Mô tả

This Online Bootcamp is a compact and accelerated version of our 400-hour in-person master's program.


It has two parts:

- In Part 1, you will learn the keys to Artificial Intelligence and the new Generative AI, as well as its potential to revolutionize businesses, startups, and employment.

- In Part 2, you will learn to build professional-level LLM Applications, the most potential applications of Generative AI.


By the end of this program, you will know how to do the following:

AI AND BUSINESS

  • Know the businesses that AI puts at risk of disappearing.

  • Know the new opportunities created by AI for businesses.

  • Design a plan to introduce AI into your company.

  • Select an appropriate pilot project to introduce AI into your company.

  • Form the first AI team in your company.

  • Prepare your company's AI strategy.


AI AND STARTUP

  • Identify 100 opportunities to create AI startups.


AI AND EMPLOYMENT

  • Know the professions that AI puts at risk of disappearing.

  • Know the new professions created by AI.


LLM APPLICATIONS, THE APPLICATIONS WITH THE GREATEST POTENTIAL OF GENERATIVE AI.

  • Know the main use cases of LLM Applications in businesses and startups.


CREATION OF PROFESSIONAL LLM APPLICATIONS.

  • You will learn the Architecture of an LLM Application.

  • You will learn how to learn programming languages like Python and Javascript.

  • You will learn to work with your computer's terminal.

  • You will learn to work with Jupyter notebooks.

  • You will learn to work with code editors like Visual Studio Code.

  • You will learn to work with virtual environments.

  • You will learn to work with hidden files to save credentials.

  • You will learn the RAG (Retrieval Augmented Generation) technique.

  • You will learn to use LangChain.

  • You will learn to use the LangChain Expression Language (LCEL).

  • You will learn to use the new version v010 of LangChain.

  • You will learn to use LlamaIndex.

  • You will learn to use the OpenAI API.

  • You will learn to use OpenAI's functions.

  • You will learn to use LangSmith.

  • You will learn to use LangServe.

  • You will learn to use templates of LangChain and LlamaIndex.

  • You will learn what AI Agents are and how to create them.

  • You will learn to create prototypes (demos) of LLM applications with LangChain and Streamlit.

  • You will learn to create full-stack CRUD applications with Nextjs, FastAPI, and Postgres.

  • You will learn to create professional full-stack LLM applications with LangChain, LlamaIndex, Nextjs, FastAPI, and Postgres.

  • You will learn to use vector and traditional databases.

  • You will learn to deploy applications on Vercel and Render.

  • You will learn to use AWS S3 as a remote storage platform.

  • You will learn to use ChatGPT as a programming assistant.

  • You will learn to work with Github and Github Codespaces.

  • You will learn what LLMOps is and how to use it in your LLM Applications.

  • You will learn the principles of Responsible AI and how to use them in your LLM Applications.


The Bootcamp consists of:

  • 238 lessons divided into 36 sections.

  • More than 200 videos.

  • More than 150 attached presentations.

  • More than 70 practical notebooks.

  • 17 practical code repositories on Github.

  • 25 LLM applications of different difficulty levels: basic, intermediate, and advanced.

  • Material for more than 100 hours of study and practice for the student.

  • 2 downloadable books valued at $50: "Keys to Artificial Intelligence" and "100 AI Startups that made more than $500,000 before the first year".


Topics included in this Bootcamp:

AI, Generative AI, AI Applications, LLM Applications, Full-Stack Applications, LangChain, LangChain Expression Language (LCEL), LangChain v010, LlamaIndex, OpenAI, OpenAI API, RAG, RAG Technique, Vector databases, Postgres, Pinecone, Chroma, DeepLake, Streamlit, Nextjs, Vercel, FastAPI, Render, AWS S3, LangSmith, LangServe, LangChain Templates, LlamaIndex Templates, LLMOps, Responsible AI.

Bạn sẽ học được gì

Keys to Artificial Intelligence and the new Generative AI.

Keys to LLM Applications, the highest potential applications of Generative AI.

How to create an LLM Application from scratch to professional level.

Opportunities and threats of AI for businesses, startups, and jobs.

Professional opportunities opened by Artificial Intelligence.

Steps to become an Artificial Intelligence Engineer.

How to introduce Artificial Intelligence into your business.

Architecture of professional LLM Applications.

The RAG Technique (Retrieval Augmented Generation).

Artificial Intelligence Agents.

Basic and advanced LangChain, LangChain LCEL, and LangChain v010. LangSmith, LangServe, LangChain Templates.

Basic and advanced LlamaIndex. LlamaIndex Templates.

ChatGPT, OpenAI, OpenAI functions, and the OpenAI API.

Large Language Models (LLM): ChatGPT, Llama2, Mistral, Falcon, etc.

Vector databases: Postgres, Pinecone, Chroma, FAISS, DeepLake, etc.

Full-Stack Applications: Nextjs and FastAPI.

Professional deployment: Vercel and Render.

Provisional deployment: Streamlit.

Cloud hosting: AWS S3.

LLMOps.

How to apply the principles of Responsible AI.

Daily tools of the AI Engineer: Jupyter Notebooks, Python, Terminal, Github, Codespaces, etc.

Yêu cầu

  • No previous technical knowledge is required.
  • Students with some prior knowledge will improve their professional preparation.

Nội dung khoá học

41 sections

Program presentation

13 lectures
Program presentation
01:05
Opportunities this program will open for you
04:47
What will you learn in this program
07:05
Materials included in the program
01:50
Who is this program for
01:46
What makes this program different
03:49
Become part of the Alumni Network
01:32
Distinguish yourself as Honor Student
04:42
Get Consulting Gigs through the Alumni Network
14:18
Get Job Offers through the Alumni Network
09:25
Find Future Startup Team Members through the Alumni Network
06:13
Introduction of the Instructor
02:07
Share your progress
01:38

Tips for the students

5 lectures
Tips for the students
00:26
Practical tips for the students
04:33
The secret to successfully completing this bootcamp
06:46
The 3 Alternative Rhythms: Beginner, With some Experience, Expert
12:26
IMPORTANT: Please do this if you want us to keep adding new sections
05:16

INTRODUCTION: LLM Apps, the key to the New AI

2 lectures
LLM Apps, the key to the New AI
00:32
LLM Apps and the universalization of AI
07:59

ChatGPT vs. LLM Apps

1 lectures
ChatGPT vs. LLM Apps
09:17

Download the two books included with the program

1 lectures
Download the two books included with the program
00:04

PART 1: IMPORTANCE OF ARTIFICIAL INTELLIGENCE AND GENERATIVE AI.

3 lectures
Intro to Artificial Intelligence
00:32
Artificial Intelligence: What is it? Why is so popular now? How important is it?
15:04
Changes introduced by AI: Introduction
08:32

AI: Changes in Employment

6 lectures
AI: Changes in Employment
01:00
Jobs that will benefit the most from AI
03:52
Jobs most affected by AI
03:40
Jobs least affected by AI
03:43
New professions created by AI
08:25
The new AI Engineers
07:40

AI: Changes in Businesses

5 lectures
AI: Changes in Businesses
00:48
Consequences of the changes in employment
03:34
Industries with high impact
03:22
Industries with median impact
04:48
Industries with immediate impact
04:17

AI: Changes in Startups

5 lectures
AI: Changes in Startups
01:06
Opportunities for Startups: Characteristics of the New AI
10:45
Opportunities for Startups: Changes in Employment
02:46
Opportunities for Startups: AI impact on Businesses
02:27
Opportunities for Startups: Book “100 AI Startups”
05:13

AI: Changes in Society

3 lectures
AI: Changes in Society
00:38
Social changes generated by the New AI
03:00
Social challenges generated by the New AI
02:40

How to introduce AI in your company

7 lectures
How to introduce AI in your company
01:08
Plan to introduce AI in your company
03:41
Tech and Business Analysis to introduce AI in your company
04:50
How to select the right pilot project to introduce AI in your company
04:22
How to form the first AI Team in your company
03:11
How to prepare the AI Strategy of your company
02:34
Example: Plan to adopt a new LLM App in your company
03:55

The new AI Training

5 lectures
The new AI Training
00:52
AI Training in your company: an strategic necessity
01:25
Who should get AI Training in your company?
02:07
How to design an AI Training Plan for your company
02:02
The new AI Training for engineers
05:28

The new AI creates opportunities for consulting, advisors and marketing agencies

2 lectures
The new AI creates opportunities for consulting, advisors and marketing agencies
01:09
Opportunities for consulting firms, business advisors, and marketing agencies
07:02

PART 2: LLM APPS, THE GENERATIVE AI APPLICATIONS WITH THE HIGHEST POTENTIAL

7 lectures
Intro: LLM Apps, the Generative AI applications with the highest potential
01:10
What is an LLM App?
05:25
The myth of pre-requisites for learning
06:38
Most frequent types of students: reasons to learn
05:53
DIY or hire an external professional?
04:35
The long way from the “toy demo” to the professional app
04:12
Job opportunities for the LLM App Developer
06:48

Use Cases for LLM Applications

6 lectures
Use Cases for LLM Applications
04:22
Use Cases for LLM Apps by Industry
14:28
Use Cases for LLM Apps in Startups
04:16
Use Cases for LLM Apps in Professions
05:03
Most frequent Use Cases for LLM Apps
04:44
Use Cases for LLM Apps by autonomy level
05:35

Intro to LLMs

4 lectures
Intro to LLMs
00:38
Origins of LLM Apps: AI, ML, NLP, Generative AI, LLMs
04:45
LLM: size, precision, and cost
04:35
The Foundation LLM Models
03:24

LLMs: Basic Concepts

6 lectures
LLMs: Basic Concepts
00:39
What is the Context Window?
02:25
What are Tokens?
02:30
What are Prompts?
03:29
What is Prompt Engineering?
14:52
What are Hallucinations?
04:57

Architecture of an LLM App

5 lectures
Architecture of an LLM App
00:34
Basic Architecture of an LLM App
01:45
Advanced Architecture of an LLM App
04:15
Preview of a professional LLM App (1)
08:47
Preview of a professional LLM App (2)
03:57

Details of the advanced architecture of an LLM Application

5 lectures
Details of the advanced architecture of an LLM Application
00:55
Selection of Foundation LLMs
03:58
Stack of tools
09:46
Orchestration Frameworks
05:25
Other interesting notes
03:33

The RAG Technique (Retrieval Augmented Generation)

5 lectures
The RAG Technique (Retrieval Augmented Generation)
00:44
Basic Concepts
15:23
Components
05:49
RAG Technique: Advanced Concepts
04:40
Challenges
02:46

Selecting Orchestration Framework: LangChain, LlamaIndex or OpenAI API?

3 lectures
Selecting Orchestration Framework: LangChain, LlamaIndex or OpenAI API?
00:48
LangChain, LlamaIndex or OpenAI API?
21:29
[UPDATE] LangChain makes a great move: implications of the LangSmith News
12:25

Intro to the usage of Programming Languages

17 lectures
Intro to the usage of Programming Languages
00:48
Never programmed before? Do not worry.
06:50
Practical Tips if you are new to programming
12:48
DEMO: W3Schools and ChatGPT in action
05:58
[NEW] Python and Javascript for LLM App Developers: the important parts (part 1)
05:18
[NEW] Python and Javascript for LLM App Developers: the important parts (part 2)
51:52
Virtual environment: what is it, why is it important and how to create one (1)
01:08
Virtual environment: what is it, why is it important and how to create one (2)
07:57
[NEW] Create a virtual env and install Jupyter Lab to access notebooks (1)
01:50
[NEW] Create a virtual env and install Jupyter Lab to access notebooks (2)
05:15
FOR WINDOWS SYSTEMS: How to create a virtual env and install Jupyter Lab
01:31
Terminal: what is it, why is it important, basic operations (1)
00:50
Terminal: what is it, why is it important, basic operations (2)
03:39
File for secret credentials: why is it important, how to create it
01:01
How to create and read Hybrid Notebooks (code + text) with Jupyter (1)
01:22
How to create and read Hybrid Notebooks (code + text) with Jupyter (2)
01:44
QUICK INSTRUCTIONS: How to create a Notebook and how to perform main operations.
01:52

Basic LangChain

18 lectures
Warning: Modifications introduced by the LangChain v0.1.0 version
14:10
Examples of .env file and /data folder
04:29
Links to /data folder and example of .env file
00:02
Basic LangChain
01:16
Basic LangChain in 15 minutes
23:28
Models
06:32
Prompts and prompt templates
05:06
Few shot prompt templates
05:10
Output parsers
07:39
Memory
07:42
Chains
13:56
Document loaders
07:12
Splitters
11:03
Callbacks
06:41
OpenAI functions
18:22
Connect with fastAPI
05:30
Agents
09:37
Indexing API
03:35

LangChain Expression Language

6 lectures
LangChain Expression Language
02:23
LCEL: Chains
03:36
LCEL: Output parsers
02:18
LCEL: Arguments
02:06
LCEL: OpenAI functions
01:45
LCEL: RAG Applications
05:59

LangChain Advanced Components

5 lectures
LangChain Advanced Components
01:14
LangSmith
08:19
LangServe
03:53
LangChain Templates
05:12
The new LangChain Chatbot
01:21

Level 1 Applications: “Toy Demos” with LangChain

16 lectures
Reminder: Modifications introduced by the LangChain v0.1.0 version
14:10
Reminder: Examples of .env file and /data folder
04:29
Reminder: Link to /data folder and sample .env file
00:02
Level 1 Applications: “Toy Demos” with LangChain
02:23
Note: Quick trick to avoid the "deprecation warning" messages
00:14
Basic app: summarize long article
05:27
Basic RAG app: document QA
09:09
Basic app: extract structured data from conversation
07:03
Basic app: eval of QA app
10:26
Basic app: ask a database
05:13
Basic app: ask a github repo
04:22
Basic app: ask an API
05:31
Basic app: chatbot with personality and memory
03:42
Basic app: RAG with DeepLake
15:15
Basic app: simple agent
12:07
Basic app: advanced output parser
11:45

Level 2 Applications: “Toy Demos” with LangChain and “Toy UIs” with Streamlit

23 lectures
Reminder: Modifications introduced by the LangChain v0.1.0 version
14:10
Reminder: Examples of .env file and /data folder
04:29
Reminder: Link to /data folder and sample of .env file
00:02
Level 2 Applications: “Toy Demos” with LangChain and “Toy UIs” with Streamlit
01:09
Intro to the level 2 apps
07:13
From Proof of Concept to Production
07:39
Basic Streamlit
18:36
App for re-writing informal text
17:03
Link to download the code of the app in Github and URL to try the app
00:05
App to write a Blog Post from a topic
06:27
Link to download the code of the app in Github and URL to try the app
00:05
App to summarize the content of a TXT file
04:59
Link to download the code of the app in Github and URL to try the app
00:05
App to summarize text
03:34
Link to download the code of the app in Github and URL to try the app
00:05
App to Extract Key Data from a Product Review
05:22
Link to download the code of the app in Github and URL to try the app
00:05
RAG App to ask about the content of a private PDF file
04:00
Link to download the code of the app in Github and URL to try the app
00:05
RAG App to ask about the content of a private CSV file
02:50
Link to download the code of the app in Github and URL to try the app
00:05
App to Evaluate a RAG App
06:31
Link to download the code of the app in Github and URL to try the app
00:05

LlamaIndex

3 lectures
LlamaIndex
02:55
Introduction to LlamaIndex
10:45
LlamaIndex in depth
19:38

The OpenAI API

4 lectures
The OpenAI API
01:25
The OpenAI API as alternative to LangChain and LlamaIndex
17:49
The OpenAI API in depth
04:24
The OpenAI Functions
03:20

Intro to Level 3 LLM Applications: Professional Applications

12 lectures
Intro to Level 3 LLM Applications: Professional Applications
00:48
Intro to Full-Stack Applications (1)
01:22
Intro to Full-Stack Applications (2)
20:51
Front-End Key Elements in a Level 3 Application
01:08
Next.js and Vercel
09:43
Front-End Key Elements with an Orchestration Framework: LlamaIndex
04:53
Link to download the code of the previous lesson from Github
00:00
Back-End Key Elements in a Level 3 Application
01:14
FastAPI
03:33
Link to download the code of the previous lesson from Github
00:00
Back-End Key Elements with an Orchestration Framework: LangChain
05:09
Link to download the code of the previous lesson
00:00

Level 3 LLM Applications: Professional Applications

35 lectures
Level 3 LLM Applications: Professional Applications
01:11
Reminder: Architecture of an Advanced LLM App
04:15
Reminder: Preview of a Professional LLM App (1)
08:47
Reminder: Preview of a Professional LLM App (2)
03:57
Reminder: main elements of a Professional LLM App
06:44
Basic Level 3 App: CRUD with FastAPI, Postgres and Next.js
07:04
Link to download the code of the Basic Level 3 App from Github
00:00
Basic Level 3 App: What is CRUD? (1)
06:05
Basic Level 3 App: What is CRUD? (2)
01:38
Basic Level 3 App: How to build the Backend (1)
01:36
Basic Level 3 App: How to build the Backend (2)
33:40
Basic Level 3 App: How to build the Backend (3)
14:42
Basic Level 3 App: How to build the Frontend (1)
02:25
Basic Level 3 App: How to build the Frontend (2)
12:58
Basic Level 3 App: How to start the Full Stack App (1)
01:22
Basic Level 3 App: How to start the Full Stack App (2)
03:50
Basic Level 3 App: How to deploy the backend to Render.com (1)
01:38
Basic Level 3 App: How to deploy the backend to Render.com (2)
07:28
Basic Level 3 App: How to deploy the frontend to Vercel.com (1)
01:39
Basic Level 3 App: How to deploy the frontend to Vercel.com (2)
05:24
Medium Level 3 App: CRUD integrated with AWS S3 (1)
02:27
Link to download the App code from Github
00:00
Medium Level 3 App: CRUD integrated with AWS S3 (2)
21:23
Medium Level 3 App: CRUD integrated with AWS S3 (3)
01:04
Basic Level 3 App with Orchestration Frameworks and LLMs (1)
03:56
Reminder: Modifications introduced by LangChain v010
14:10
Link to download the App code from Github
00:00
Basic Level 3 App with Orchestration Frameworks and LLMs (2)
09:27
Medium Level 3 App with Orchestration Frameworks and LLMs (1)
03:52
Link to download the App code from Github
00:00
Medium Level 3 App with Orchestration Frameworks and LLMs (2)
05:21
Advanced Level 3 LLM App (1)
05:08
Advanced Level 3 LLM App (2)
11:25
Other interesting Level 3 LLM Apps (1)
01:04
Other interesting Level 3 LLM Apps (2)
08:36

LLM Applications: Advanced Concepts

4 lectures
LLM Applications: Advanced Concepts
00:44
Database preparation
04:15
RAG Optimization: Advanced Concepts
06:28
Latency and Speed in LLM Applications
05:33

Cost control in LLM Applications

1 lectures
Cost control in LLM Applications
07:31

LLMOps

7 lectures
LLMOps
01:04
Intro to LLMOps
10:10
Evaluation: Misaligned Behavior
03:59
Evaluation: Lack of Reproducibility
01:40
Lifecycle Management
05:16
Responsible AI
12:50
LLMOps Solutions
08:49

[NEW] LLMOps with LangSmith: LLMOps Cycle & How LangSmith solves the challenges

13 lectures
The new LangSmith Announcement
01:35
The new LangSmith Announcement: Summary and Implications (part 1)
03:31
The new LangSmith Announcement: Summary and Implications (part 2)
09:00
Lessons learned: The Full Cycle of Building Professional LLM Apps (part 1)
02:16
Lessons learned: The Full Cycle of Building Professional LLM Apps (part 2)
03:22
Lessons learned: Main Challenges on Each Phase of the LLMOps Cycle (part 1)
03:40
Lessons learned: Main Challenges on Each Phase of the LLMOps Cycle (part 2)
06:04
How LangSmith Solves the Challenges of the Prototyping Phase (part 1)
03:29
How LangSmith Solves the Challenges of the Prototyping Phase (part 2)
15:28
How LangSmith Solves the Challenges of the Beta Testing Phase (part 1)
01:27
How LangSmith Solves the Challenges of the Beta Testing Phase (part 2)
09:48
How LangSmith Solves the Challenges of the Production Phase (part 1)
02:28
How LangSmith Solves the Challenges of the Production Phase (part 2)
04:29

[NEW] LLMOps with LangSmith: LangSmith in Depth

19 lectures
LangSmith in Depth
02:05
LangSmith: Official Definition, Terminology and FAQs (part 1)
02:59
LangSmith: Official Definition, Terminology and FAQs (part 2)
20:21
LangSmith: Initial Operations (part 1)
01:03
LangSmith: Initial Operations (part 2)
08:29
LangSmith solutions for the Prototyping Phase (part 1)
02:32
LangSmith solutions for the Prototyping Phase (part 2)
10:09
LangSmith Datasets: Advanced Tips (part 1)
01:13
LangSmith Datasets: Advanced Tips (part 2)
06:18
LangSmith solutions for the Beta Testing Phase (part 1)
01:27
LangSmith solutions for the Beta Testing Phase (part 2)
08:45
LangSmith solutions for the Production Phase (part 1)
01:23
LangSmith solutions for the Production Phase (part 2)
06:15
LangSmith: Advanced Tips (part 1)
01:14
LangSmith: Advanced Tips (part 2)
03:58
LangSmith: How-To Guides, Use Case Guides, and Recommendations (part 1)
01:17
LangSmith: How-To Guides, Use Case Guides, and Recommendations (part 2)
04:39
LangSmith Next Features: Detailed View (part 1)
01:08
LangSmith Next Features: Detailed View (part 2)
03:25

[NEW] LangSmith Versions

1 lectures
LangSmith Versions
04:02

[NEW] LangSmith At Work: From Basic Example to Professional Project

3 lectures
LangSmith At Work: From Basic Example to Professional Project (part 1)
01:07
LangSmith At Work: From Basic Example to Professional Project (part 2)
02:00
LangSmith At Work: From Basic Example to Professional Project (part 3)
05:21

[NEW] Multimodal LLM Applications

10 lectures
Multimodal LLM Applications
01:24
What are Multimodal LLM Applications?
02:12
What LLM Models will we use to create Multimodal LLM Apps?
05:10
GPT4-Vision: How to Use It
02:28
GPT4-Vision: Main Use Cases
11:20
GPT4-Vision: Limitations
06:39
If GPT4-V is so good, why do we need Multimodal LLM Apps for?
04:02
How to Build Multimodal LLM Apps: Alternative Ways
03:56
How to Build Multimodal LLM Apps with LangChain and GPT4-Vision (1)
03:51
How to Build Multimodal LLM Apps with LangChain and GPT4-Vision (2)
25:52

Top Information Channels for AI Engineers

1 lectures
Top Information Channels for AI Engineers
06:56

Congrats! Next steps.

1 lectures
Congrats! Next steps.
09:25

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