Jonathan A. Fernandes

I was invited by LinkedIn Learning to create a course on the essential's of Python's Pandas. This course includes basic data analysis and plotting, indexing, groupby and reshaping. The entire course is based on the Olympics dataset and each section builds on knowledge covered in previous sections.
Anyone who engages with the course content should have intermediate-level Pandas skills at the end of the course.

Want to get up and running with Apache Spark as soon as possible? If you're well versed in Python, the Spark Python API (PySpark) is your ticket to accessing the power of this hugely popular big data platform. This practical, hands-on course helps you get comfortable with PySpark, explaining what it has to offer and how it can enhance your data science work. To begin, instructor Jonathan Fernandes digs into the Spark ecosystem, detailing its advantages over other data science platforms, APIs… Show more

Want to get up and running with Apache Spark as soon as possible? If you're well versed in Python, the Spark Python API (PySpark) is your ticket to accessing the power of this hugely popular big data platform. This practical, hands-on course helps you get comfortable with PySpark, explaining what it has to offer and how it can enhance your data science work. To begin, instructor Jonathan Fernandes digs into the Spark ecosystem, detailing its advantages over other data science platforms, APIs, and tool sets. Next, he looks at the DataFrame API and how it's the platform's answer to many big data challenges. Finally, he goes over Resilient Distributed Datasets (RDDs), the building blocks of Spark. Show less

In a field where reproducible results are essential, Docker is rapidly emerging as one of the top tools for bringing efficiency to the work that data science teams—particularly those working in machine learning (ML)—are doing. Creating and developing ML models is often messy. Seasoned data scientists know that different versions of the same software can produce different results. With Docker, you can include the right versions of each needed dependency and library, so no one ever has to do any… Show more

In a field where reproducible results are essential, Docker is rapidly emerging as one of the top tools for bringing efficiency to the work that data science teams—particularly those working in machine learning (ML)—are doing. Creating and developing ML models is often messy. Seasoned data scientists know that different versions of the same software can produce different results. With Docker, you can include the right versions of each needed dependency and library, so no one ever has to do any configuration. After the Dockerfile is built, you'll have exactly what you need. In this course, Jonathan Fernandes helps data scientists get up and running with Docker, demonstrating how to build a Dockerized ML application that can easily be shared. Along the way, he shares common use cases for the tool. Upon wrapping up this course, you'll be prepared to leverage the power of containers in your other ML projects. Show less

Artificial intelligence (AI) is taking the world by storm. Manufacturing, healthcare, and a host of other industries are steadily adopting this technology to streamline processes, enhance predictability, and generally keep ahead of the curve. In this course, discover what it takes to successfully introduce AI to your organization. Instructor Jonathan Fernandes steps through how to determine whether your organization is ready for AI, as well as how to develop and present a compelling business… Show more

Artificial intelligence (AI) is taking the world by storm. Manufacturing, healthcare, and a host of other industries are steadily adopting this technology to streamline processes, enhance predictability, and generally keep ahead of the curve. In this course, discover what it takes to successfully introduce AI to your organization. Instructor Jonathan Fernandes steps through how to determine whether your organization is ready for AI, as well as how to develop and present a compelling business case for adopting the technology. Plus, he shares how to successfully implement AI—including how to do so using the scrum methodology—how to handle data collection and AI modeling, deploy, and finally monitor AI models once in production. Show less

Deep learning is a fairly recent and hugely popular branch of artificial intelligence (AI) that finds patterns and insights in data, including images and video. Its layering and abstraction give deep learning models almost human-like abilities—including advanced image recognition. Using OpenCV—a widely adopted computer vision software—you can run previously trained deep learning models on inexpensive hardware and generate powerful insights from digital images and video. In this course… Show more

Deep learning is a fairly recent and hugely popular branch of artificial intelligence (AI) that finds patterns and insights in data, including images and video. Its layering and abstraction give deep learning models almost human-like abilities—including advanced image recognition. Using OpenCV—a widely adopted computer vision software—you can run previously trained deep learning models on inexpensive hardware and generate powerful insights from digital images and video. In this course, instructor Jonathan Fernandes introduces you to the world of deep learning via inference, using the OpenCV Deep Neural Networks (dnn) module. You can get an overview of deep learning concepts and architecture, and then discover how to view and load images and videos using OpenCV and Python. Jonathan also shows how to provide classification for both images and videos, use blobs (the equivalent of tensors in other frameworks), and leverage YOLOv3 for custom object detection. Show less

PyTorch is quickly becoming one of the most popular deep learning frameworks around, as well as a must-have skill in your artificial intelligence tool kit. It's gained admiration from industry leaders due to its deep integration with Python; its integration with top cloud platforms, including Amazon SageMaker and Google Cloud Platform; and its computational graphs that can be defined on the fly. In this course, join Jonathan Fernandes as he dives into the basics of deep learning using PyTorch… Show more

PyTorch is quickly becoming one of the most popular deep learning frameworks around, as well as a must-have skill in your artificial intelligence tool kit. It's gained admiration from industry leaders due to its deep integration with Python; its integration with top cloud platforms, including Amazon SageMaker and Google Cloud Platform; and its computational graphs that can be defined on the fly. In this course, join Jonathan Fernandes as he dives into the basics of deep learning using PyTorch. Starting with a working image recognition model, he shows how the different components fit and work in tandem—from tensors, loss functions, and autograd all the way to troubleshooting a PyTorch network. Show less

After its debut in 2017, PyTorch quickly became the tool of choice for many deep learning researchers. In this course, Jonathan Fernandes shows you how to leverage this popular machine learning framework for a similarly buzzworthy technique: transfer learning. Using a hands-on approach, Jonathan explains the basics of transfer learning, which enables you to leverage the pretrained parameters of an existing deep-learning model for other tasks. He then shows how to implement transfer learning for… Show more

After its debut in 2017, PyTorch quickly became the tool of choice for many deep learning researchers. In this course, Jonathan Fernandes shows you how to leverage this popular machine learning framework for a similarly buzzworthy technique: transfer learning. Using a hands-on approach, Jonathan explains the basics of transfer learning, which enables you to leverage the pretrained parameters of an existing deep-learning model for other tasks. He then shows how to implement transfer learning for images using PyTorch, including how to create a fixed feature extractor and freeze neural network layers. Plus, find out about using learning rates and differential learning rates. Show less