Automate feature selection in your machine learning models using Lasso Regression. Learn how to use Lasso for both regression and feature selection to create more efficient and accurate predictive models by eliminating irrelevant features.

Learn how to build and host Alexa Skills that respond to users’ vocal commands.

In this course you will learn the details of linear classifiers like logistic regression and SVM.

Learn how PyTorch, a deep learning framework, can be used to automate and optimize processes through the development and deployment of state-of-the-art AI applications. The course will also help you understand the importance of data quality, how to choose the right model, and the challenges in deploying and maintaining both deep learning and machine learning applications.

Learn to deploy and optimize applications utilizing large language models for natural language processing.

Learn the theoretical foundations of Deep Learning through practical Python code.

Master dimensionality reduction techniques like PCA (Principal Component Analysis) and t-SNE. Learn how to reduce the complexity of large datasets, improve model performance, and visualize high-dimensional data with ease.

This talk compares a modern streaming architecture to traditional batch and big data alternatives and benefits.

Thanks to AI, modern apps are more interactive and intelligent than ever before. Microsoft's Cognitive Service APIs offer easy-to-use machine learning models that are trained on vast repositories of data to offer solutions for common use cases.

Understand the importance of machine learning explainability. Learn to interpret and explain complex ML models using techniques like SHAP, LIME, and other methods to ensure transparency and trust in AI predictions.

Understand the fundamentals of Machine Learning and how it's applied in the business world.

Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.