine Through Machine Learning

This article was published as a part of the Data Science Blogathon


medicine needs to be customized according to the body system of an individual. The ongoing pandemic acts as a burning example as it has been observed that one the set of medicines like Remdisivir, Tocilizumab, etc. works for one category of patients while the same set of medicines cannot prevent another category of patients with almost similar clinical parameters from progressing to severe stage from mild or moderate condition. Personalized medicine can be a solution to this challenge as it has a more “customized” approach. It is also known as precision/individualized/customized medicine

Machine Learning (ML) is often pooled with Artificial Intelligence (AI). However, ML is a branch of AI that identifies variable patterns of data to predict or classify hidden or unseen patterns which in turn can be used for exploratory data analysis, data mining, and data modeling. The ML algorithms indicate the possibility of identifying target-based medicines based upon clinical, genomics, laboratory, nutrition, and lifestyle-related data.

Throughout this article acronyms like ML, and AI, and terminology personalized medicine would be used interchangeably with Machine Learning, and Artificial Intelligence, and precision/individualized/customized medicine. It is assumed that the readers of this article have basic knowledge of medical terminologies, python, and data science.

                                                                    Image Source: KDnuggets

Benefits of personalized medicine

1. It would reduce trial and error-based treatment decisions.

2. It would bring down the burdens associated with a condition both in terms of health and finance.

3. Patient-centric medication through the integration of multi-modal data from an individual.

4. More emphasis would be laid on preventive mode rather than the reactive mode in medicine

5. Reduction in time, and cost associated with clinical trials conducted by pharmaceuticals.

Role of Machine Learning

1. There is a scope of applying the algorithms of machine learning to the genomic datasets which would enable the delivery of personalized medicines.

2. The use of multi-modal data helps in deeper analysis of large datasets which improves the understanding of human health and disease by leaps and bound.

3. As ML is capable of identifying hidden patterns of data, many future diseases can be prevented.

4. Advancement in the field of “in silico” experimental systems would improve the efficiency of clinical trials which would reduce the time and cost associated with clinical trials. The experimental system “in silico” refers to using computers to run various experiments (Wanner, 2023).

5. Reduction of the burden on the healthcare system on screening of various diseases of seriousness like lung cancer, covid19, heart diseases, etc.

Challenges for Machine Learning in the field of Personalized Medicine

1. Optimization of application is required.

2. The knowledge base of the stakeholders which would involve physicians, laboratory technicians, data analysts, programmers, and paramedical staff has to be increased. Everyone needs to have a basic understanding of the concerned domains.

Tools of Machine Learning for Precision Medicine

There are three primary tools for machine learning algorithms. These are classification, regression, and clustering. Let’s have a look at the basic concept of each of these.

1. Classification – Logistic Regression and Naive Bayes are the most common supervised learning classification algorithms.

2. Regression – Linear Regression is the most common supervised learning regression algorithm.

3. Clustering – K-means Algorithm, Mean Shift Algorithm, and Hierarchical Clustering are the common algorithms. These are all unsupervised, i.e. target variable is not available.

4. Classification and Regression combined – Support Vector Machine (SVM), Decision Tree, Random Forest, and K-Nearest Neighbors are types of supervised ML algorithms that are applicable in both classification and regression predictive problems.

Another very important ML type is Reinforcement Learning which is applied when a categorical target variable is available as well as when no target variable is available. It has a got wide application in the area of auto-car and optimized marketing. It is a semi-supervised algorithm.

In this article, we would restrict ourselves to a few ML algorithms which are exclusively used in precision medicine.

Machine Learning and Precision Medicine in real world

The purpose of personalized medicine is to select and deliver patient-specific treatments to achieve the best possible outcome. The challenge lies in identifying an optimum treatment as the number of possible predictors of good response like genetic and other biomarkers, and the option of treatments is increasing.

In addition to this, as most clinical trials are based upon average treatment effects, similar medicines become non-responsive for some patients and responsive for some other patients.

An example in this regard is the primary analysis of the COMBINE Study which is one of the largest clinical trials regarding treatments for alcohol dependence in the USA. The study inferred that there was an impact of one of the considered pharmacological treatments (naltrexone) but was non-responsive for another, acamprosate (Tsai et al., 2023).

CART (Classification and Regression Trees) methods consider a large number of potential predictors and identify combinations of patient characteristics and good outcomes. Personalized medicine has a focus on whom a particular treatment may be more effective than that of another. The application of a modified tree-based approach indicates the possibility of selection of the best individualized treatment based on baseline features (Tsai et al., 2023).

                                                                    Image Source: Tsai et al., 2023

The approach of modern-day medicine is based upon a population-wide model which is intended to be applied to the overall population and is optimized to have decent predictive performance on an average number of people out of that population. This approach has done remarkably well for decades but it ignores individual differences in treatment responses.

A better approach for capturing individual differences in treatment responses is the patient-specific modeling approach. The personalized decision tree model is a patient-specific modeling approach that performed a bit better than the CART method (Adam, & Aliferis, 2023).

Gini impurity, information entropy, and variance reduction are 3 important metrics for decision tree algorithm. Gini impurity is the more preferred metric among the 3 metrics. It measures how a randomly chosen element is incorrectly labeled.

Criteria for constructing a tree

2. The preferred split between the 2 child nodes would be the one in which Gini impurity is higher would be split further.

3. Depending upon the complexity of parameters, the exploration of nodes is discontinued.

Packages and tools for precision medicine 1. Scikit-learn –

One of the most important tools for ML. It is an open-source library that aids in both supervised as well as unsupervised learning. From Scikit-learn various estimators or predictors are imported to model a particular dataset. Scikit provides us numerous models and ML algorithms. A few of them are

from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import LogisticRegression from chúng tôi import DecisionTreeClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score, confusion_matrix 2. pyGeno – pip install pyGeno from pyGeno.Genome import * print(

Now, to build a personalized genome, we need to select the type of data we are interested in from the BioMart database. After selecting a database, we have to select a dataset that would be followed by filtration of the query. Then, we need to select BioMart attributes, by default it would be “Ensembl Gene ID” and “Ensembl Transcript ID”. Finally, the query will be displayed and retrieved. The details have been illustrated in the image below

Importing the whole genome is an uphill task requiring 3 GB memory. In this scenario, the bootstrap modules and data wrap are handy tools.

import pyGeno.bootstrap as B B.printRemoteDatawraps()

A snapshot of the same has been provided below. It is to be noted that the file type is GZ so it has to be extracted with “Archive extractor online” or any other good extraction tools.

from pyGeno.Genome import * g = Genome(name = "GRCh37.75") prot = g.get(Protein, id = 'ENSP00000438917')[5] print (prot.sequence) print (prot.gene.biotype)

In the above lines, we are trying to extract protein sequence and gene biotype which would act as a reference set and would be used to create a personalized genome.

dummy = Genome(name = 'GRCh37.75', SNPs = 'dummySRY') dummy = Genome(name = 'GRCh37.75', SNPs = 'dummySRY', SNPFilter = myFilter()) dummy = Genome(name = 'GRCh37.75', SNPs = ['dummySRY', 'anotherSet'], SNPFilter = myFilter())

Above are steps for creating a personalized genome. It allows clinicians to work on the genomes and proteomes of patients. The entire working mechanism of pyGeno in the field of precision medicine can be seen in the image below.

                                      Image Source: Daouda, Perreault, & Lemieuxb, 2023

Machine Learning, Precision Medicine, and ongoing Pandemic

In the ongoing pandemic, deciding upon the proper line of treatment for clinicians has become an enormous challenge. The clinicians are confused about the efficacy of remdisivir and corticosteroid on covid19 patients. ML algorithm can make a breakthrough in this area.

Lam et al. (2023) put forth that to evaluate the performance of corticosteroid versus remdesivir on identifying patients with longer survival times, Gradient-boosted decision-tree models were used. The models were trained and tested on data from 10 hospitals in the US on COVID-19  adult patients (age ≥18 years). Significant findings in treated and nontreated patients were based upon Fine and Gray proportional-hazards models.

The sample size was 2364 where 893 patients were treated with remdesivir, and 1471 were treated with a corticosteroid. The confounding was adjusted and it was found that in the populations identified by the algorithms, both corticosteroids and remdesivir were significantly associated with an increase in survival time, with hazard ratios of 0.56 and 0.40, respectively (both, P = 0.04). This contradicted the finding that neither corticosteroids nor remdesivir use were associated with increased survival time (Lam et al., 2023). This indicates that the ML algorithm holds promise in this field.


ML algorithms can identify at present which set of Covid19 patients would require Remdisivir and which set of patients would require corticosteroid so that the patient health outcome is improved. The algorithms can further be expanded into other areas of medicine as well. Nowadays, big biomedical data are in abundance, the need of the hour is to leverage these data for research in the field of medicine, public health, and biomedical procedures so that more and more people can be cured of serious diseases.

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