refining

docs/0. Overview/0.0. Course.md

@@ -2,13 +2,13 @@
 
 ## What will this course teach you?
 
-Welcome to our comprehensive course designed to elevate your Python programming from basic notebooks to crafting a sophisticated, production-grade AI/ML codebase. Throughout this journey, you will learn:
+Welcome to our MLOPS coding course designed to bring your Python programming level from basic notebooks to production-grade AI/ML codebase. Throughout this journey, you will learn:
 
-- How to build and deploy production-grade software artifacts.
-- Transitioning from prototyping in notebooks to developing structured Python packages.
-- Enhancing code reliability and maintenance through linting and testing tools.
-- Streamlining repetitive tasks using automation, both locally and via CI/CD pipelines.
-- Adopting best practices to develop a versatile and resilient AI/ML codebase.
+- How to build and deploy production ready software artifacts.
+- Transitioning from prototyping in notebooks to structured Python packages.
+- Enhancing code reliability and maintanability through linting and testing.
+- Streamlining repetitive tasks using automation, locally and through CI/CD pipelines.
+- Adopting best practices to develop versatile and resilient AI/ML codebases.
 
 ## Is there a fee for this course?
 
@@ -18,32 +18,32 @@ Additionally, for those seeking a deeper understanding, we provide extra support
 
 ## Why enroll in this course?
 
-The intersection of AI and ML with software applications is becoming increasingly complex, necessitating sophisticated management of models, datasets, and code. This course aims to bridge the knowledge gap between software engineers and data scientists, empowering you to efficiently navigate and manage AI/ML projects.
+The intersection of AI and ML with software applications is becoming increasingly complex, requiring management of models, datasets, and code. This course aims to bridge the knowledge gap between software engineers and data scientists, empowering you to efficiently navigate and manage AI/ML projects.
 
 A key focus is the shift from using notebooks for production, which often lack rigorous software development practices, to a structured codebase. This transition is crucial for tackling production challenges, fostering better collaboration, and advancing your MLOps capabilities.
 
 ## What should you know before starting?
 
 To get the most out of this course, you should have:
 
-1. A basic grasp of Python programming—understanding loops, conditionals, functions, and classes.
+1. A good understanding of Python including loops, conditionals, functions, and classes.
 2. Familiarity with terminal commands for software installation, following README guides, and launching applications.
-3. An introductory level of knowledge in data science, including data exploration, feature engineering, model training and tuning, and performance evaluation.
+3. Basic knowledge in data science, including data exploration, feature engineering, model training and tuning, and performance evaluation.
 
 ## What skills will you acquire?
 
 The course is divided into six in-depth chapters, each focusing on different facets of coding and project management skills:
 
-1. **[Initializing](../../1. Initializing/)**: Equip yourself with the necessary tools and platforms for your development environment.
-2. **[Prototyping](../../2. Prototyping/)**: Begin with notebooks to dive into data science projects and pinpoint viable solutions.
+1. **[Initializing](../../1. Initializing/)**: Go through the necessary tools and platforms for your development environment.
+2. **[Prototyping](../../2. Prototyping/)**: Start with notebooks to dive into data science projects and pinpoint viable solutions.
 3. **[Refactoring](../../3. Refactoring/)**: Transform your prototype into a neatly organized Python package, complete with scripts, configurations, and documentation.
 4. **[Validating](../../4. Validating/)**: Adopt practices like typing, linting, testing, and logging to refine code quality.
 5. **[Refining](../../5. Refining/)**: Leverage advanced software development techniques and tools to polish your project.
 6. **[Sharing](../../6. Sharing/)**: Foster a productive team environment for effective contributions and communication.
 
 ## What's beyond the scope of this course?
 
-While this course provides a solid grounding in managing AI/ML projects, it does not delve into specific MLOps platforms like [SageMaker](https://aws.amazon.com/sagemaker/), [Vertex AI](https://cloud.google.com/vertex-ai/), [Azure ML](https://azure.microsoft.com/en-us/products/machine-learning), or [Databricks](https://www.databricks.com/) as vendor courses already cover these end-to-end platforms. Instead, this course focuses on core principles and practices that are universally applicable, whether you're working on-premise, cloud-based, or in a hybrid setting.
+While this course provides a solid bases in managing AI/ML projects, it does not enter into the specificity of the different MLOps platforms like [SageMaker](https://aws.amazon.com/sagemaker/), [Vertex AI](https://cloud.google.com/vertex-ai/), [Azure ML](https://azure.microsoft.com/en-us/products/machine-learning), or [Databricks](https://www.databricks.com/). Vendor courses already cover these end-to-end platforms. Instead, this course focuses on core principles and practices that are universally applicable, whether you're working on-premise, cloud-based, or in a hybrid setting.
 
 ## How much time do you need to complete this course?