javascript scope determines how variables can be accessed throughout your code. There are two scopes: global and local. Global variables are accessible from anywhere; local variables are accessible only from within the function or block the variables are declared. Scope can be confusing, especially when factors such as hoisting and closures enter the mix. This course provides a thorough introduction to the scope options for javascript variables, alongside challenges that help you practice what you've learned. LinkedIn Learning instructor Emmanuel Henri helps you properly scope your variables, mitigate the effects of hoisting, and declare local variables inside non-function code such as if statements using block scope.

Are you looking for a practical way to use machine learning to solve complex real-world problems? Logistic regression is an approach to supervised machine learning that models selected values to predict possible outcomes. In this course, Notre Dame professor Frederick Nwanganga provides you with a step-by-step guide on how to build a logistic regression model using Python. Learn hands-on tips for collecting, exploring, and transforming your data before you even get started. By the end of this course, you’ll have the technical skills to know when and how to design, build, evaluate, and effectively manage a logistic regression model all on your own.This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out the “Using GitHub Codespaces with this course” video to learn how to get started.

Knowing how to use Microsoft ML.NET is one of the most in-demand skills in today’s job market. If you’re looking to boost your know-how with machine learning, ML.NET is a must-have in your toolbox. In this course, senior software engineer and Microsoft MVP Sam Nasr provides a comprehensive overview of some of the advanced features of ML.NET, including how to use the command line interface, gather metrics, and evaluate and improve model accuracy and performance. Get started with an introduction to automated machine learning (AutoML) before learning how to automate model training with the ML.NET command-line interface. Along the way, Sam shows you how to evaluate an ML.NET model with metrics and improve model accuracy with cross-validation, hyperparameter tuning, pipeline value inspection, and more.

Building Modern Web Applications: A Practical Journey through ASP.NET MVC

Domestika - Programming for WordPress: Design a Website

https://www.udemy.com/course/master-react/

https://www.udemy.com/course/object-oriented-programming-oop-in-c20/

Mastering React in Monorepo: Leveraging MobX, Axios, Fluent UI, CyPress, Vite, Vitest

https://training.talkpython.fm/courses/python-type-hint-course-with-hands-on-examples

https://www.udemy.com/course/build-a-stack-overflow-clone-with-react-and-laravel-11/

https://www.udemy.com/course/data-analysis-masterclass-a-z-data-analysis-in-python/

https://www.udemy.com/course/python-creating-advanced-ai-applications/

https://www.udemy.com/course/learn-java-by-debugging-basic-to-advanced-hands-on/

https://www.udemy.com/course/the-complete-c-bootcamp-from-basics-to-advanced/

https://www.udemy.com/course/chatgpt-for-developers-new/

https://training.talkpython.fm/courses/visual-studio-code-for-python-developers

It's official: Visual Studio Code is the most popular way Python developers write Python code [1]. In 2022, 37% of Python devs and data scientists reported VS Code as their primary editor/IDE, followed by a close second with PyCharm and then nothing else close.  People choose VS Code because it's clean and simple. But that simplicity belies a massive amount of functionality that is out there to discover. That's the goal of this course, to take you from simply using VS Code to really leveraging its true potential.

Master data visualization in Python with Matplotlib, Seaborn, and Plotly libraries for effective data storytelling.

Did you know that computers have both short- and long-term memory just like you? It’s true. If you peel back the layers of what it really means to be a programmer, you find that most programming tasks involve moving values around in memory. Many contemporary programming languages hide those mechanics, but as you advance they get harder to avoid.Join instructor Maaike van Putten to take your foundational skills to the next level, diving in deeper to learn how code really works and exploring the basic tools of memory management. Get tips from Maaike on making code more efficient and fixing it when it breaks, as she walks you through best practices and power skills to improve your overall performance. By the end of this course, you’ll know more about memory management as well as how to avoid and debug common leaks.

Programmers shouldn’t have to guess whether software is working correctly. They should be able to prove it, every step of the way. A formal test-driven development (TDD) process allows you to build testing into your daily routine. You can run tests many times a day, getting instant feedback on the quality of your code. This course explains how to adopt a TDD mindset and process—vital skills for all modern software developers. Find out what makes a good test, why you should be more interested in failure than success, and how to measure and repeatedly run tests. Then explore the jargon: test suites, test harnesses, mock and stub objects, and more. Finally, look at how TDD is used in the most common programming languages and environments and what tools and frameworks exist to help you succeed. Instructor Neelam Dwivedi tackles each topic from a programmer’s perspective, emphasizing the time and cost savings that a good TDD workflow can provide.

Design patterns are reusable solutions that solve the challenges software developers face over and over again. Rather than reinventing the wheel, learn how to make use of these proven and tested patterns that will make your software more flexible and resilient to change. This course introduces you to design patterns and takes you through several of the most used object-oriented patterns. Elisabeth Robson and Eric Freeman, coauthors of Head First Design Patterns, join forces to provide an overview of each pattern and examples of the pattern in action. Featured design patterns include the strategy, adapter, observer, and decorator patterns. Elisabeth and Eric lend these concepts a real-world context by providing code demos in Java, with the occasional example in Python and javascript.

Complete hands-on machine learning and GenAI tutorial with data science, Tensorflow, GPT, OpenAI, and neural networks

Learn to create Machine Learning Algorithms in Python using Different Datasets

Bug fixing in code can start off rather simple, with obvious and recurring bugs easy to identify and root out. But eventually the bugs become harder to find and more difficult to fix. Instead of searching out errors one by one, getting to the root of the problem by enhancing your exception handling can turn your code base from chaos to clarity.
Learn how to handle errors and anomalies in your C++ code like a pro in this course with software engineer Károly Nyisztor. From the basics of exceptions to advanced techniques for robust error handling, Károly guides you through the nuances of exception handling, and explains how proper handling and logging make bugs easier to track and fix.