For students who already grasp the fundamentals of machine learning and wish to enhance their capacity to create more dependable and accurate models, Kaggle’s Intermediate Machine Learning is a useful, hands-on course. Essential real-world machine learning techniques are covered in the course, including how to deal with missing data, work [...]
Kaggle’s Introduction to Machine Learning is a hands-on, beginner-friendly micro-course that uses scikit-learn and Python to present the basic ideas of machine learning. Students will construct their first machine learning models, investigate and comprehend datasets, verify model performance, deal with overfitting and underfitting, and use methods like random forests and [...]
Google created the free, beginner-friendly Machine Learning Crash Course (MLCC) to teach the principles of contemporary AI and machine learning. Easy-to-understand lectures, Google expert videos, interactive visualizations, tests, and practical coding tasks are all included in this course. Large language models (LLMs), neural networks, embeddings, linear regression, classification, data preparation, [...]
Duke University offers an intermediate-level course on Coursera called Introduction to Machine Learning. With subjects including logistic regression, neural networks, deep learning, computer vision, natural language processing (NLP), and reinforcement learning, the course offers a solid foundation in machine learning ideas. Through practical exercises with Python and PyTorch, students learn [...]
Andrew Ng teaches a beginner-friendly AI course called Machine Learning Specialization, which is provided via Stanford Online and DeepLearning.AI. Supervised learning, unsupervised learning, neural networks, decision trees, recommender systems, and best practices for creating practical AI applications are all covered in this course, which offers a hands-on introduction to machine [...]
Andrew Ng’s machine learning specialty is DeepLearning.AI, and Stanford Online is an industry-recognized, beginner-friendly curriculum created to establish a solid foundation in AI and machine learning. Neural networks, decision trees, recommender systems, supervised learning, unsupervised learning, and machine learning best practices are all covered in this area. Students get practical [...]
This course introduces Vertex AI Studio, a tool to interact with generative AI models, prototype business ideas, and launch them into production. Through an immersive use case, engaging lessons, and a hands-on lab, you’ll explore the prompt-to-product lifecycle and learn how to leverage Vertex AI Studio for Gemini multimodal applications, [...]
This course teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able [...]
This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that [...]
This is an introductory-level microlearning course designed to explain what responsible AI is, why it’s important, and how Google implements responsible AI in its products. It also introduces Google’s 3 AI principles. Google course link: [...]