A comprehensive collection of free resources for learning machine learning (ML) and artificial intelligence (AI) has been made available, catering to both beginners and seasoned professionals looking to deepen their expertise.
One of the standout offerings comes from TensorFlow, Google's open-source library for machine learning. This resource features an extensive array of educational materials, including courses, books, and tutorials. Newcomers can kick off their journey with introductory courses like "Machine Learning Basics" or delve into advanced topics such as "Theoretical and Advanced Machine Learning." Although the website offers auto-translation to Russian, all materials are primarily in English, ensuring a rich learning experience with practical assignments on linear algebra and deep learning.
Additionally, a self-study roadmap is accessible, providing a curated selection of resources that cover various aspects of ML and AI. This evolving collection includes specialized content on niche applications like natural language processing and artistic endeavors. Learners can craft their personalized educational path, combining foundational Python courses with insights into Generative AI and Data Science.
For those seeking unique perspectives, Segun Akinyemi's compilation of rare tutorials and articles stands out. This resource highlights unconventional methods, such as training models through Excel, and offers insights into AI agents—a trending technology with limited training materials available.
A notable course, "Elements of AI," features two parts aimed at beginners and those with some knowledge of AI. The first part covers the basics of neural networks and machine learning applications, while the second dives deeper into the algorithms powering AI, requiring foundational Python skills.
For Russian speakers, the "Introduction to Data Science and Machine Learning" course, led by Anatoly Karpov, provides a straightforward approach to the subject, making it accessible without heavy jargon.
Moreover, the "Practical Deep Learning for Coders" course caters to individuals with programming experience, emphasizing practical applications of deep learning and machine learning for real-world challenges.
To assist organizations integrating ML into their operations, Selectel Academy has curated a course titled "Building ML Workflows." This resource emphasizes MLOps—a discipline aimed at standardizing the development and deployment processes of ML systems.
In the video domain, the "StatQuest with Josh Starmer" channel offers engaging content for those looking to visualize complex ML concepts.
For those interested in literature, several books, including "Lectures on General Algebra" by A.G. Kurosh and "Linear Algebra and Its Applications" by L.I. Golovina, provide essential mathematical foundations critical for understanding ML.
These resources represent a significant opportunity for individuals and companies alike to enhance their knowledge and capabilities in the rapidly evolving field of machine learning. As competition in the AI sector intensifies, access to quality educational materials could prove crucial for both new entrants and established firms aiming to stay ahead.
