Artificial Neural Networks

In this Machine Learning Capstone course, you will be using various Python-based machine learning libraries such as Pandas, scikit-learn, Tensorflow/Keras, to: build a course recommender system; analyze course related datasets, calculate cosine similarity, and create a similarity matrix; create recommendation systems by applying your knowledge of KNN, PCA, and [...]

In the second course of the Machine Learning Specialization, you will: build and train a neural network with TensorFlow to perform multi-class classification; apply best practices for machine learning development so that your models generalize to data and tasks in the real world; build and use decision trees and [...]

Nossas boas-vindas ao Curso Engajamento, Conversão e o Consumidor. Neste curso, você aprenderá sobre as diferentes técnicas de análise de dados que permitem acompanhar e monitorar o consumidor com relação ao seu engajamento e aos seus níveis de conversão.

In this course, we will build on our knowledge of basic models and explore advanced AI techniques. We’ll start with a deep dive into neural networks, building our knowledge from the ground up by examining the structure and properties. Then we’ll code some simple neural network models and learn [...]

This course is aimed at anyone interested in applying machine learning techniques to scientific problems. In this course, we'll learn about the complete machine learning pipeline, from reading in, cleaning, and transforming data to running basic and advanced machine learning algorithms. We'll start with data preprocessing techniques, such as [...]

In How Diffusion Models Work, you will gain a deep familiarity with the diffusion process and the models which carry it out. More than simply pulling in a pre-built model or using an API, this course will teach you to build a diffusion model from scratch.