The actual trick to get free access to Andrew Ng Machine Learning on Coursera (2023)

In a world enthralled by the potential of AI and machine learning, the need to understand these complex concepts is paramount. With applications spanning from ChatGPT to DALL-E, Midjourney, and more, machine learning is indeed a vital ingredient in the recipe of modern IT solutions. If you’re aspiring to understand the crux of machine learning, Andrew Ng’s courses on Coursera remain a top-tier resource for honing your skills.

These courses, while immensely beneficial, are generally accessed with a monthly fee on Coursera. However, what if we told you that it’s possible to absorb this well of knowledge without spending a dime? Although the free access doesn’t allow you to earn official credits or complete labs, it opens up a treasure trove of lectures and a plethora of other materials.

For self-paced learning without financial commitments, auditing Andrew Ng’s machine learning course is an excellent choice. Bear in mind that the full courses—with all their features—are absolutely worth the investment if it aligns with your career goals and personal growth.

Access Andrew Ng’s Coursera Machine Learning for free with these simple steps:

1. Navigate to the Machine Learning Specialization page
2. Scroll down to explore the individual courses under this specialization.
3. Click on the desired course title. This is important and often missed - you audit courses, not specializations!
4. Once you’re on the course page, find the “Enroll for Free” button and click it.
5. A 7-day Free Trial modal will pop up. Instead of choosing the free trial, click on the “Audit the course” link.

Voila! You’ve now accessed Andrew Ng’s Coursera Machine Learning for free, ready to dive into the riveting world of artificial intelligence and machine learning. Happy learning!

If machine learning is your cup of tea, you might also be interested the following books either as a supplement to the course or as a standalone resource:

1. ”Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron
   A great book for those new to machine learning but with some coding experience. Géron provides practical examples and clear explanations of the theory and algorithms you’ll encounter in machine learning. This is a hands-on guide that will teach you how to implement machine learning models using popular libraries in Python.
2. “Pattern Recognition and Machine Learning” by Christopher M. Bishop
   This is a more advanced, detailed, and theoretical book. Bishop covers both supervised and unsupervised learning and goes in-depth into Bayesian models, neural networks, and support vector machines. If you’re looking for a deep understanding of the theory behind machine learning, this is the book for you.
3. “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
   This book is widely considered the definitive guide to deep learning, written by top researchers in the field. Covering everything from the basics to the cutting edge of deep learning, it’s an essential resource for anyone looking to understand the field deeply. It is quite technical and best suited for those with a solid foundation in calculus and linear algebra.

Remember, knowledge is invaluable, and Andrew Ng’s Machine Learning course offers a deep well of it. Whether you choose to audit the course for free or invest in the complete learning experience, you’re taking a significant step in expanding your understanding of one of today’s most influential technology fields.

The tip above is not specific to just Andrew Ng’s ML course. You can audit any course on Coursera for free using the same method — this includes Andrew’s Deep Learning specialization courses.