Machine learning has emerged as an important skill for a plethora of professionals and enthusiasts in recent years. Understanding the requirement, Coursera has emerged as a popular online learning platform and is offering a range of courses to help individuals deepen their understanding of machine learning concepts and its applications as well. Let us explore the top five trending machine learning courses on Coursera here.

Machine Learning Specialization by Andrew Ng

The Machine Learning Specialization by Andrew Ng is one of the most popular courses in the machine learning segment. It is being offered by DeepLearning.AI in collaboration with Stanford University. The course is basically designed for beginners and priced at $49 per month. It is a three-month course if nine hours a week is dedicated.

It is following a practical approach towards machine learning teaching and covers the fundamentals like supervised learning and unsupervised learning. The ability of Andrew Ng to simplify complex topics like linear regression, classification and neural networks makes the course accessible to a broad audience. Learners gain a solid foundation in machine learning concepts by the end of the specialization and thereafter can immediately apply to real-world challenges such as spam detection, image recognition and credit scoring.

The specialization consists of three courses. These are Supervised Machine Learning: Regression and Classification, Structuring Machine Learning Projects as well as Machine Learning Engineering for Production (MLOps). However, it may not be for such learners who are looking for an in-depth focus on the mathematical aspects of machine learning.

Machine Learning Engineering for Production (MLOps) Specialization

Machine learning is gradually becoming more integrated into business processes and knowing how to deploy models into production is an important skill. The Machine Learning Engineering for Production (MLOps) Specialization course is being offered by Andrew Ng and DeepLearning. It is priced at $49 per month can be completed in four months with five hours of weekly study.

The course goes beyond the theoretical world of machine learning and focuses on practical aspects of model deployment. Many data scientists complete bootcamps with strong coding skills but lack the knowledge to take their models from Jupyter notebooks into live production environments. The course teaches how to manage the entire machine learning lifecycle like from data collection and feature engineering to model deployment and monitoring. The primary focus is on using TensorFlow Extended (TFX) tools as it is widely used in the industry.

Students also learn about advanced topics such as handling time-series data, active learning and semi-supervised learning. Learners get a competitive advantage in the job market after completing the course. They are therefore equipped with the required skills to deploy and manage machine learning models in production.

Deep Learning Specialization by Andrew Ng

The Deep Learning Specialization by Andrew Ng is a popular course and offered by DeepLearning.AI. It costs $49 per month and takes about five months to complete with eleven hours of study a week.

Deep learning is basically a subset of machine learning and powers many AI-driven applications such as image recognition, natural language processing and self-driving cars.

Learners gain a thorough understanding of how to build, train and optimize deep learning models through the course. It is perfect for those who are looking ahead to take their machine learning skills to the next level and mainly in segments like AI and neural networks.

Introduction to TensorFlow for Artificial Intelligence, Machine Learning and Deep Learning

TensorFlow is a leading open-source platform for machine learning and this course is collectively being offered by DeepLearning.AI and Google Cloud. It is priced at $49 per month and can be completed in just one month with five hours of study a week.

Even though the course is said to be beginner-friendly, it is suggested to have some prior knowledge of Python programming for better understand.

IBM Machine Learning Professional Certificate

The IBM Machine Learning Professional Certificate is a renowned course that covers a wide range of machine learning topics. It is offered by IBM and mainly designed for beginners who want to build a strong foundation in machine learning concepts as well as techniques. The course costs $39 per month and can be completed in six months with an average of four hours of study a week.

It mainly focuses on practical applications of machine learning in business settings. Learners are exposed to real-world case studies and hands-on projects.

The course also introduces students to key tools and platforms such as Watson Studio and Scikit-learn. Hence, the students get practical experience with some of the most widely used technologies in the segment.

FAQ

What is machine learning?

Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without being explicitly programmed.

Do I need programming skills to learn machine learning?

Yes, basic programming skills, especially in Python, are helpful for learning machine learning.

How long does it take to complete a machine learning course?

It depends on the course, but most take between 1 to 6 months to complete.

Can beginners take machine learning courses?

Yes, many courses are designed for beginners and do not require prior experience required.

What are the key concepts covered in machine learning courses?

Key concepts include supervised learning, unsupervised learning, regression, classification and neural networks.

Do machine learning courses offer hands-on practice?

Yes, most courses include practical exercises using tools like Jupyter Notebooks to apply what you have learned.

Is there a demand for machine learning skills in the job market?

Yes, machine learning professionals are in high demand across various industries such as tech, finance and healthcare.

What is the difference between machine learning and deep learning?

Machine learning is a broad field of AI, while deep learning is a subset that uses neural networks to model complex patterns.

Can I learn machine learning without a strong math background?

Basic knowledge of math, especially in areas like calculus and linear algebra, is useful, but not always mandatory.

What tools are commonly used in machine learning?

Popular tools include Python, TensorFlow, Scikit-learn and PyTorch for building and deploying machine learning models.