Machine Learning
Showing 73–84 of 273 results
Designing Machine Learning Workflows in Python
Learn to build pipelines that stand the test of time.
Developing Machine Learning Models for Production
Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.
Developing Models in Microsoft Azure
It's common to spend a lot of resources on identifying an optimal machine learning model. In this course, you will learn how to simplify this process using various cutting edge features offered by Microsoft Azure Machine Learning service.
Diabetes classification with KNN in Python
Learn how to classify diabetes using the K-Nearest Neighbors (KNN) algorithm in Python. Understand how to preprocess data, train the model, and evaluate its performance to predict the likelihood of diabetes in individuals based on their health data.
Digital Analytics & Regression
Learn the fundamentals of digital analytics and regression analysis to uncover insights from data. Master techniques like linear regression and advanced analytics tools to optimize business performance and drive data-driven decision-making.
Dimensionality Reduction in Python
Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
Diving Deep into Deep Belief Networks (DBNs)
Dive into the world of deep belief networks (DBNs) and discover their significance. This course will teach you about restricted Boltzmann machines (RBMs) and DBNs, provide real-world data challenges, and expand your deep learning knowledge.
Doubling the Performance of AI for Fraud Detection with Graph
Using TigerGraph and some Python machine-learning, we show you how anyone can build a payment fraud system that halves the number of frauds and reduces the number of inappropriately blocked transactions.
Efficient Data Feeding and Labeling for Model Training
Creating data models using machine learning requires effective training data. This course will teach you how to feed your data model’s training process using data labeling for supervised training and unlabeled data for semi-supervised training.
Efficient models: reduce dimensionality with LDA in Python
Learn how to reduce dimensionality and improve model efficiency using Linear Discriminant Analysis (LDA) in Python. Discover how LDA helps with classification problems by maximizing class separability while reducing noise in high-dimensional data.
Encoder-Decoder Architecture
This course gives you a 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.
End-to-End Machine Learning
Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.