Added precourse checklist

docs/_toc.yml

@@ -3,6 +3,7 @@ root: README
 parts:
   - caption: Course Requirements
     chapters:
+      - file: docs/course-requirements/precourse-checklist
       - file: docs/course-requirements/learning-python
       - file: docs/course-requirements/python-environment
       - file: docs/course-requirements/data-science

docs/course-requirements/data-science.md

@@ -22,3 +22,10 @@ To help you build the required skills in data science, machine learning, and NLP
 | NLP with Transformers | Hugging Face       | 4 hours    | [Hugging Face Transformers](https://huggingface.co/learn/nlp-course/chapter1)              |
 | NLP Basics               | fast.ai                | 3 hours    | [NLP with fast.ai](https://course.fast.ai/)                                                          |
 | Machine Learning Basics  | Coursera (Andrew Ng)   | 60 hours   | [Coursera ML course](https://www.coursera.org/learn/machine-learning)                     |
+
+
+## Youtube Videos
+
+If you are very, very short on time and just want a quick introduction to LLMs, please watch this video:
+
+[**30 Minute Introduction to Large Language Models**](https://www.youtube.com/watch?v=wjZofJX0v4M)

docs/course-requirements/precourse-checklist.md

@@ -0,0 +1,16 @@
+# Pre-Course Checklist
+
+To help you prepare effectively for the course, we have summarized all the necessary steps in this checklist. The checklist provides links to the relevant sections as needed. Please ensure you go through each step and complete the required tasks.
+
+## Pre-Course Checklist
+
+Before the course begins, make sure you have completed the following steps:
+
+1. **Prerequisite Knowledge and Skills**  
+   Ensure you have reviewed the required sections to gain the necessary foundational knowledge in [Python](/docs/course-requirements/learning-python.md) and [Data Science and LLMs](/docs/course-requirements/data-science.md). These resources will help you build a strong foundation for the course content.
+
+2. **Python Environment Setup**  
+   Make sure Python is installed on your system. You should be able to use Jupyter Notebooks and have set up VS Code with all the required packages installed. Instructions for setting up your Python environment can be found in the [Python Environment Setup Section](/docs/course-requirements/python-environment.md).
+
+3. **Sign Up for Platforms**  
+   Register for accounts with OpenAI, Hugging Face, Twilio, AWS and GitHub. These platforms will be used throughout the course. Follow the instructions [here](/docs/course-requirements/platforms.md) to ensure you have signed up for these platforms and retrieved the necessary API keys.

docs/malawi-nov-24/README.md

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+# LLM Application Development with LangChain and Python
+In this iteration of the course, participants will explore how to develop advanced applications using Large Language Models (LLMs) with the LangChain framework in Python. Through practical exercises and real-world case studies, the course dives into the technical aspects of building LLM-powered solutions, covering everything from prompt engineering to integrating various data sources and APIs. Participants will gain hands-on experience in creating dynamic applications, including intelligent chatbots and automated workflows. The course begins by providing a foundational understanding of LLMs—how they are trained and adapted for different domains through techniques like prompt engineering and fine-tuning. It then introduces LangChain, a leading framework for building LLM applications, empowering participants to enhance their business processes with LLMs. Ideal for developers, data scientists, data engineers, analysts, and professionals across industries such as banking, telecommunications, and the public sector, this course equips you with the skills needed to build your first production-grade LLM application.
+
+The course is structured into self-contained modules, each building on the skills learned in previous ones. Each module includes lectures for key concepts, practical labs with programming activities and modifiable recipes, and case studies that showcase real-world applications. To reinforce learning, assessments combine theoretical and programming questions to evaluate the learner's understanding and skills gained.
+
+
+
+## Session Details
+
+### Audience
+This session targeted staff from National Statistical Offices across 13 African countries, including Kenya, Tunisia, Burundi, Niger, Burkina Faso, Senegal, Cameroon, Mali, Côte d'Ivoire, Uganda, Central African Republic (RCA), Tanzania, and Mozambique.
+
+### Organization
+The course was divided into three phases, each tailored to maximize learning and engagement:
+
+- **Phase 1: Virtual Session**  
+  This brief, 3-hour virtual session introduced participants to the course content and sparked enthusiasm for the in-person session.
+
+- **Phase 2: In-Person Session**  
+  Conducted over five days, this phase combined two components: a 3-day module on big data, followed by this 2-day LLM course.
+
+- **Phase 3: Project Implementation**  
+  In this phase, participants applied what they learned in the previous sessions by building LLM-based applications, primarily chatbots, to facilitate the dissemination of information.