Example Curriculum
Section 00 - Introduction
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Section 01 - Virtual Environments in Jupyter Notebook
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Section 02 - Essential Python Libraries for AI: Requests & Pandas
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- Getting Started with the requests and httpx Libraries in Python (8:49)
- Handling HTTP Errors (4:38)
- Managing HTTP Authentication and Headers (OpenAI API) (9:59)
- Setting Up the Environment: Jupyter Notebook and Pandas (3:54)
- Introduction to Pandas: Series and DataFrames (6:08)
- Importing and Exporting Data: Working with CSV Files (6:37)
- Exporting Data to Different Formats: Excel, JSON, SQL, YAML (7:46)
- Modifying Data: Adding and Dropping Columns and Rows (6:04)
- Accessing Data: Using df.iloc[] and df.loc[] (5:42)
- Sampling and Previewing Data: Using df.sample() and df.head() (6:14)
- Filtering Data: Masks and pandas.Series.between() (7:14)
- Sorting Data: Understanding Pandas Sorting Methods (7:10)
- Handling Missing Data (4:43)
- Aggregations and Grouping Data (4:53)
- Project: Analyzing Website Traffic Data (4:32)
- Time Series Data Manipulation in Pandas (6:59)
- Unlimited Updates
Section 03 - Introduction to LLMs, APIs, and AI Libraries
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- Foundations of LLMs and Generative AI (8:31)
- Tokens, Context Windows and Cost (5:25)
- Exploring LLM APIs: AI as a Service (9:22)
- OpenAI Playground, Google AI Studio, and Anthropic Workbench (6:05)
- Challenges and Limitations of LLMs (9:02)
- The State of AI: Present and Future – The Good and the Bad (10:05)
- Implement a New Life System
Section 04 - Deep Dive into LLMs
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- Pretraining Data (Internet) (6:40)
- Tokenization (6:06)
- Training the Neural Network (9:25)
- Post-Training: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) (8:25)
- Reinforcement Learning (RL) (5:29)
- Becoming Better than Humans: AGI and ASI with RL (7:31)
- Reinforcement Learning with Human Feedback (RLHF) (6:22)
- How to Deal With Hallucinations (7:36)
- Using Tools: Internet Search, Interpreter, and Deep Search (7:48)
- Big Ideas Recap (Core Summary) (9:51)
Section 05 - Diving into OpenAI API with Python
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- Authenticating to OpenAI using Python Dotenv (8:16)
- Chat Completions Endpoint (6:57)
- Developer Message (4:30)
- Streaming API Responses (4:30)
- Using Local Base64 Images as Input (6:43)
- Using Online Images as Input (2:04)
- Chat Completion API Parameters: Temperature and Seed (6:13)
- Chat Completion API Parameters: Top P, Max_Tokens, Penalties (9:49)
- Diving into OpenAI’s Reasoning Models (o1 and o3) (7:55)
- Best Practices for Prompting Reasoning Models (5:25)
- Transcriptions with Whisper (5:47)
- Translations with Whisper (3:11)
- Text-to-Speech (TTS) API (7:02)
- Generating Original Images Using the DALL-E 3 (10:49)
- Creating Variations of Images with DALL-E (3:04)
- Editing Images with DALL-E (5:39)
- Course Check-In
Section 06 - Prompt Engineering for Generative AI
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- Intro to Prompt Engineering (2:40)
- Tactic 1: Position Instruction Clearly with Delimiters (4:12)
- Tactic 2: Provide Detailed Instructions for the Context (6:37)
- Tactic 3: Use the Rich Text Format (RTF) (7:45)
- Tactic 4: Few Shot Prompting (8:12)
- Tactic 5: Specify the Steps Required to Complete a Task (5:16)
- Tactic 6: Give Models Time to Think (2:12)
- Other Tactics and Principles for Better Prompting (5:37)
- Avoid Hallucinations Using Guarding (3:06)
- Summary (2:06)
Section 07 - OpenAI API Project: Building a Healthy Daily Meal Plan
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Section 08 - Diving into Google’s Gemini API
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- Setting Up the Python SDK and Authenticating for Gemini API (9:50)
- Generating Text From Text Prompts (4:14)
- Streaming Gemini Responses (2:58)
- Generating Text From Images (5:48)
- Gemini API Generation Parameters: Controlling How the Model Generates Responses (6:11)
- Gemini API Generation Parameters Explained (10:13)
- Building Chat Conversations (7:53)
- Project: Building a Conversational Agent Using Gemini Pro (7:18)
- System Instructions (5:42)
- The File API: Prompting with Media Files (6:08)
- Tokens (6:41)
- Prompting with Audio (4:20)
Section 09 - Gemini API Project: Talking With an Image
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- Project Requirements (5:53)
- Building the Application (5:22)
- Testing the Application (1:48)
- Streamlit: Transform Your Jupyter Notebooks into Interactive Web Apps (2:48)
- Creating the Web App Layout With Streamlit (11:19)
- Saving and Displaying the History Using the Streamlit Session State (5:19)
- Exercise: Imposter Syndrome (2:55)
Section 10 - Gemini API Project: Building an AI-Powered Image Renaming Tool
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Section 11 - Diving into LangChain
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- LangChain Demo (5:05)
- Introduction to LangChain (5:09)
- Working with the OpenAI Models (8:42)
- Caching LLM Responses (4:56)
- LLM Streaming (2:57)
- Prompt Templates (5:35)
- ChatPromptTemplate (5:54)
- Understanding Chains (7:47)
- Installing the Python Libraries for Gemini and Authenticating to Gemini (4:30)
- Integrating Gemini with LangChain (6:01)
- Using a System Prompt and Enabling Streaming (6:31)
- Multimodal AI With Gemini (14:12)
- LangChain Tools: DuckDuckGo and Wikipedia (11:07)
- Creating a React Agent (13:29)
- Testing the React Agent (4:49)
Section 12 - Diving into Embeddings
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Section 13 - RAG Project: Q&A Application on Your Private Documents (Pinecone and Chroma)
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- Project Introduction (6:08)
- Loading Your Custom (Private) PDF Documents (7:27)
- Loading Different Document Formats (5:12)
- Public and Private Service Loaders (4:37)
- Chunking Strategies and Splitting the Documents (6:38)
- Intro to Vector Stores and Authenticating to Pinecone (9:02)
- Working with Pinecone Indexes (9:31)
- Working with Vectors (8:42)
- Pinecone Namespaces (6:43)
- Embedding and Uploading to a Vector Database (Pinecone) (13:52)
- Asking and Getting Answers (11:42)
- Using Chroma as a Vector DB (11:10)
- Adding Memory to the RAG System (Chat History) (9:25)
- Using a Custom Prompt (8:09)
Section 14 - Building a Simple ReAct Agent from Scratch
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Section 15 - Diving into LangGraph
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Section 16 - Project: Tweet Generator (Reflection)
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Section 17 - LangSmith: Platform for Building Production-grade LLM Apps
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Section 18 - Project: Essay Writer (Reflexion)
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Section 19 - Master Project: Build a Research Agent with LangGraph, GPT-4o, RAG, Pinecone, ArXiv, and Google SerpAPI
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- Note (2:15)
- Application Overview (3:33)
- Extracting Data from ArXiv with Pandas (12:43)
- Downloading Research Papers (4:52)
- Loading, Splitting and Expanding Data (9:53)
- Building a Knowledge Base for RAG (5:34)
- Creating a Pinecone Index (7:16)
- Loading the Knowledge Base and Deploying to Pinecone (5:03)
- Developing Custom Tools (5:12)
- Implementing the ArXiv Fetch Tool (8:00)
- Unlocking Web Search with Google SerpAPI (3:28)
- Building Google SerpAPI Tools (4:25)
- Creating RAG Tools (6:19)
- Implementing the Final Answer Generation Tool (2:17)
- 06_14 Initializing the Oracle LLM (11:01)
- Testing the Ecosystem (3:32)
- Building a Decision-Making Pipeline (8:33)
- Defining the Agent State (3:24)
- Defining the Graph (6:35)
- Generating Reports (4:26)
- Building the Final Research Report (5:19)
- Concluding the Project (6:22)
Appendix - Working with Python Modules
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Where To Go From Here?
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