https://python-course-earlybird.framer.website/
Table of Content: Sub-courses
| Lesson | Duration |
|---|---|
| Introduction | |
| WATCH ME FIRST! | 03:35 |
| Tips for success | — |
| How to find and choose project ideas | |
| What can you build with Python? | 03:33 |
| Project ideation strategies | 07:32 |
| Brainstorm your projects (worksheet) | — |
| What’s next? | — |
| Lesson | Duration |
|---|---|
| Getting started | |
| Introduction - Biohack Investigation Project | 01:34 |
| How to install Python | 00:25 |
| Create and run your first Python program 🎉 | 08:01 |
| How Python program works | 03:34 |
| Useful terminologies | 07:28 |
| Jupyter Notebook & VSCode | |
| Intro to Jupyter Notebook | 05:20 |
| Intro to Visual Studio Code | 06:18 |
| Working with Jupyter notebooks in VSCode | 06:25 |
| Biohack investigation - Level 1 | |
| Getting started - Variables | 04:05 |
| 💬 5-minute quizzes | — |
| Printing case information - Print(), input() functions | 05:11 |
| Overview of built-in data types | 05:33 |
| Integer, string types | 05:17 |
| Float, boolean types | 01:25 |
| Suspect’s profile: List and Dictionary | 07:44 |
| Gathering evidence: Set and Frozenset | 04:30 |
| How to get help on data types | 07:28 |
| What’s up with suspect’s name: String operations | 04:00 |
| Finding top targets: List slicing | 05:06 |
| Target locations are changing: List unpacking and manipulation | 10:56 |
| Mysterious DNA sequences: List comprehensions | 09:15 |
| Understanding suspect’s profile: Dictionary operations | 07:27 |
| New evidence found: Set operations | 02:58 |
| 💬 5-minute quizzes | — |
| Biohack investigation - Level 2 | |
| Operators | 07:31 |
| Checking suspect’s age: Conditional statements | 09:00 |
| Cracking secret password: Loops | 11:06 |
| Saving time using Break and Continue statements | — |
| 💬 5-minute quizzes | — |
| Decoding secret messages: Functions | 12:21 |
| Local vs. global variable scope | 03:06 |
| Object-oriented programming | 10:27 |
| Biohack Investigation Blueprint ⭐ | 08:15 |
| 💬 5-minute quizzes | — |
| Packages and modules | |
| What are packages and modules, exactly? | 03:44 |
| Install and import Python packages | 08:25 |
| Understanding namespaces - A quick explanation | — |
| Create your own local Python packages | 04:30 |
| Lesson | Duration |
|---|---|
| Introduction to Machine Learning | |
| An introduction to Machine Learning, Deep Learning and AI | 10:27 |
| How Machine Learning works: An example | 06:30 |
| Three Machine Learning paradigms | 12:26 |
| Algorithms, models, parameters & hyperparameters | 12:07 |
| [Exercise] Coding a KNN model from scratch vs. Sklearn | 25:11 |
| Building blocks of Supervised Learning (1): Loss functions & Optimization methods | 08:06 |
| Building blocks of Supervised Learning (2): Model selection & Evaluation metrics | 10:42 |
| 📑 Evaluation metrics in Machine Learning | — |
| Machine learning model development pipeline | 05:52 |
| What is model deployment? | 03:58 |
| 📑 Model drift and Model monitoring | — |
| Fundamentals of Deep Learning & NLP | |
| Introduction | 01:29 |
| Neural networks - Intuition & Forward propagation | 11:38 |
| Neural networks - Back propagation & Gradient descent algorithm | 17:51 |
| What is Natural Language Processing? | 06:30 |
| Understanding text embeddings | 20:13 |
| Generative AI and Large Language Models (LLMs) | |
| Generative AI technology and LLMs | 05:22 |
| Generative foundation models | 05:13 |
| What goes into developing an LLM? | 06:55 |
| Applications of LLMs: Prompting, RAG, finetuning, and pre-training | 12:44 |
| [Exercise] Interacting with LLMs: Local models vs APIs | 04:09 |
| [Project] Creating a simple reputation monitoring app with Streamlit + OpenAI LLM | 05:16 |
| [Project] Deploying reputation monitoring app to Streamlit Community Cloud | 11:03 |
| [Project] Deploying reputation monitoring app using Docker | 06:46 |
| 🏅 Project Challenge: Building a Python application with prompt-based approach with LLMs | — |
| Deep Dive into Prompting with LLMs | |
| Basic prompting tactics and techniques | 14:21 |
| 📑 Advanced prompting techniques | — |
| 📑 Additional prompting guides & Resources | — |
| Deep Dive into Retrieval Augmented Generation (RAG) | |
| RAG architecture overview | 06:49 |
| 📑 More on vector databases | — |
| [Project] Building a PDF Q&A tool | (coming soon) |
| Advanced RAG techniques | (coming soon) |
| Deep Dive into Agents | |
| What are agents? | (coming soon) |
| Popular agent frameworks in Python | (coming soon) |
| [Project] Building and deploying an LLM agent | (coming soon) |
| Lesson | Duration |
|---|---|
| Introduction | |
| Why we should use AI tools | 01:23 |
| General purpose AI tools and coding assistants | |
| Improving your workflow with general-purpose AI tools | 11:16 |
| Boosting productivity with coding assistants: GitHub Copilot, Cursor, Codeium | 06:56 |
| Lesson | Duration |
|---|---|
| Introduction | |
| What will we learn here? - Introduction | 01:08 |
| Command line basics | |
| Basics of working with command line | 16:01 |
| Git version control | |
| Introduction to Git | 04:12 |
| Basic components of version control with Git | 14:42 |
| 💬 Let's have a quiz! | — |
| 📑 Git best practices & Tips | — |
| A common collaborative workflow using GitHub | (coming soon) |
| Managing environments | |
| Create and manage Python virtual environments | (coming soon) |
| Hiding secrets | (coming soon) |
| Other tips | |
| Automatic code formatting | (coming soon) |
| Code debugging in VS Code | (coming soon) |
| Sharing your projects on social | (coming soon) |
| Portfolio project checklist (PDF) |