In this course, you will learn how to apply deep learning models to Natural Language Processing (NLP) tasks using Python. By the end of the course, you will be able to understand and implement cutting-edge deep learning models, including Feedforward Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks, tailored for NLP applications. You will also get … [Read more...] about Natural Language Processing – Deep Learning Models in Python
Data Science
Machine Learning Pipelines with Azure ML Studio
In this project-based course, you are going to build an end-to-end machine learning pipeline in Azure ML Studio, all without writing a single line of code! This course uses the Adult Income Census data set to train a model to predict an individual's income. It predicts whether an individual's annual income is greater than or less than $50,000. The estimator used in this project … [Read more...] about Machine Learning Pipelines with Azure ML Studio
Advanced Machine Learning and Deep Learning
This advanced machine learning and deep learning course provides a robust foundation in these transformative technologies. Starting with an overview of deep learning, you'll explore its core concepts, real-world applications, and significance in AI's evolution. Practical aspects include neural network layers, activation functions, and performance metrics in model evaluation. … [Read more...] about Advanced Machine Learning and Deep Learning
GenAI for Portfolio Managers: Smarter Asset Allocation
In this course, we’ll break down complex financial strategies using simple, practical insights. No advanced tech skills or CFA charter required! You’ll discover how Generative AI can: 1. Help you ask better questions when creating an Investment Policy Statement (IPS). 2. Simplify Strategic Asset Allocation (SAA) by matching risk-return objectives with asset choices. 3. Identify … [Read more...] about GenAI for Portfolio Managers: Smarter Asset Allocation
Introduction to Bayesian Statistics for Data Science
This course introduces the theoretical, philosophical, and mathematical foundations of Bayesian Statistical inference. Students will learn to apply this foundational knowledge to real-world data science problems. Topics include the use and interpretations of probability theory in Bayesian inference; Bayes’ theorem for statistical parameters; conjugate, improper, and objective … [Read more...] about Introduction to Bayesian Statistics for Data Science
