AI Engineering Bootcamp: Building AI Applications (LangChain, LLM APIs + more)

18h 33m 41s
English
Paid

Course description

This course is your practical path to the profession of a generative AI engineer: not just using technologies, but creating them.

First, you will enhance your Python skills: structuring modular code, working with APIs, and data processing. Then, you'll dive into the basics of large language models (LLMs) - how they are structured, trained, and how to interact with them effectively through advanced prompt engineering.

Next, practice. You will learn to create real AI applications based on OpenAI and Gemini API, including chat systems, working with images and audio. You'll master LangChain for building agents and prompt chains, as well as LangGraph for managing multi-step processes. You will add memory to your applications using embeddings and vector databases, and learn to debug and scale production systems with LangSmith.

Throughout the course, you will develop chatbots, intelligent tools for working with images, search Q&A systems, and much more. The final project will combine all the skills: you will create a research agent that uses search, tools, and reasoning to generate high-quality reviews of real data.

This course is a path from AI experiments to a real engineering approach.

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# Title Duration
1 AI Engineering Bootcamp: Building AI Applications with LLM APIs, LangChain + much more!Using Jupyter Notebook 02:02
2 Using Jupyter Notebook 09:24
3 Using Virtual Environments (venv) 10:07
4 Getting Started with the requests and httpx Libraries in Python 08:50
5 Handling HTTP Errors 04:39
6 Managing HTTP Authentication and Headers (OpenAI API) 09:59
7 Setting Up the Environment: Jupyter Notebook and Pandas 03:55
8 Introduction to Pandas: Series and DataFrames 06:09
9 Importing and Exporting Data: Working with CSV Files 06:38
10 Exporting Data to Different Formats: Excel, JSON, SQL, YAML 07:47
11 Modifying Data: Adding and Dropping Columns and Rows 06:05
12 Accessing Data: Using df.iloc[] and df.loc[] 05:43
13 Sampling and Previewing Data: Using df.sample() and df.head() 06:15
14 Filtering Data: Masks and pandas.Series.between() 07:15
15 Sorting Data: Understanding Pandas Sorting Methods 07:11
16 Handling Missing Data 04:44
17 Aggregations and Grouping Data 04:54
18 Project: Analyzing Website Traffic Data 04:33
19 Time Series Data Manipulation in Pandas 06:59
20 Foundations of LLMs and Generative AI 08:32
21 Tokens, Context Windows and Cost 05:26
22 Exploring LLM APIs: AI as a Service 09:23
23 OpenAI Playground, Google AI Studio, and Anthropic Workbench 06:06
24 Challenges and Limitations of LLMs 09:03
25 The State of AI: Present and Future – The Good and the Bad 10:06
26 Pretraining Data (Internet) 06:41
27 Tokenization 06:07
28 Training the Neural Network 09:26
29 Post-Training: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) 08:26
30 Reinforcement Learning (RL) 05:30
31 Becoming Better than Humans: AGI and ASI with RL 07:32
32 Reinforcement Learning with Human Feedback (RLHF) 06:23
33 How to Deal With Hallucinations 07:37
34 Using Tools: Internet Search, Interpreter, and Deep Search 07:49
35 Big Ideas Recap (Core Summary) 09:51
36 Authenticating to OpenAI using Python Dotenv 08:17
37 Chat Completions Endpoint 06:58
38 Developer Message 04:31
39 Streaming API Responses 04:31
40 Using Local Base64 Images as Input 06:44
41 Using Online Images as Input 02:05
42 Chat Completion API Parameters: Temperature and Seed 06:14
43 Chat Completion API Parameters: Top P, Max_Tokens, Penalties 09:50
44 Diving into OpenAI’s Reasoning Models (o1 and o3) 07:56
45 Best Practices for Prompting Reasoning Models 05:26
46 Transcriptions with Whisper 05:48
47 Translations with Whisper 03:12
48 Text-to-Speech (TTS) API 07:03
49 Generating Original Images Using the DALL-E 3 10:50
50 Creating Variations of Images with DALL-E 03:05
51 Editing Images with DALL-E 05:40
52 Intro to Prompt Engineering 02:41
53 Tactic 1: Position Instruction Clearly with Delimiters 04:13
54 Tactic 2: Provide Detailed Instructions for the Context 06:38
55 Tactic 3: Use the Rich Text Format (RTF) 07:46
56 Tactic 4: Few Shot Prompting 08:13
57 Tactic 5: Specify the Steps Required to Complete a Task 05:17
58 Tactic 6: Give Models Time to Think 02:13
59 Other Tactics and Principles for Better Prompting 05:38
60 Avoid Hallucinations Using Guarding 03:07
61 Summary 02:07
62 Project Introduction 02:32
63 Creating a Daily Meal Plan Using OpenAI API 05:39
64 Creating the Prompt 08:43
65 Running the Program 03:24
66 Generating Original Images for the Recipes using DALL-E 11:54
67 Narrate the Meals using the Text-to-Speech Model 10:24
68 Setting Up the Python SDK and Authenticating for Gemini API 09:51
69 Generating Text From Text Prompts 04:15
70 Streaming Gemini Responses 02:59
71 Generating Text From Images 05:49
72 Gemini API Generation Parameters: Controlling How the Model Generates Responses 06:12
73 Gemini API Generation Parameters Explained 10:14
74 Building Chat Conversations 07:54
75 Project: Building a Conversational Agent Using Gemini Pro 07:19
76 System Instructions 05:43
77 The File API: Prompting with Media Files 06:09
78 Tokens 06:42
79 Prompting with Audio 04:21
80 Project Requirements 05:54
81 Building the Application 05:23
82 Testing the Application 01:49
83 Streamlit: Transform Your Jupyter Notebooks into Interactive Web Apps 02:49
84 Creating the Web App Layout With Streamlit 11:20
85 Saving and Displaying the History Using the Streamlit Session State 05:20
86 Exercise: Imposter Syndrome 02:57
87 Project Introduction 00:57
88 Getting Images Using a Generator 06:18
89 Renaming Images Using Gemini 09:35
90 LangChain Demo 05:06
91 Introduction to LangChain 05:10
92 Working with the OpenAI Models 08:43
93 Caching LLM Responses 04:57
94 LLM Streaming 02:58
95 Prompt Templates 05:36
96 ChatPromptTemplate 05:55
97 Understanding Chains 07:48
98 Installing the Python Libraries for Gemini and Authenticating to Gemini 04:31
99 Integrating Gemini with LangChain 06:02
100 Using a System Prompt and Enabling Streaming 06:32
101 Multimodal AI With Gemini 14:13
102 LangChain Tools: DuckDuckGo and Wikipedia 11:08
103 Creating a React Agent 13:30
104 Testing the React Agent 04:50
105 Intro to OpenAI's Text Embeddings 03:16
106 Generating Simple Embeddings 05:54
107 Embedding the Dataset for Similarity Searches 04:52
108 Estimating Embedding Costs With Tiktoken 05:12
109 Performing Semantic Searches 07:05
110 Project Introduction 06:09
111 Loading Your Custom (Private) PDF Documents 07:28
112 Loading Different Document Formats 05:13
113 Public and Private Service Loaders 04:38
114 Chunking Strategies and Splitting the Documents 06:39
115 Intro to Vector Stores and Authenticating to Pinecone 09:02
116 Working with Pinecone Indexes 09:32
117 Working with Vectors 08:43
118 Pinecone Namespaces 06:44
119 Embedding and Uploading to a Vector Database (Pinecone) 13:53
120 Asking and Getting Answers 11:43
121 Using Chroma as a Vector DB 11:11
122 Adding Memory to the RAG System (Chat History) 09:26
123 Using a Custom Prompt 08:10
124 Introduction to Agents and ReAct 04:20
125 Creating the Agent Class 02:42
126 Creating the ReAct Prompt 02:31
127 Creating the Tools 02:41
128 Testing the Agent 06:06
129 Automating the Agent 07:01
130 LangGraph Concepts and Core Components 05:43
131 Building a Chatbot 05:30
132 Visualizing the Graph 02:13
133 Running the Chatbot 01:32
134 Tavily AI 08:29
135 Enhancing the ChatBot with Tools 08:17
136 Adding Memory to the Chatbot 07:06
137 Intro to Reflection 02:14
138 Generate 04:16
139 Reflect and Repeat 02:33
140 Define the Graph - Part 1 03:44
141 Define the Graph - Part 2 02:49
142 Running the App 03:55
143 Intro to LangSmith 03:29
144 Setting Up LangSmith 01:55
145 Tracing with LangSmith 06:17
146 Tracing the Reflective Agentic App 03:51
147 Project Overview 01:48
148 Defining the Agent State and the Prompts 07:39
149 Implementing Agents and Nodes 09:39
150 Defining the Conditional Edge 01:27
151 Defining the Graph 04:25
152 Running the App 04:07
153 Tracing the App with LangSmith 02:51
154 Note 02:16
155 Application Overview 03:34
156 Extracting Data from ArXiv with Pandas 12:44
157 Downloading Research Papers 04:53
158 Loading, Splitting and Expanding Data 09:54
159 Building a Knowledge Base for RAG 05:35
160 Creating a Pinecone Index 07:17
161 Loading the Knowledge Base and Deploying to Pinecone 05:04
162 Developing Custom Tools 05:13
163 Implementing the ArXiv Fetch Tool 08:01
164 Unlocking Web Search with Google SerpAPI 03:29
165 Building Google SerpAPI Tools 04:26
166 Creating RAG Tools 06:20
167 Implementing the Final Answer Generation Tool 02:18
168 06_14 Initializing the Oracle LLM 11:02
169 Testing the Ecosystem 03:33
170 Building a Decision-Making Pipeline 08:34
171 Defining the Agent State 03:25
172 Defining the Graph 06:36
173 Generating Reports 04:27
174 Building the Final Research Report 05:20
175 Concluding the Project 06:23
176 Understanding Python Modules 06:17
177 The OS Module 07:57
178 Advanced Import Techniques and Best Practices 04:11
179 Using __name__ == '__main__' for Modular and Reusable Code 06:24
180 Mastering Python Package Management with pip 08:35
181 Thank You! 01:18

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