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We all know looking for the right course can be such a chore. The team at KDnuggets have come together to put this blog for you so you don’t have to do the work – we’ve done it for you!
The world is moving fast, real fast. The use of technology in the year 2024 has shown the world how it can improve our workflows, healthcare industry, financial sector and more. You’re probably wondering or have had thoughts of being part of this community.
If you’re looking to enter the machine learning industry and are ready to take the next steps to become qualified. Continue reading.
Machine Learning Specialization
Link: Machine Learning Specialization
Level: Beginner
Duration: 2 months at 10 hours a week
A 3-course program by AI visionary Andrew Ng, a program designed to help course takers master fundamental AI concepts and develop practical machine learning (ML) skills, such as building and training machine learning models.
You will learn how to build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks). You will also learn how to build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods.
Apply these best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection and then go on to build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model.
IBM Machine Learning Professional Certificate
Link: IBM Machine Learning Professional Certificate
Level: Intermediate
Duration: 3 months, 10 hours a week
IBM’s online, six-course educational program equips course takers with practical ML skills, such as supervised learning, unsupervised learning, neural networks, and deep learning. Upon completing the program’s six courses, you will be awarded a professional certificate from IBM and Coursera.
In this course, you will learn how to master the most up-to-date practical skills and knowledge machine learning experts use in their daily roles, learn how to compare and contrast different machine learning algorithms by creating recommender systems in Python and develop a working knowledge of KNN, PCA, and non-negative matrix collaborative filtering. You will also predict course ratings by training a neural network and constructing regression and classification models.
Google Professional Machine Learning Engineer Certification
Link: Google Professional Machine Learning Engineer Certification
Level: Beginner
Duration: 2 hours (approximately)
Designing, building and producing machine learning models using Google Cloud. In this course, you will handle large, complex datasets and create repeatable, reusable code. You will also consider responsible AI and fairness throughout the ML model development process, and collaborate closely with other job roles to ensure long-term success of ML-based applications.
In order to earn the certification, you must take and pass a two-hour exam consisting of 50-60 multiple-choice questions covering such topics as framing ML problems, architecting ML solutions, and developing ML models.
Machine Learning Specialization
Link: Machine Learning Specialization
Level: Intermediate
Duration: 2 months at 10 hours a week
A machine learning specialisation course offered by The University of Washington, a four-course online educational program covering the major areas of ML, including prediction, classification, clustering, and information retrieval. Through the course, you’ll also analyze large and complex datasets, create systems that adapt and improve over time and build intelligent applications that can make predictions from data.
Once you have completed the course, you will receive a shareable certificate that you can share on your resume to signal your knowledge and skill set to potential employers.
End-to-End Machine Learning
Link: End-to-End Machine Learning
Level: Intermediate
Duration: 4 hours
If you are interested in how the machine learning model process works, from start to finish – check out this course provided by DataCamps.
Dive into how to design, train, and deploy end-to-end models with this comprehensive course. Through engaging, real-world examples and hands-on exercises, you’ll learn to tackle complex data problems and build powerful ML models. By the end of this course, you’ll be equipped with the skills needed to create, monitor, and maintain high-performing models that deliver actionable insights.
Wrapping Up
Up-skill at a fraction of the price of university fees with these top machine learning courses. Education and elevation do not have to be expensive – you just need to ensure that you are taking the right course for you!
Nisha Arya is a data scientist, freelance technical writer, and an editor and community manager for KDnuggets. She is particularly interested in providing data science career advice or tutorials and theory-based knowledge around data science. Nisha covers a wide range of topics and wishes to explore the different ways artificial intelligence can benefit the longevity of human life. A keen learner, Nisha seeks to broaden her tech knowledge and writing skills, while helping guide others.
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