For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3BcmeEA

Jure Leskovec
Computer Science, PhD

Having defined a GNN layer, the next design step is how to stack GNN layers together. To motivate different ways of stacking GNN layers, we first introduce the issue of over-smoothing that prevents GNNs learning meaningful node embeddings. We learn 2 lessons from the problem of over-smoothing: (1) We should be cautious when adding GNN layers; (2) we can add skip connections in GNNs to alleviate the over-smoothing problem. When the number of GNN layers is small, we can enhance the expressiveness of GNN by creating multi-layer message / aggregation computation, or adding pre-processing / post-processing layers in the GNN.

To follow along with the course schedule and syllabus, visit:
http://web.stanford.edu/class/cs224w/

Learn how to use Pandas and Python for Data Analysis, to Data Cleaning and Data Wrangling. You will learn by creating real life projects interactively to help you take the next step in your Data Science Career.

Learn more about the projects at https://www.datawars.io/freecodecamp

⚠️ Important! We encourage you to try to resolve the projects by yourself first! And watch the solution afterwards ⚠️

💻 Course created by Santiago Basulto from DataWars.
🔗 Find more interactive Data Science projects solve at: https://www.datawars.io

⭐️ Projects Covered ⭐️
⌨️ (0:00:00) Introduction

⌨️ (0:02:22) DataFrames practice: working with English Words [🟢 Easy]
This project focuses on the basics of pandas DataFrames, including understanding its structures and modifying them. The data we're using is a big dictionary of english words.
🔗 Solve the project by yourself: https://www.datawars.io/fcc-english-words

⌨️ (0:33:26) Filtering and sorting with Pokemon data [🟢 Easy]
This project focuses on the main tasks of Data Analysis: filtering and sorting and question/answering. The dataset includes information of pokemons from all generations (to make it more fun!)
🔗 Solve the project by yourself: https://www.datawars.io/fcc-filtering-pokemon

⌨️ (1:24:30) The Birthday Paradox in the NBA [🟡 Intermediate]
The Birthday Paradox answers the question: how many people do you need in the same room in order to have a probability of at least 50% that two people share a birthday. The answer is astonishing! You'll use your findings to find which teams in the NBA share player's birthdays.
🔗 Solve the project by yourself: https://www.datawars.io/fcc-birthday-nba

⌨️ (1:55:28) Matching Strings by Similarity using Levenshtein distance [🟡 Intermediate]
One of the most challenging tasks of Data Cleaning is dealing with Strings. This project is all about string handling and advanced techniques, as using the Combinatorics and the Levenshtein distance to find irregularities in company names.
🔗 Solve the project by yourself: https://www.datawars.io/fcc-string-similarity

⌨️ (2:24:15) Data Cleaning with Google Playstore dataset [🟡 Intermediate]
An all-encompassing project that covers all the aspects of Data Cleaning, including: finding and fixing null values, duplicate values, outliers and more. The data was scraped from the Google Playstore which means that is full of irregularities. The project finishes with some Data Analysis tasks!
🔗 Solve the project by yourself: https://www.datawars.io/fcc-data-cleaning-playstore

⌨️ (3:18:34) Premier League Match Analysis [🔴 Advanced]
This project increases the complexity of your Data Analysis skills, as it combines Data Cleaning, with some analysis based on grouping operations. The dataset comes from the Premier League, the top-division Football/Soccer league in England.
🔗 Solve the project by yourself: https://www.datawars.io/fcc-premier-league

⌨️ (3:53:46) NBA 2017 season analysis: joining and groupby practice [🔴 Advanced]
This project puts your Data Wrangling skills to a test, by asking you to merge different dataframes, clean them, and finish doing some analysis and question/answering. The dataset contains the full information of 2017 NBA statistics.
🔗 Solve the project by yourself: https://www.datawars.io/fcc-nba-analysis


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This is a short and very simple video I created to explain some of the concepts that go into large language models like Chat-GPT4. It covers various aspects of LLM artificial intelligence like foundations, architecture, pre-training, fine-tuning, tokenization, embeddings, self-attention, positional encoding, layers and headers, and decoding. The text was created with the use of chat gpt4. All of the images were created using Midjourney AI based on the text.

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This Introduction to Artificial Intelligence Full Course by Simplilearn provides a complete roadmap to mastering AI. It begins with an introduction to artificial intelligence, its future, and essential concepts like machine learning, deep learning, and generative AI. You’ll explore AI careers, the AI engineer roadmap, and how to land a job in this field. Dive into practical tutorials on using AI for coding, creating digital products, and building websites. Advanced topics include transformers, reinforcement learning, Q-learning, and LangChain. You’ll learn about AI bias, multimodal AI, and concepts like LLMs, benchmarking, and running models privately. The course also covers prompt engineering, data analytics with AI, and AI-powered tools like Cursor AI. Additionally, you’ll get hands-on with projects for beginners, including machine learning and AI, and explore top hackathons in India. Finally, you’ll gain insights into interview preparation and how to earn money using tools like ChatGPT.

00:00:00 Introduction To Artificial Intelligence Full Course
00:08:08 Introduction To Artificial Intelligence
00:17:14 Future Of Artificial Intelligence
00:24:13 Deep Learning Tutorial For Beginners
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00:45:55 Top 10 Technologies To Learn
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01:04:31 How Create And Sell Digital Products Using AI
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01:15:48 Artificial Intelligence Engineer Roadmap
01:30:53 How To Get A Job In Artificial Intelligence
02:27:30 What Is Machine Learning
02:33:11 AI Bias
02:38:53 Open AI Strawberry
02:45:04 Difference between Artificial Intelligence, Machine Learning, And Deep Learning
02:46:11 Run Lama Privately
02:54:55 Introduction To LLM
03:08:24 LLM Benchmarkeing
03:14:01 Mutimodel Artificial Intelligence
03:33:52 Chatgpt Analyse Tutorial
03:34:53 Transformers In Artificial Intelligence
06:59:07 Reinforcement Learning
06:59:57 Q Learning
07:22:58 Langchain Explained
07:37:26 What Is Prompt Engineering
07:59:49 Data Analytics Using Artificial Intelligence
08:01:31 Cursor AI
10:17:41 Recurrent Neural Network
10:35:26 Machine Learning Projects For Beginners
11:01:07 Build Website With Artificial Intelligence
11:19:18 Confusion Matrix In Machine Learning
11:27:32 Top Hackathons In india
11:40:51 Artificial Intelligence Projects With Source Code For Beginers
11:46:00 Generative AI Tools For Job Interview
13:41:50 Artificial Intelligence and Deep learning Interview Questions
13:42:50 How To Earn Money Using ChatGPT

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This video on the Artificial Intelligence full course video cover all the topics you need to know to become a master in AI and ML. It covers all the basics of Machine Learning, the different types of Machine Learning, and the various applications of Machine Learning used in different industries. This video will also help us understand the basics of artificial intelligence. We will look at the future of AI and listen to some of the industry experts and learn what they have to say about AI. Finally, you will learn the Top 10 Artificial Intelligence Technologies.

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What is Artificial Intelligence?
Artificial Intelligence is a method of making a computer, a computer-controlled robot, or software think intelligently like the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. The outcome of these studies develops intelligent software and systems.

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This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.

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Summary:
AI Agents seem overwhelming, but in 2026, we've gotten to the point that any non-technical person can create and manage their own AI agents to accomplish tasks. I cover everything simply: what an agent actually is, what to automate, how to start, and build two agents step by step using two of the leading platforms. Then dive into common pitfalls and how to avoid them. This is everything you need to get started with building AI agents in 2026, no coding required.

Chapters
0:00 Intro
0:50 What is an agent?
1:37 Where we're at right now
2:08 What to automate first
4:58 How to start
7:01 Time to build
7:24 Build 1
14:04 Build 2
20:43 More complex agents
22:03 Zapier vs n8n
22:40 Common Pitfalls (and how to avoid them)
24:44 The real skill

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AI agents are transforming how intelligent systems work 🤖. Deanna Berger explains how Agentic AI, workflow automation, and AI infrastructure enable smarter, autonomous solutions. See how connected agents drive innovation and real‑world impact.

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For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

October 14, 2025
This lecture covers adversarial robustness and generative models.

To learn more about enrolling in this course, visit: https://online.stanford.edu/co....urses/cs230-deep-lea

To follow along with the course schedule and syllabus, visit: https://cs230.stanford.edu/syllabus/

More lectures will be published regularly.
View the playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

Andrew Ng
Founder of DeepLearning.AI
Adjunct Professor, Stanford University’s Computer Science Department

Kian Katanforoosh
CEO and Founder of Workera
Adjunct Lecturer, Stanford University’s Computer Science Department