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My Journey Through the “Git for Beginners” Udemy Course by Navin Reddy

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Embarking on the journey to master Git, I recently completed the “Git for Beginners” course on Udemy, taught by Navin Reddy.  The “Git for Beginners” course covers everything from setting up Git on your machine to mastering basic commands, branching, merging, and handling merge conflicts. Key Learnings Here are some of the key takeaways from the course: Understanding Version Control Setting Up Git Basic Git Commands Branching and Merging Collaborating with GitHub Best Practices Practical Experience Throughout the course, I had opportunities to apply what I learned through hands-on exercises. You can check out my GitHub repository where I have applied these learnings: Git Course Repository . If you’re looking to get started with Git, I highly recommend this course. You can find it on Udemy  here .

Structured Test Strategy Series - Day 2

The  Project Environment  in HTSM refers to the external factors, limitations, and context in which the product is developed and tested. Understanding this can help to set realistic expectations, prioritise the tests, and identify key areas where constraints might affect the testing. Mnemonic: MID-TEST-D . (Remember MID STD' ) M: Mission I: Information D: Developer Relations T: Test Team E: Equipment & Tools S: Schedule T: Test Items D: Deliverables Example Application: Adding Products to Shopping Basket Using this model for the  “Add to Basket”  feature on an e-commerce platform: Mission : Confirm the feature’s functionality and reliability under various conditions (e.g., adding different items, adjusting quantities). Information : Gather user stories, past issues with cart functionality, and common customer complaints about similar features. Developer Relations : Check in with developers about any known complexities in the “Add to Basket” code. Test Team : Leverage team membe

Structured Test Strategy Series - Day 1

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 Day 1: Heuristic Test Strategy Model Introduction ·        Goal: Understand the basics and components of the Heuristic Test Strategy Model (HTSM). ·         Tasks: ·         Read an overview of the  HTSM  (by James Bach) to understand its purpose. ·         Study the main categories of the model:  Project Environment, Product Elements, Quality Criteria, and Test Techniques .   Notes: What is the Heuristic Test Strategy Model (HTSM)? Heuristic: Think of this as a rule of thumb or a general guideline, not a strict rule. In testing, heuristics help you decide what to test and how to test it. Test Strategy Model: It's a framework or guide that helps testers think about different areas of a product that need to be tested.   Why is HTSM Useful? It helps testers identify what aspects of a product to focus on. It provides a structure, so you don’t miss important areas during testing.     How to Use HTSM? When testing a product, you can use these categories as a checklist or guide to ensu

Personal Branding Session with Pradeep Soundararajan

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Today I had the opportunity to attend "Personal Branding Session led by the renowned speaker and thought leader, Pradeep Soundararajan." The session was both enlightening and empowering, providing valuable insights into building a strong personal brand. This mind map provides a comprehensive overview of the key takeaways of personal branding from the session. 😊

Day 3: Navigating the Landscape of Generative Models

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Key Points: Generative AI is a type of AI that can create new content, such as images, text, and audio. Generative Image Models use techniques like diffusion to create images from prompts or existing images. Generative Language Models learn patterns in language from training data and can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.   1. speechify.com speechify.com Predictive Models take data and labels as input, learn the relationship between them, and output the label. Prompt Design is the process of creating a prompt that will generate the desired output from a language model. Examples of Generative AI Applications: Image Generation: Creating images from text descriptions. Image Captioning: Generating text descriptions of images. Text Generation: Writing different kinds of creative content, translating languages, and answering questions. Translation: Translating text from one lang

Day 2: Understanding Deep Learning: Generative vs. Discriminative Models

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Generative Model : Focuses on understanding and creating visual representations of data. It learns about the features and can generate new data instances. Discriminative Model : Focuses on distinguishing between different classes based on their features. It classifies data by learning the differences between classes. This mind map helps in understanding the key differences between generative and discriminative models in deep learning.