As the role of information and communication technologies gradually increases in our lives, source code security becomes a significant issue to protect against malicious attempts. Furthermore, with the advent of data-driven techniques, there is now a growing interest in leveraging machine learning and natural language processing (NLP) as a source code assurance method to build trustworthy systems. Therefore, training our future software developers to write secure source code is in high demand. In this thesis, we propose a framework including learning modules and handson labs to guide future IT professionals towards developing secure programming habits and mitigating source code vulnerabilities at the early stages of the software development lifecycle. In this thesis, our goal is to design learning modules with a set of hands-on labs that will introduce students to secure programming practices using source code and log file analysis tools to predict/identify vulnerabilities. In a Secure Coding Education framework called (SeCodEd) we will (1) improve students' skills and awareness on source code vulnerabilities, detection tools, and mitigation techniques; (2) integrate concepts of source code vulnerabilities from Function, API, and library level to bad programming habits and practices; (3) leverage deep learning, NLP and static analysis tools for log file analysis to introduce the root cause of source code vulnerabilities.

This article covers my experience in getting certified with AWS Certified Machine Learning -- Specialty, and I have shared the resources and cheatsheets, which helped me understand concepts! In the preparation phase of certification, I came across many excellent articles, blogs, and experience posts alongside the courses, which immensely helped me in understanding the width and breadth of the AWS ML world. I want to share my experience and the resources I found along the way, which boosted my confidence to take up the certification! Let's fill in some colors. What all are we talking about in this article?

This course covers Amazon's API Gateway service, which will help you create some incredibly robust APIs for your workloads.

Hello Capital Region GDG enthusiasts, Join our community of like-minded developers and explore the latest Google Cloud technologies as a team. Get Cloud Study Jam training from Google and get hands-on experience with Machine Learning, a $55 value, free of charge. After the Study Jam, you'll receive 30-days free access to the entire Qwiklabs catalog, so you can keep developing your skills at home. Complete all of the labs in the session's quest, and you'll earn an additional free 30-days of access to Qwiklabs as well as a badge for your online profile. The labs will run on all of the latest versions of popular browsers.

This Quest of all-Introductory-level Self-Paced Labs is intended for the complete beginner in Google Cloud. In these short hands-on labs, you will get your first-touch experiences with a subset of the Google Cloud services that provide tools for working with big (and small!) data and machine learning / artificial intelligence services in Google Cloud. You can use the BigQuery web UI as a visual interface to complete tasks like running queries, loading data, and exporting data. This lab shows you how to query public tables and load sample data into BigQuery using the Web UI. This hands-onlab shows you how to query public tables and load sample data into BigQuery using the Command Line Interface.

This content is designed for audience without any prior Machine learning knowledge. It starts from very basics and goes to advanced topics. We will try to keep this content live and include more and more advanced lab sessions with real life scenarious.