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If you've reached this article, you may still be feeling unsure about applying your machine learning knowledge, and that's totally understandable.
In our modern society, continuous learning is the only constant. That’s why, following the rise of ai and ML, more and more people want to improve their skills and increase their confidence in these areas.
Whether you are a non-techie or have a technical background, gaining a deeper understanding of ai and ML will be of great benefit to you.
The main problem?
There are so many machine learning resources out there that it can be difficult to find the ones that are relevant and high-quality. That’s why, in this article, I’ll share my favorite machine learning courses from top universities.
1. Generative ai for Everyone by DeepLearning.ai
The first course had to be dedicated to the buzzword of the year: ai and LLM. Designed by DeepLearning.ai and taught by Andrew Ng, “Generative artificial intelligence for Everyone” is a great way to get started with GenAI, even without any prior knowledge in the field.
The course aims to be clear and easy to learn GenAI, and will guide you through how generative ai works and what it can (and can't) do.
It includes practical assignments where you will learn how to use generative ai to help you in your daily work and receive tips to improve your teaching and get the most out of the LLM. In addition, you will delve into real-world applications and learn common use cases.
Upon completion, you will understand the concepts of large language models, deep learning, and generative ai skills. You will be able to put your knowledge into practice and better understand the impact of ai on business and society based on the three core elements of today's machine learning world.
You will also learn how to apply generative ai to everyday tasks, making it immediately practical and useful. ai/courses/generative-ai-for-everyone/” target=”_blank” rel=”noopener”>The course is available for free at Deeplearning.ai.
2. CS229: Stanford Machine Learning
As a second option, I recommend a classic, but still one of the best free ML courses out there. There are many versions and instructors, but as a personal recommendation, I would choose the ones taught by Andre Ng, widely regarded as one of the best machine learning instructors.
It offers an easy-to-follow introduction to machine learning and statistical pattern recognition, covering a variety of topics such as supervised learning, unsupervised learning, learning theory, reinforcement learning, and control. It starts from the basics and ends with advanced concepts. This course is perfect for anyone looking to gain a solid foundation in machine learning and end up with deep domain knowledge.
You can find all the material in the following link and their corresponding YouTube videos below.
3. Machine Learning with Python from MIT
If you're looking to master ML with Python, a good option is to take the MIT course specially designed with this specific goal in mind. It provides a comprehensive introduction to ML algorithms and models, including deep learning and reinforcement learning, all through practical Python projects.
If you're new to the field, choosing a specific subdomain can be overwhelming. A better way to understand the full and diverse world of machine learning is to start with a course that covers most of it. This way, you'll have the chance to figure out what excites you the most. This course is perfect for beginners looking to explore the entire diverse world of machine learning.
technology-machine-learning-with-python-from-linear-models-to-deep-learning” target=”_blank” rel=”noopener”> You can find the course at the following link
4. Mathematics for Machine Learning, from Imperial College London
If you're scared of maths, it's time to face it. Imperial College London has designed a course that aims to teach a basic skill for anyone who wants to build a career in machine learning.
Mathematics is fundamental to machine learning, and understanding mathematical principles is crucial to interpreting the results produced by machine learning algorithms. This specialization includes three courses:
- Linear algebra
- Multivariate calculus
- Principal component analysis
Each course lasts 4-6 weeks and covers the fundamental mathematical concepts needed to understand machine learning algorithms.
You can find the course videos for free on YouTube
5. Practical Deep Learning from fast.ai
This free course is designed for people with some coding experience who want to apply deep learning and ML to practical problems. Developed by fast.ai, this course aims to help people become industry-ready ai developers. It covers fundamental topics in computer vision and natural language processing, among others, through a project-based approach that progresses from basic to advanced concepts.
Its main scope of application is based on:
- Building and training deep learning models for computer vision, natural language processing, tabular analysis, and collaborative filtering.
- Creating random forests and regression models.
- Implementation of models.
- Using PyTorch, the world's fastest growing deep learning library, along with popular libraries like fastai and Hugging Face.
You can find the course ai/” target=”_blank” rel=”noopener”>on the following website.
Ending up
In short, there are plenty of resources to get started with machine learning and improve your current knowledge. Whether you are a beginner or someone with some coding experience, these courses offer a comprehensive introduction to the field, starting with basic topics and ending with complex topics.
Josep Ferrer Josep is an analytical engineer from Barcelona. He graduated in physical engineering and currently works in the field of data science applied to human mobility. He is a part-time content creator focused on data science and technology. Josep writes about everything related to ai and covers the application of the current explosion in this field.
