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7 Top Coursera Machine Learning Courses to Improve Your Programming Skills!

Coursera Machine Learning courses are considered the absolute best but sorting out the perfect courses among them is the real deal.

Coursera Machine Learning courses are considered the absolute best but sorting out the perfect courses among them is the real deal.

Key Points

  • Machine Learning suggests ideas and concepts which enable the implantation and design of such algorithms which are able to make predictions on the basis of the data being fed to them rather than the conventional sort of programming.
  • Andrew Ng’s courses on Machine Learning can be of great help if you are looking forward to progress in the professional domain.
  • Taking a specialized course in Python can assist you well to interpret the advanced systems which are functional and about to get deployed in the real world.
  • Focusing on real-life issues for the development of advanced algorithms and API designs can aid you a lot to enhance your skills as a Machine Learning Specialist.

In this article, we are going to curate the top 7 best Coursera Machine Learning courses which have made waves among the learners’ circle because of their content specificity and scope.

What is Machine Learning?

Just as the name indicates, it is a work of wonder through the efforts of multiple programmers. It is an unconventional approach of programmers to develop a way by which the computers can learn from the data which is being fed to them rather than by the development of mainstream programs for doing so.

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You can put forward this in a way that aims to devise algorithms that are quick to interpret data instructions and based on the data received by them, they conveniently evolve and become more and more accurate in their predictions over time. This approach is introduced to save large-scale data systems which are at risk of hacking due to the intrinsic programming of the computers storing that data set. However, with the development of algorithms that are capable of thinking and making amendments, it becomes simple and secure to protect against forgery and achieve more data accuracy because of the intelligence depicted by them.

7 Best Coursera Machine Learning Courses

Right in 2012, when the foundation of Coursera was laid down, one of the genius minds behind this project introduced the complex terminologies and data systems associated with Machine Learning before aspiring learners. Andrew Ng’s approach to simplifying the complex concepts of Machine Learning became highly popular and this eventually led him to constitute Deep Learning, which is a project launched exclusively for the sake of encouraging young programmers and software engineers to learn and deliver the best of their knowledge. Coursera paved a gateway between efficient learners and such organizations which have initiated the task of developing these concepts. So, this year we have curated 7 best Coursera Machine Learning courses that can aid you a lot in taking the next big leap of your career.

Advanced Learning Algorithms

Algorithms are the building blocks of every Machine Learning project. If you are an active mind to unravel the mysteries of Machine Learning, it’s very important that you first learn to build your strategy through advanced training. This course is suitable for those who are seeking specialization in this complex domain. From the mind of the tech genius. Andrew Ng, this course clears out the multi-class classification module for you by using the aid of TensorFlow.

The course pattern is systemic and initially, you are able to master the skills of neural networking. In the next stage, using the concepts of TensorFlow, you can learn to work on decision trees and find out the best tree ensemble methods. Andrew Ng’s courses have the end goal of being applicable in the real world, so with the course, you are able to tackle and develop integrated systems that intelligently focus on real-world concerns and provide a solution. The course learning phase is arranged in a cyclical pattern where you can master both the art of supervised and unsupervised learning.

Supervised Machine Learning: Regression and Classification

Many aspiring programmers believe that the concept of supervised Machine Learning is comparatively more terse and technical as compared to unsupervised learning beliefs. Andrew Ng has exclusively worked on this course where the prime concepts of regression including logistic regression, neural networking, dimensionality reduction, and recommender systems are explained with a chief focus on real-world problems.

The star concept of this course is the influence on learning with the implementation of learning libraries like NumPy and Scikit-learning. The base of the learning is Python and a data-centric approach is what makes this course ideal for learning advanced the art of building advanced technology models. Alongside the learning libraries, neural networking is wrapped up to promote the AI-building models and fundamentals of the machine world.

Machine Learning Specialization

This individual course is divided into a 3-course phase where each and every phase has something different to offer. This course is exclusively meant for specialists who want to attain a wider approach to the presence of already existing concepts. The initial model is built on the idea of supervised learning where concepts like logistic regression and binary classification processes are common.

During the second phase, you get to see an overview of the digital libraries upon which the complex ideas of Machine Learning are standing. Yes, you perceived it rightly. The presence of NumPy and Scikit helps the young learners to take the social problems in context and then derive their solutions by employing the strategies of advanced learning Inc models. The end phase is all about the mechanics of unsupervised learning where you get the opportunity to develop recommender systems using the aid of case studies. Again Andrew Ng is the face behind this specialization which means you can expect some interesting twists throughout the course.

Custom Models, Layers, and Loss Functions with Terse Flow

Building a commercial API is really child’s play. Don’t believe our word? Well, Professor Laurence Moroney believes that and his belief seems really valid when you view the extensive course outline of this course. Starting from a comparison between the conventional Functional API and Sequential API, you learn to build newer models using the very popular Functional APIs. This course is very significant in exploring the implementation, development, and real-world usage of Siamese networking.

You are able to come up with models deriving multiple outputs through Siamese networking in association with the earlier used Implementation networking. The most popular aspects of this course revolve around TerseFlow and ReSet networking, where respective model classes suggest the significance and potential use in terms of large-scale development networking. This particular course is a specialized course but if you are just starting to build your expertise in neural networking and the API development domain, then you can definitely go for this one.

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Mathematics for Machine Learning Specialization

Powered by The Imperial College London, this course serves as a model for advanced Machine Learning strategies. Divided into certain phases, you find a relation between mathematics and real-world data systems. Of course, the hero of this course is linear algebra, so there are few introductory classes signifying the interpersonal relationship between advanced data systems and their dependence on the principles of linear algebra.

Taking a round of basic mathematical concepts, you finally explore the complex principles associated with Multivariate Calculus. In this course, you are given an introduction to Calculus and then you explore the matrices and advanced concepts of founding data systems. The final course deals with the Principal Component Analysis where you learn to compress high-ordered data structures and of course, it uses pre-existing knowledge about Dimensionality Reduction and Python as you will be exclusively dealing with these two concepts throughout the conversion and development.

Machine Learning Engineering for Production (MLops) Specialization

This is a specialized course for intermediate software engineers. You learn about product development, prototype construction, and deployment and understand the tactics behind concept drift. The introduction involves an understanding of the API integration using which you are able to construct various commercial model baselines.

As you move forward, getting an idea about the metadata tools and dataset validation becomes compulsory for you. You are able to learn the mechanics of building data-gathering pipelines through these strategies. The end goal is to develop a fully functional productive model focusing on real-world convenience. Your goal is to maintain the standard and monitor its continuous smooth functioning alongside improvising modeled datasets with time and introducing better metadata tools for effective integration and application. This specific course is significant if you want to adapt to app development on a permanent basis.

Introduction to Data Science in Python

Python serves as an elixir for programmers. Especially, if you want to master the art of Machine Learning, you have to gain an unflinching command of Python. The reason is simple. Without Python, you can never learn to unravel the modern concepts of data integration and deployment. This course is exclusively built to impart the concepts of Python for large-scale product design and development. Professor Christie Brooks from The University of Michigan teaches you different facets of Python.

As you are introducing yourself, you are able to learn about the chief Python programming techniques and Lambda design. Alongside, you are also able to learn and manipulate the CSV files which are the core of this technology. The star idea of this course is the introduction of the much acclaimed Python Pandas Data Science library and the NumPy library where you can build and manipulate different systems and models. Taking this particular course can make other courses in Machine Learning easier for you because here you find the basic to advanced level strategies involved in mastering Python which later on helps you in comprehending the advanced algorithmic patterns associated with Machine Learning.

Frequently Asked Questions

Can I take two of these Coursera courses at the same time?

Yes, you can since flexible timings are one of the greatest features offered by Coursera. So, if it seems okay then you can take both courses at the same time. However, from a learning perspective, this isn’t suggested especially if you are learning a beginner’s course on one hand and then making up your mind to take an advanced course. Actually, many of the concepts offered are quite complex and can take time to be fully comprehended, so it’s preferable that you take up these courses in a fixed order; one after the other rather than taking both at the same time.

Do all these Coursera courses cost the same?

No, the pricing of the courses is based on the level of difficulty and the course content offered by every single course. Of course, some of the courses will be more expensive than the other ones but the good part is that you will also be able to get a more in-depth understanding of the concept and of course financial aid is always there for your assistance.

Are specialized Coursera courses on Machine Learning better than beginner courses?

Though specialized courses are more detailed and interactive, these can only be interpreted if you have a fine idea about the major concepts employed in Machine Learning. So, it is advised to first take the beginners’ courses on the platform and then you can always seek more help from the specialized courses.


Coursera’s multiple courses on Machine Learning have made it very convenient for aspiring developers and software engineers to find out the best course suiting their interests. Since most of these courses are aimed to give a perfect and flawless understanding of the multiple concepts involving Machine Learning. Therefore, it can be a great idea to rely on Coursera Machine Learning Courses as a starting point because you are not just able to view the works of pros in the field, but the strategies offered by these courses aim to explore real-life problems and these aids you to develop systems while focussing on actual systems and settings.



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