50 Machine Learning Courses for Learning Artificial Intelligence
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Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Learn best practices for using TensorFlow, a popular open-source machine learning framework.
Introduction to Responsible AI
This is an introductory-level microlearning course aimed at explaining what responsible AI is, why it's important, and how Google implements responsible AI in their products.
Structuring Machine Learning Projects
Learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.
Transformer Models and BERT Model
This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model.
Probability & Statistics for Machine Learning & Data Science
Visually and intuitively understand the properties of commonly used probability distributions in machine learning and data science.
Create Image Captioning Models
This course teaches you how to create an image captioning model by using deep learning.
Machine Learning Foundations for Product Managers
In this first course of the AI Product Management Specialization offered by Duke University's Pratt School of Engineering, you will build a foundational understanding of what machine learning is, how it works and when and why it is applied
Encoder-Decoder Architecture
Synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering.
Introduction to Machine Learning
Provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction.
Clustering
An exhaustive description of and comparison between different algorithmic approaches to clustering.