List of top Free AI Courses…
Artificial intelligence (AI) is one of the most exciting and rapidly evolving fields of technology today. AI is transforming various domains such as healthcare, education, entertainment, business, and more. If you want to learn more about AI and how to apply it in real-world scenarios, you might be wondering where to start and what to study.
Fortunately, there are many online courses that can help you learn the basics and advanced concepts of AI from the comfort of your home. Whether you are a beginner or an expert, you can find a course that suits your level and interests. In this article, we will guide you through some of the best AI courses that are offered by reputable universities and industry leaders. These courses cover topics such as natural language processing, computer vision, deep learning, reinforcement learning, probabilistic graphical models, neural networks, and more.
Stanford University AI Courses
Stanford University is well known for being one of the world’s leading research and teaching institutions and providing insightful courses. You can access Stanford Online Artificial Intelligence courses from the Stanford Online website or by visiting the YouTube playlists of each course. Some of the courses are:
- CS221 – Artificial Intelligence: Principles and Techniques: This course provides a broad introduction to AI and its applications. You will learn about search algorithms, knowledge representation, planning, reasoning, machine learning, natural language processing, computer vision, robotics, and more.
- CS224n – Natural Language Processing with Deep Learning: This course focuses on how to use deep learning methods to solve natural language processing problems such as sentiment analysis, machine translation, question answering, text summarization, etc.
- CS231n – Convolutional Neural Networks for Visual Recognition: This course teaches you how to build and train convolutional neural networks (CNNs) for various computer vision tasks such as image classification, object detection, face recognition, etc.
Carnegie Mellon University AI Courses
Carnegie Mellon University aims to create problem solvers, drivers of innovation and pioneers in technology and the arts. They provide a good range of courses that can help you kickstart and elevate your career in artificial intelligence. With the developments in Large Language Models (LLMs) and NLP’s role, below is a list of courses that will help you better understand the theory and building of LLMs. You can access these courses from the Carnegie Mellon University website or by visiting the YouTube playlists of each course. Some of the courses are:
- CS/LTI 11-711: Advanced NLP : This course covers advanced topics in natural language processing such as syntactic parsing, semantic analysis, discourse modeling, information extraction, text generation, etc.
- CS/LTI 11-747: Neural Networks for NLP : This course introduces you to the theory and practice of using neural networks for natural language processing. You will learn how to design and implement neural network models for various NLP tasks such as word embeddings, language modeling, sequence labeling, sequence-to-sequence modeling, etc.
- CS/LTI 11-785: Introduction to Deep Learning : This course provides a comprehensive overview of deep learning methods and applications. You will learn about feedforward networks, convolutional networks, recurrent networks, attention mechanisms, generative models, adversarial training, etc.
Massachusetts Institute of Technology AI Courses
Another well-known university that focuses on private research and the advancement of knowledge and education in areas such as science and technology is Massachusetts Institute of Technology (MIT). These courses have a heavier focus on deep learning. You can access these courses from the [MIT OpenCourseWare website] or by visiting the [YouTube playlists] of each course. Some of the courses are:
- 6.S191 – Introduction to Deep Learning : This course introduces you to the fundamentals and applications of deep learning. You will learn about neural network architectures, optimization techniques, regularization methods,