This course is the first part in a two part course and will teach you the fundamentals of PyTorch. In this course you will implement classic machine learning algorithms, focusing on how PyTorch creates and optimizes models. You will quickly iterate through different aspects of PyTorch giving you strong foundations and all the prerequisites you need before you build deep learning models.

Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.

Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.

Explore Image Representation and Processing to learn transforming images for AI and computer vision tasks efficiently.

Many of the millions of digital images we're generating need interpretation, but there aren't enough human eyes for the task. This course will teach you how to use Python libraries and deep learning models to automate image segmentation.

Image recognition is used in a wide variety of ways in our daily lives. This course will teach you how to tune and implement convolutional neural networks in order to implement image recognition and classification on a business case.

Time series analysis is one of the more difficult and confusing aspects of data science. This course will teach you how to use TensorFlow with time series data and generate high performing forecasts and predictions.

Training a machine learning model is only the first step. This course will teach you how to deploy, monitor, and scale your machine learning solution in preparation for the Machine Learning Specialty exam.

TensorFlow is popular a library for implementing a range of deep learning solutions but is especially useful for solutions that deal with images. This course will teach you the basics of how to use TensorFlow to implement the most typical scenarios.

In this course, you will learn how you can develop your machine learning workflow using RapidMiner Studio, a data science platform for data preparation, machine learning, and predictive model deployment.

In this course, you will learn how you can develop your machine learning workflow using Weka, an open-source machine learning software for data preparation, machine learning, and predictive model deployment.

TensorFlow is a widely-used data science and machine learning software library. This course will teach you the basics of implementing predictive analytics using TensorFlow, including supervised learning, recommendation, and reinforcement systems.