AI Quality & Testing

This Linked In Learning course provides a practical introduction to hypothesis testing for data science. You will learn about the different types of hypothesis tests and how to apply them to real-world data. The course includes hands-on exercises using Python.

This course introduces Quality 4.0, which transforms quality management using digitalization and artificial intelligence technologies, aligning quality practices with Industry 4.0.

This Udacity course, developed by Google, provides a practical introduction to A/B testing. You will learn how to design and analyze A/B tests. The course covers topics such as metrics, sample size, and statistical significance.

Data Science and Machine Learning with R

Welcome to the Deep Learning Fundamentals course on Udemy! Are you ready to unlock the power of neural networks and delve into the exciting world of artificial intelligence? Look no further! This comprehensive course is designed to equip you with the essential knowledge and practical skills needed to become proficient in both TensorFlow and PyTorch based deep learning together!Deep learning has revolutionized the field of AI, enabling machines to learn from vast amounts of data and make accurate predictions, recognize patterns, and perform complex tasks. In this course, we will demystify the concepts behind deep learning and guide you through hands-on exercises to build and train your neural networks.Here's an overview of what you'll learn:Introduction to Deep Learning:Understand the fundamentals of artificial neural networks.Explore the history and evolution of deep learning.Gain insights into real-world applications and their impact.Neural Networks and Architectures:Study the structure and functioning of artificial neurons.Learn about various neural network architectures, including feedforward, convolutional, and recurrent networks.Explore activation functions, weight initialization, and regularization techniques.Building Deep Learning Models:Implement deep learning models using popular frameworks such as TensorFlow or PyTorch.Understand the process of data preprocessing, including feature scaling and one-hot encoding.Design effective training and validation sets for model evaluation.Training Neural Networks:Grasp the concept of backpropagation and how it enables model training.Explore optimization algorithms like stochastic gradient descent (SGD) and Adam.Learn techniques to prevent overfitting, such as dropout and ea