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The last quarter of the year is when people come alive. You have your final push to achieve your yearly goals so that you can hit your 2024 goals. Whether it’s starting a new career in the tech industry or developing your current skills, self-development is important.
The continuous improvement in technology is causing there to be a rush to get into the industry. People from all walks of life want to get involved. The aim of the blog is to provide you with a list of excellent FREE courses that you can take to help you get there. I will break it down into sections by topic to make it easier for you to navigate towards your area of focus.
These free courses are all available on YouTube, making it feel like you are enrolled on an actual course. It is difficult to find the right content on YouTube because there’s so much of it! Hopefully, this article makes your search easier, so let’s get into it.
1. Introduction to Machine Learning, 2020/21
Link: Introduction to Machine Learning, Dmitry Kobak, 2020/21
2. Stanford CS229: Machine Learning
Link: Stanford CS229: Machine Learning Full Course taught by Andrew Ng
3. Cornell Tech CS 5787: Applied Machine Learning
Link: Applied Machine Learning (Cornell Tech CS 5787, Fall 2020)
4. Making Friends with Machine Learning
Link: Making Friends with Machine Learning, Cassie Kozyrkov
5. Foundation Models
Link: Foundation Models
1. Statistical Machine Learning
Link: Statistical Machine Learning
Beginners:
1. MIT 6.S191: Introduction to Deep Learning
Link: Introduction to Deep Learning
2. CMU Introduction to Deep Learning
Link: Introduction to Deep Learning: 11785 Spring 2023 Lectures
3. MIT: Introduction to Deep Learning
Link: Introduction to Deep Learning
4. Neural Networks: Zero to Hero
Link: Neural Networks: Zero to Hero
5. Foundations of Deep RL
Link: Foundations of Deep RL
Intermediate:
1. Stanford CS230: Deep Learning
Link: Stanford CS230: Deep Learning, Autumn 2018
2. Stanford CS25 – Transformers United
Link: Transformers United
3. MIT 6.S192: Deep Learning for Art, Aesthetics, and Creativity
Link: Deep Learning for Art, Aesthetics, and Creativity
4. CS 285: Deep Reinforcement Learning
Link: Deep Reinforcement Learning
5. Stanford: Reinforcement Learning
Link: Reinforcement Learning
6. Berkeley: Deep Unsupervised Learning
Link: Deep Unsupervised Learning, Spring 2020
7. NYU Deep Learning
Link: Deep Learning SP21
8. Full Stack Deep Learning
Link: Full Stack Deep Learning 2021
9. Deep Learning for Computer Vision
Link: Deep Learning for Computer Vision
1. Hugging Face Course: NLP
Link: NLP: Hugging Face Course
2. Stanford CS224U: Natural Language Understanding
Link: Natural Language Understanding
3. CMU Advanced NLP
Link: Advanced NLP, 2022
4. CMU Multilingual NLP
Link: Multilingual NLP
5. UMass CS685: Advanced Natural Language Processing
Link: Advanced Natural Language Processing
1. Practical Deep Learning for Coders
Link: Practical Deep Learning for Coders
2. Machine Learning Engineering for Production (MLOps)
Link: Machine Learning Engineering for Production
And that’s it!
As I mentioned before, there are a lot of courses out there and it can be difficult to stick to one. There may be a particular lecturer’s voice you prefer over another or the way a lecturer presents. There are so many things you take into consideration.
I have provided an extensive list in each section to help you choose which one you prefer and can continue your learning with.
Hope this list has helped you. And if you know of any good resources, please drop them in the comments to share with the learning community – thank you!
Happy Learning!
Nisha Arya is a Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.
