Alex Wang

By 2030, we’ll see 170 million jobs created — and 92 million jobs lost.
39% of current skills will be outdated.
And 59% of workers will need reskilling — but 1 in 5 may not get it.

According to the new Future of Jobs 2025 report from the World Economic Forum, AI and information processing tech are expected to transform 86% of businesses — more than any other technology.

We’re not in a “future of work” moment anymore.
We’re in a present of rapid transformation — and AI is at the center of it.

These aren’t just stats.
They’re a snapshot of the pressure — and potential — we’re all navigating.

💡 The AI-native workforce isn’t just technical.
Yes, AI and big data top the list of fastest-growing skills.
But so do:

🔹 Resilience & agility
🔹 Creative thinking
🔹 Curiosity & lifelong learning
🔹 Leadership & influence
🔹 Systems thinking

The world doesn’t just need more AI experts.
It needs more adaptive leaders who can think with AI — strategically, creatively, and ethically.

That’s the mindset we’re helping build in our Executive AI course — for leaders who want to stay ahead of the curve, not chase it.

We dive deep into skills like agentic AI, enterprise data strategy, and predictive decision-making — exactly the capabilities WEF highlights as essential for the future of work.

Enroll / More info here ➡️https://bit.ly/3C3GSgF

Curious — which of these shifts are you feeling most in your work right now?
__________

For more on AI and news, plz check my previous posts.

I share my journey here. Join me and let's grow together. Alex Wang

#leadership #ai #technology #innovation #data

The brilliant illustration that the ups and downs of life make us progress faster...

> Ups and downs are where to get our momenta, don’t lose our faith when we are at downhills
> Shortest path might not be the fastest

These days many things might be going wrong in different parts of the world but we all need to keep the faith and push forward and make the world a better place!

#inspiration

Interesting concept! Subjects that should be mandatory in schools.

If I could proposal one to include in the schools' subjects, it would be “AI Thinking.“

AI thinking involves understanding the basics of how AI works and being able to differentiate between human abilities and what AI can do; recognizing the strengths and limitations of both humans and AI.

By developing these skills, children would learn how to effectively work alongside AI and make the most of its capabilities. And enable them to comprehend the unique aspects of human capacity and develop them as their core competencies.

Nowadays, we have access to an incredible amount of information, and the way we learn and teach is changing rapidly. The future learning might become customize for each student. What subjects would you like to include in the list?

For resources and learning materials in  AI/ ML/  Data Science, please check my previous posts.

Image credit to startup_rules

I share my learning journey into AI and Data Science with my amazing LinkedIn friends, please make sure you’ve clicked follow button and let's grow together ! Alex Wang

Ants helped us to build Driverless cars!

The future of global autonomous driving networks seems to become very similar to theirs.

They use a wireless pheromone network to ensure there is no congestion even if there is a very narrow way for thousands of ants.

AI is amazing, and human intelligence is even more stunning by being inspired by nature :)

For ML/ AI/ Data Science learning materials, please check my previous posts.

I share my learning journey into Data Science with my amazing LinkedIn friends, please follow me and let's grow together! Alex Wang

#innovation #artificialintelligence #future

This is not a concept or fake. This is real technology.

Nimo Planet wants to make today's laptop history with their state-of-the-art smart glasses.

Beta was earlier this year. Expected to ship in early 2023

What do you think?

Credit PBornet

#innovation #AR #tech #VR #futureofwork

One min to have a general idea about Neural Network, which is a technique that could achieve many different aims, facial recognition, predicting, music composition, etc.

NN contains virtual ‘neurons’ that are arranged in layers that are connected to each other. The neurons pass on the information and thereby perform calculations.

The connections between the neurons also have values associated with them, called weights.

Those weights tell us how much the information from one layer matters for the next layer. The values of neurons and the weights of the connections are essentially the free parameters of the network.

By training the network, we want to find those values of the parameters that minimize a certain function, called the ‘loss function’. It is an optimization problem, we use a method called backpropagation to solve it.

Backpropagation means if the NN gives us a result that is not good enough, we go back and change the weights of neurons and their connections. This is how the network can learn from failure.

Image credit: mql5

I share my learning journey into Data Science with my amazing LinkedIn friends, please follow me and let's grow together! Alex Wang

#data #datascience #machinelearning #algorithms

Realtime Detection of feelings in the video using AI.

Will Smith looks pretty chill in this video!

Can we get fooled by people's facial expressions?

For ML/ AI/ Data Science learning materials, please check my previous posts.

Awesome project by Rondinelli Morais.

I share my learning journey into Data Science with my amazing LinkedIn friends, please follow me and let's grow together!

#innovation #artificialintelligence #machinelearning

Nicely visualized learning path to become a Data/BI analyst.
 
No one size fits all, learning paths can vary. I guess mine is pushed by work demands and less linear. I wish I started programming earlier in my learning process, even in high school. Many more opportunities come when you know programming fundamentals.
 
Here are some downloadable learning materials (PDF books) that might be helpful,
 
Probability for Data Science https://lnkd.in/gxJ9RxDs
 
Statistics and Machin Learning in Python  https://lnkd.in/gdT4s_QE 
 
Excel Bible https://lnkd.in/giqWhVFJ 
 
Machine learning https://lnkd.in/g-uxJmdn 
 
Deep Learning https://lnkd.in/gsRqaenX 
 
For more ML/ AI/ Data Science learning materials, please check my previous posts.
 
The image credited to Helen Wall
 
I share my learning journey into Data Science with my amazing LinkedIn friends, follow me and let’s grow together! Alex Wang
 
#datascience #learning

Visualization of global temperature changes from 1880 to 2023, by NASA.

Scientists found that July 2023 was the hottest month on record since 1880, likely due to human activity. 😔

*Data is collected from multiple sources, including satellites, weather stations, and buoys.

I am also curious to see what the data looks like if we can trace it back further in time, and how the patterns change when we extend the timeline to thousands or even millions of years.

For ML/ AI/ Data Science learning materials and resources, please check my previous posts. Alex Wang

When I am into something always curious about how it works!

ChatGPT was trained on massive amounts of data that have been gathered from the internet and other sources through 2021, by using Reinforcement Learning from Human Feedback (RLHF).

It first demonstrates data from humans and trains a supervised policy;
The next step is to run the model and let humans manually rank (label) the quality of outputs produced by the model from best to worst, then collect those new feedback data to train a reward model;
Then use a reinforcement learning algorithm (Proximal Policy Optimization) to optimize a policy by training a model against the reward model. (the key phases of RLHF)

ChatGPT is the third version, it is fine-tuned from a model in the GPT3.5 series (a code model) making it more human-like. Read more in the comments.

For more ML/ AI/ Data Science learning materials, please check my previous posts.

Image creadit to Yogesh Haribhau Kulkarni

I share my learning journey into Data Science with my amazing LinkedIn friends, click follow and let's grow together! Alex Wang

#datascience #machinelearning #technology #chatgpt

Nice and neat! :)

How does ChatGPT work?
https://lnkd.in/gWk4bUUa

Microsoft embedded ChatGPT into Office
https://lnkd.in/gmMXtHyn

LLMs history in one minute
https://lnkd.in/g88ncAV8

For more ML/ AI/ Data Science learning materials, please check my previous posts.

Image credit to Hasan Toor

I share my learning journey into Data Science with my amazing LinkedIn friends, click follow and let's grow together! Alex Wang

#artificialintelligence #generativeai #datascience #chatgpt #technology

OpenAI quietly released a GPT-4.1 Prompting Guide a couple of weeks ago, and it’s one of the most practical resources on how to actually use LLMs well.

Here’s what stood out:

✅ GPT-4.1 is very literal - It follows instructions exactly as written, which is powerful but also unforgiving

✅ Instruction placement matters - Putting them at the start and end of your prompt can improve results

✅ It handles long contexts better, but still benefits from clear structure (delimiters, labels, step-by-step thinking)

✅ Prompt techniques like internal monologue, self-critique, and chain-of-thought can boost reliability

📌 If you’re using ChatGPT, you’re likely using GPT-4 (or now GPT-4o).
📌 But GPT-4.1 is only available through the OpenAI API — and it’s built for more controllable, consistent output in production settings.

Quick takeaway:

If you're writing content, brainstorming, or doing 𝗴𝗲𝗻𝗲𝗿𝗮𝗹 𝘄𝗼𝗿𝗸 → ChatGPT with GPT-4o is great

If you're 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀, 𝗮𝗽𝗽𝘀, or need 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 over the full prompt/response pipeline → GPT-4.1 via API is a better fit

*If you’re building agents, coding copilots, or anything remotely complex, this doc is gold. Sign up to our AI community to learn more for free: https://lnkd.in/gAE32Fae
__________

For more on AI and news, plz check my previous posts.

I share my journey here. Join me and let's grow together. Alex Wang

Autopilot tech by Tesla.

A full build of Autopilot neural networks involves 48 networks that take 70,000 GPU hours to train. Together, they output 1,000 distinct tensors (predictions) at each timestep...read more in the comments.

Cannot wait to see a fully self-driving car in the future!

For ML/ AI/ Data Science learning materials, please check my previous posts.

I share my learning journey into Data Science with my amazing LinkedIn friends, please follow me and let's grow together! Alex Wang

#artificialintelligence #deeplearningai #technology

Best online courses to start in Gen AI, Machine Learning, and Data Science in 2024

*all are free to listen and learn, but a few short courses require payment for certification

𝐇𝐚𝐫𝐯𝐚𝐫𝐝 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 | 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 (𝐛𝐚𝐬𝐢𝐜𝐬)

◼️ Statistics and R https://lnkd.in/ggwKPfH3

◼️ Probability https://lnkd.in/gRA-sCrU

◼️ Linear Regression https://lnkd.in/gCqYdf_W

◼️ Inference and Modelling https://lnkd.in/gSnuYTMW

𝐇𝐚𝐫𝐯𝐚𝐫𝐝 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 | 𝐌𝐋 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 (𝐟𝐫𝐨𝐦 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐨𝐫𝐲 𝐭𝐨 𝐈𝐧𝐭𝐞𝐫𝐦𝐞𝐝𝐢𝐚𝐭𝐞, 𝐛𝐞𝐠𝐢𝐧𝐧𝐞𝐫-𝐟𝐫𝐢𝐞𝐧𝐝𝐥𝐲)

◼️ Data Science: Machine Learning
https://lnkd.in/gUNVYgGB

◼️ High-dimensional data analysis
https://lnkd.in/gv9RV9Zc

◼️ Introduction to Programming with Python
https://lnkd.in/gAdyf6xR

𝐑𝐞𝐩𝐮𝐭𝐚𝐛𝐥𝐞 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐬𝐮𝐜𝐡 𝐚𝐬 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭, 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧… | 𝐒𝐡𝐨𝐫𝐭 𝐜𝐨𝐮𝐫𝐬𝐞𝐬 𝐰𝐢𝐭𝐡 𝐜𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐞 | 𝐆𝐞𝐧𝐀𝐈

◾AI for Beginners
https://lnkd.in/dHeHqtAA 
*beginner-friendly course, that covers the basics of ML, its applications, and real-world use cases, providing a strong foundation in the field

◾AI for Everyone
https://lnkd.in/dtFpAQkg 
*designed for a general audience to grasp the essentials of AI, its impact, and its potential in everyday life

◾Generative AI
https://lnkd.in/dwruHPSj 
*explore how GenAI is redefining online search and making it analytical, learn effective search strategies

◾Deep Learning Specialization
 https://lnkd.in/dCCjRfTc 
*a key AI technology, and learn about its mechanisms, applications, and the skills needed to develop DL models

◾Introduction to Large Language Models 
https://lnkd.in/ghk5eygZ 
*Learn about the development, capabilities, and applications of LLM, including their role in advancing natural language processing

◾Introduction to Responsible AI 
https://lnkd.in/ghfQiMmP 
*the principles of responsible AI development, including fairness, transparency, and accountability in AI systems

◾Ethics in the Age of Generative AI
 https://lnkd.in/dD63DHUs 
*ethical challenges in deploying Generative AI, and learning about frameworks for ethical analysis and decision-making in AI applications
______________

I share my learning journey in AI. Join me and let's grow together!

For more learning materials, please check my previous posts. Alex Wang

#datascience #machinelearning #artificialintelligence #technology

A really nice, well-covered knowledge page for Data & Data Science

Introduction level, which covers the concepts of traditional data, big data; what's business intelligence, machine learning, what are the traditional methods we are using now, and what's the near future trend...in a timeline manner.

As well as the business use cases, major technicals, and the tools to learn. It's also quite interesting to check the last row.

Would love to see a page for Machine learning :)

For downloadable ML/ AI/ Data Science learning materials, please check my previous posts.

Image credit to 365 Data Science on Undemy

I share my learning journey into Data Science with my amazing LinkedIn friends, click follow and let's grow together! Alex Wang

#data #datascience #machinelearning #technology #businessanalytics

One of the most comprehensive cheetsheet about Data Science. It covers all the essential topics you need to know as a data science learner. I would highly recommend using it as a roadmap or quick review for your learning.

Credit: Fjodor van Veen

*** It could be downloaded directly from the post by making it full screen and then click the download icon on the top right corner.***

For more ML / DS / AI learning materials, please check my previous posts.

I share my learning journey into Data Science with my amazing LinkedIn friends, please follow me and let's grow together! Alex Wang

#artificialintelligence #datascience #machinelearning #deeplearning #python

Monthly Book Review: Two books for building with GenAI (Foundations + Applications)

Took me five months to get to my second book review, but hopefully still counts as a series, right? 😂

This time, I’m sharing two books together - 
focus on different stages, make a good match to cover a lot of ground for anyone building real-world GenAI applications.

Highly recommend especially if you're working on any serious LLM projects this year. Let's start!

📘 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 𝐚𝐧𝐝 𝐏𝐲𝐓𝐨𝐫𝐜𝐡 (𝟐𝐧𝐝 𝐄𝐝𝐢𝐭𝐢𝐨𝐧) - recommend to start with this one first

In short, this book is about understanding how modern GenAI models work and building them from scratch - great if you want to dive deep, and not just how to prompt.

It starts with the foundations: transformers, embeddings, training LLMs; and then moves into more advanced topics like fine-tuning models, building diffusion models (for image generation), and has a touch on the latest trends like multimodal AI.

What I personally liked the most:

- It's technical, but very approachable if you have some Python background.

- A strong focus on building from scratch, you don't just run libraries, you understand what's happening under the hood.

- It doesn’t overwhelm you with theory either; every concept is quickly tied to something you can actually build.

But if you’re newer to PyTorch or deep learning, you might want to brush up a little before jumping in. But overall, it’s a smooth and rewarding read, especially if you're hands-on.

📙 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧

In short, you will learn how to orchestrate and deploy modern GenAI models into robust real-world applications.

This book focuses on the real-world application side, how to actually build and deploy LLM-powered systems.

It walks through using LangChain and LangGraph to create structured pipelines, smart agents, and robust applications that can handle real-world complexity (not just simple question answering).

What stood out for me the most:

- It’s packed with practical patterns: memory handling, tool usage, agent frameworks, evaluations; things that show up all the time when you move beyond demos.

- The LangGraph part is a big highlight. 
It shows a clear, modular way to design more reliable flows, for who ever struggled with keeping agent systems organized as they scale.

- Very deployment-minded. 
Less “how to build a cool project” 
More “how to ship something that actually works reliably in production”

A bit of Python and LLM background helps, but overall, it’s a very practitioner-focused guide, not academic, not too theoretical.

Links to both books below:
✔️ Generative AI with Python and PyTorch: https://packt.link/rnoOJ
✔️ Generative AI with LangChain: https://packt.link/59uUr

For my first book review plz check here https://lnkd.in/gBkxSA5j

Let's grow together! Alex Wang

5 online courses by Harvard University, from introductory to Intermediate, free to access.

𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧
https://lnkd.in/gygaeAcY

𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞: 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
https://lnkd.in/gUNVYgGB

𝐇𝐢𝐠𝐡-𝐝𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐚𝐥 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬
https://lnkd.in/gv9RV9Zc

𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐟𝐨𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥𝐬
https://lnkd.in/g8gQ6N-H

𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐑
https://lnkd.in/gUY3jd8v

2 more on Coursera, you can choose 'Audit the course' on Coursera, which is free for all the learning videos and materials.

𝐑𝐈𝐂𝐄 - 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐄𝐱𝐜𝐞𝐥
https://lnkd.in/gBVHWzvR

𝐉𝐨𝐡𝐧𝐬 𝐇𝐨𝐩𝐤𝐢𝐧𝐬 - 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞Specialization: https://lnkd.in/g_n-3Wn5

Thanks for the nice visual, Kevin Rosamont Prombo
_____________________
I share my learning journey into AI and Data Science here, join me and let's grow together.

For more learning materials, please check my previous posts. Alex Wang

#machinelearning #datascience #dataanalysts

'The deeper understand you have, the simpler way you can explain.'

An inspiring material for us to explain the machine learning terms to non related background people, may be our clients, manager, or even grandparents : )

Also a very gentle introduction for beginners.

Credit: Vasily Zubarev

For more ML/ AI/ Data Science learning materials, please check my previous posts.

I share my learning journey into Data Science with my amazing LinkedIn friends, please follow me and let's grow together! Alex Wang

#deeplearning #datascience #machinelearning

I am a big fan of a quick start and small steps when I try to learn a new skill from absolutely zero, even if the final goal is very far away.

The shorter sub-goals return a more frequent sense of achievement, which helps me to build confidence and motivates me to go further in a new area. When I feel difficult to go through it, I will reconsider the length and material, rather than be self-blaming and deeply guilty. 

This 60 pages material can be a good start for beginners to start python. *Which can be downloaded directly from the post by making it full-screen, and then click the download icon on the top right corner.

Document credit to InterviewBit

I share my learning journey into Data Science with my amazing LinkedIn friends, please follow me and let's grow together! Alex Wang

#datascience #pyhton #machinelearning #deeplearning

10 Useful Ways to Visualize your Data (with examples)

It could be directly downloaded from the post by making it full screen, then clicking the download icon on the top right corner.

Credit: Sisense

For more free downloadable ML/ AI/ Data Science learning materials, please check my previous posts.

I share my learning journey into Data Science with my amazing LinkedIn friends, please follow me and let's grow together! Alex Wang

#dataanalysis #datavisualization  #datascience #technology #dataproducts

“You can tell compelling data stories with Power BI in Jupyter notebooks!”

You can embed Power BI reports, dashboards, dashboard tiles, report visuals, or Q&A in Jupyter notebooks easily.

Install the Power BI Client for Jupyter from PyPI https://lnkd.in/gFrERSm 
and find the open-sourced Python package and associate TypeScript widget on GitHub
https://lnkd.in/g3aRQq9.

The document of Microsoft Power Platform release plan download
https://lnkd.in/gFSgBeJ

Credit: Amit Shuster, Project Manager of Microsoft, and Microsoft

For more ML/ AI/ Data Science learning materials, please check my previous posts.

#datascience #python #powerbi #datavisualisation

Nice visual for data pipelines!

But it is just an integral part of a data platform.

Data platforms are comprehensive ecosystems that manage and leverage data to derive insights and drive decisions.

It may sometimes be seen as less exciting, but they are the very foundation of those AI applications and real-time insights. No fascinating insights come from a bunch of chaos.

If you are interested in learning more about advanced data platforms, check here https://bit.ly/3O1wj0t

It is the launch event for Singlestore's Pro Max, showcasing a live demo of the data platform's capabilities in generative AI, vector technology, and real-time analytics. Free to join for both on-site and virtual.

Have fun! :)

A nice decision tree to help choose the tools to deliver the specific data science/AI-related task.

The tools are focused on MS products, the image is produced by the Microsoft technology center, author Ivan Kosyakov.

For ML/ AI/ Data Science learning materials, please check my previous posts.

I share my learning journey into Data Science with my amazing LinkedIn friends, please follow me and let's grow together! Alex Wang

#datascience #artificialintelligence #machinelearning #bigdata

Create your own #ChatGPT in minutes

This step-by-step guide shows you how to use GPT-trainer for creating your very own chatbot, enabling you to streamline various aspects of your business such as customer support.
Just import your data, customize the chatbot to your preferences and embed it on your website - it’s that simple!

Sign up for free: https://gpt-trainer.com/

What other chatbot examples can you think of?

#genai #artificialintelligence #chatgpt #technology

A nice infographic visualization of ' How Startup Funding Works'.

Credit: Adioma

For ML/ AI/ Data Science learning materials, please check my previous posts.

I share my learning journey into Data Science with my amazing LinkedIn friends, please follow me and let's grow together! Alex Wang

#dataanalysts #datavisualisation #infographics

A fun image to show the importance of statistics in ML.

If you have studied Statistics, it will be a big win for you to get into ML; But if you have less background about it, those fundamentals of statistics may help you to have a quick start in ML study:

Full understanding of
- Hypothesis testing framework, e.g. p-values, conclusions, Type I and Type II error
- Confidence interval framework

Basic understanding of 
- Probability calculations, especially Bayes rule/ conditional probability and the Central Limit Theorem
- Distributions, including properties of random variables

Full understanding of the models you use:
- Assumptions of tests, and how bias could affect results
- Confounding variables and Simpson's Paradox

Please leave your thoughts on this list in the comments, it will be very helpful for me and many others!

For ML/ AI/ Data Science learning materials, please check my previous posts.

I share my learning journey into Data Science with my amazing LinkedIn friends, please follow me and let's grow together! Alex Wang

#datascience #machinelearning #dataanalysts

Top videos learn Statistics for Data Science, in 30s.

All learning materials can be freely watched/ downloaded, please find the links in the comments.

Hope this can help you to start your statistics learning for data science!

Any more should be added to the list? Please comment it 👇

For more ML/ AI/ Data Science learning materials, please check my previous posts.

I share my learning journey into Data Science with my amazing LinkedIn friends, please follow me and let's grow together! Alex Wang

#statistics #datascience #machinelearning #python #r

New platform to create immersive experiences for AR glasses:

Qualcomm introduced Snapdragon Spaces XR Developer Platform, for game developers that supports a cross-device ecosystem and empowers developers. Read more in the comments.

For ML/ AI/ Data Science learning materials, please check my previous posts.

I share my learning journey into Data Science with my amazing LinkedIn friends, please follow me and let's grow together in 2022! Alex Wang

#innovation #artificialintelligence #augmentedreality #metaverse

AI is not limited to LLMs. There’s a high chance most of us already use AI more than we realize.

Imagine a day like this, and see how many activities you knew you were interacting with AI:

1) Your morning starts with checking your spam folder for an email you've been waiting for - 𝐞𝐦𝐚𝐢𝐥 𝐬𝐩𝐚𝐦 𝐟𝐢𝐥𝐭𝐞𝐫.

2) During lunch break, you book tickets for your next vacation and chat with a customer support bot when you encounter a payment error - 𝐜𝐡𝐚𝐭𝐛𝐨𝐭.

3) At 6 PM, back home, you do some exercise with your smartwatch and put on a suggested playlist - 𝐟𝐢𝐭𝐧𝐞𝐬𝐬 𝐭𝐫𝐚𝐜𝐤𝐞𝐫/ 𝐩𝐥𝐚𝐲𝐥𝐢𝐬𝐭 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧.

4) Your neighbor comes by and rings your camera doorbell - 𝐬𝐦𝐚𝐫𝐭 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐜𝐚𝐦𝐞𝐫𝐚.

5) Open Uber Eats to order food delivery - 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐫𝐨𝐮𝐭𝐞𝐬 𝐚𝐧𝐝 𝐞𝐬𝐭𝐢𝐦𝐚𝐭𝐞 𝐚𝐫𝐫𝐢𝐯𝐚𝐥 𝐭𝐢𝐦𝐞𝐬.

6) Later, with your kids, you browse recommended shows on Netflix - 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬.

*Bonus: Live webinar on 'Build a Recommendation System', practical and free to join (starts in 12 hours)
https://bit.ly/4fP7tgu

7) Before sleep, you open Facebook and see you've been tagged in photos your friends shared – 𝐢𝐦𝐚𝐠𝐞 𝐫𝐞𝐜𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧.

Those are all applications of AI. How many did you recognize?

Not every single application seems exciting, right? Actually, about 90% of AI solutions for real-world projects are still based ML algorithms, not LLMs or other super-advanced algorithms.

Another reason might be that many things have been incorporated into AI over time, but most of them operate in the background.

This trend has been ongoing for over a decade and has literally been integrated into every aspect of our lives.

This includes decision-making processes, such as which political ads we see, what jobs we qualify for, whether we qualify for loans, etc.

I enjoy the benefits of technology, but I also like to keep myself mindful of its influence and how it may shape my decisions and experiences.

Image: Google Intro to AI
__________________

I share my learning journey here. Join me and let's grow together.

For more on AI and learning materials, please check my previous posts. Alex Wang

#artificialintelligence #machinelearning #datascience #technology

Top 6 Github Repos to Learn Data Science and Python, or copy some code, as needed :)

1. All algorithms implemented in Python
By: The Algorithms
https://lnkd.in/guaDFtA

2. Learn Python 3
By: Jerry Pussinen
https://lnkd.in/gRm8xYs

3. DataScienceResources
By: jb
https://lnkd.in/gfn6GxN

4. Awesome Data Science
By: Fatih Aktürk, Hüseyin Mert & Osman Ungur, Recep Erol.
https://lnkd.in/g9VRjip

5. data-scientist-roadmap
By: MrMimic
https://lnkd.in/gBRwKVw

6. Data Science Best Resources
By: Tirthajyoti Sarkar
https://lnkd.in/ghk3yBd

I share my learning journey into Data Science with my amazing LinkedIn friends, please follow me and let's grow together! Alex Wang

#innovation #artificialintelligence #datascience #machinelearning

Disney scores again with its innovative HoloTile!

After their impressive bipedal robot, they're making a great breakthrough in omnidirectional VR experiences.

HoloTile is the world's first multi-person, omnidirectional, modular, and expandable treadmill floor made up of hundreds of independent, motorized tiles.

It enables multiple users to share immersive VR experiences, move freely in any direction, and avoid collisions.

Use cases?

Gaming is definitely a significant aspect, and it's also perfect for sports training, interactive exhibits, and theme parks...etc.

Plus, its potential in healthcare is vast as well, including rehabilitation, therapy, and treatment of conditions like PTSD, anxiety disorders, and phobias.

Robot from Disney (My fav Judy!)
https://lnkd.in/gyU_zid8
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For more on AI and interesting news, please check my previous posts. Alex Wang

#innovation #machinelearning #technology #gaming

Meet 🤖 Travis, your zero-cost personal Python and Data Science tutor!

Trained by OpenAI, mastered Python by diving into 125k + learning resources that are available online.

Travis can simplify, explain, provide examples, and answer questions. Best of all, he's available 24/7 for you, and completely free.

Start upskilling here: https://aigents.co/learn

Pick a topic, and Travis will be right there to assist! Let me know how it goes. :)

I share my learning journey with my amazing LinkedIn friends, join me, and let's grow together!

#artificialintelligence #datascience #python

What Are Agentic Design Patterns?

We talked about AI Agents as 'doers' (autonomous actors that plan, take action, and adapt)

Agentic AI is the broader concept, where system behaving autonomously and goal-oriented.

Often using multiple agents working in coordination through agentic workflows.

To build these systems, developers use design patterns, which are reusable architectural templates.

The field is evolving fast. Besides the 6 common patterns (shown in the image), some other notable pattens worth checking too,

especially for more advanced or or domain-specific systems (e.g. enterprise AI, robotics, R&D environments).

◾ 𝗠𝗲𝗺𝗼𝗿𝘆-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗔𝗴𝗲𝗻𝘁𝘀
Adds short-term or long-term memory to agents.

**Enables personalization, context carryover, and better reasoning over time.

Used in: LangChain Memory, ChatGPT custom GPTs with memory

Good for: Agents that need continuity (e.g. CRM agents, tutoring bots)

◾𝗘𝘃𝗲𝗻𝘁-𝗗𝗿𝗶𝘃𝗲𝗻 𝗔𝗴𝗲𝗻𝘁𝘀
Agents triggered by system events, data updates, or user actions (not just prompts)

**Enables automation and real-time responsiveness.

Used in: Zapier AI, Slackbots, enterprise automation tools

Good for: Internal ops, alerts, or workflows like
“If new order → summarize → send to Notion”

◾𝗘𝗻𝘀𝗲𝗺𝗯𝗹𝗲 / 𝗩𝗼𝘁𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝘀
Multiple agents (or models) generate options, and a “judge” selects the best one.

**Improves quality, reduces hallucination, balances multiple perspectives.

Used in: OpenAI’s system message testing, research LLM evaluations, SuperAGI

Good for: Content generation, code generation, or safety-sensitive domains

◾𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻-𝗕𝗮𝘀𝗲𝗱 𝗔𝗴𝗲𝗻𝘁𝘀
Agents that operate and learn inside simulated environments.

**Safe space for testing strategies, training, and multi-agent collaboration.

Used in: Meta’s CICERO (Diplomacy), Voyager in Minecraft, MARL research

Good for: Robotics, gaming, R&D, or long-term planning

💡Patterns aren’t mutually exclusive, acutally the most advanced agentic systems combine several of them to get smarter, faster, and more reliable over time.

Personally, I’ve seen memory-augmented and multi-agent patterns the most, especially when trying to scale workflows or personalize outputs over time.

Curious, which ones have you used or seen most often in action? Any interesting examples out in the wild?

📍Image credit: Rakesh Gohel. He shares awesome content on agentic AI and AI agents, definitely worth checking out if you're exploring this space!🐣
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For more on AI and learning materials, plz check my previous posts.

I share my journey here. Join me and let's grow together. Alex Wang

#agenticai #aiagents #mcp #llms #technology

Didn't realize the immense depth at which the Titanic rests in the ocean.

Credit to @Rebaz Hussein

50X faster data analysis with NVIDIA's open-source accelerator!

Pandas accelerator from Nvidia's RAPIDS cuDF, a GPU-accelerated library for data manipulation and analysis, similar to the popular Pandas library in Python.

It's interesting to see how Nvidia made it easy to use - they introduced a way to deploy cuDF with just a module flag when running the script or through an extension load in a notebook.

This allows you to easily switch to using cuDF instead of Pandas, without having to change your code.

pip install cudf
conda install cudf -c nvidia

Make sure you have:
- Python 3.9 or later
- NVIDIA GPU with CUDA support
- CUDA installed (version 11.2 or later)
- cuDF is installed on the same environment as your Python installation

Have fun!😎
__________________

I share my learning journey here. Join me and let's grow together.

For more on AI & learning materials, please check my previous posts. Alex Wang

#python #datascience #dataanalysis #machinelearning

AI is not limited to LLMs. Most likely many of us already use AI daily more than we realize.

Imagine a day like this, and see how many activities you knew you were interacting with AI:

1) Start your morning by checking the spam folder for an email you’ve been waiting for - 𝐞𝐦𝐚𝐢𝐥 𝐬𝐩𝐚𝐦 𝐟𝐢𝐥𝐭𝐞𝐫.

2) During lunch break, you book tickets for your next vacation and chat with a customer support bot when you encounter a payment error - 𝐜𝐡𝐚𝐭𝐛𝐨𝐭.

3) At 6 PM, head home, exercise with your smartwatch, and put on a playlist it recommends - 𝐟𝐢𝐭𝐧𝐞𝐬𝐬 𝐭𝐫𝐚𝐜𝐤𝐞𝐫/ 𝐩𝐥𝐚𝐲𝐥𝐢𝐬𝐭 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧.

4) Open Uber Eats to order food delivery - 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐫𝐨𝐮𝐭𝐞𝐬 𝐚𝐧𝐝 𝐞𝐬𝐭𝐢𝐦𝐚𝐭𝐞 𝐚𝐫𝐫𝐢𝐯𝐚𝐥 𝐭𝐢𝐦𝐞𝐬.

5) Your neighbor comes by and rings your camera doorbell - 𝐬𝐦𝐚𝐫𝐭 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐜𝐚𝐦𝐞𝐫𝐚.

6) Later, with your kids, you browse recommended shows on Netflix - 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬.

7) Before sleep, you open Facebook and see you've been tagged in photos your friends shared – 𝐢𝐦𝐚𝐠𝐞 𝐫𝐞𝐜𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧.

Those are all applications of AI. How many did you recognize?

Also, an update from Google certificates, they just updated some of their foundational programs with real-world AI applications, if you’re eyeing on those courses, time to check:

✔️UX Design https://lnkd.in/gtdb_fWc
✔️Digital Marketing & E-commerce https://lnkd.in/gndBvPqy
✔️Cybersecurity https://lnkd.in/gZM6fUva
✔️Project Management https://lnkd.in/gg58JsP2

*I use Coursera, but you can also find those courses on other platforms.
__________________

For more on AI and learning materials, please check my previous posts.

I share my learning journey here. Join me and let's grow together. Alex Wang

🔉Breaking: The DeepSeek Conspiracy

The House of OpenAI, once the uncontested ruler of the realm, now finds itself in turmoil. A new challenger has risen from the East—DeepSeek, the rogue alchemist who, with cunning and coin, has forged a model that rivals the might of the great O1… and gave it to the people for free.

But the gods of Silicon Valley do not forgive so easily. Whispers in the keep of Microsoft’s Azure Towers speak of stolen magic—𝗢𝗽𝗲𝗻𝗔𝗜 𝗻𝗼𝘄 𝗮𝗰𝗰𝘂𝘀𝗲𝘀 𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸 𝗼𝗳 𝗱𝗶𝘀𝘁𝗶𝗹𝗹𝗶𝗻𝗴 𝘁𝗵𝗲𝗶𝗿 𝘃𝗲𝗿𝘆 𝗲𝘀𝘀𝗲𝗻𝗰𝗲, siphoning the knowledge from their sacred models like a shadowy apprentice learning forbidden spells.

The accusation? 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻, a dark art where one takes the wisdom of a mighty, well-trained AI and transfers it into a smaller, more efficient model. A clever trick, yet one that OpenAI deems high treason, for it was done not with scrolls and study, but through the plundering of API calls.

Microsoft’s ravens claim to have seen 𝘃𝗮𝘀𝘁 𝗱𝗮𝘁𝗮 𝘀𝗺𝘂𝗴𝗴𝗹𝗲𝗱 into the East, and the lords of the Valley demand justice.

But the story does not end there. Even as OpenAI sharpens its blades, House Alibaba has unleashed 𝗤𝘂𝗲𝗻 𝟮.𝟱 𝗠𝗮𝘅, another open-source warrior, standing alongside Kim 1.5, yet another rising power.

And in the shadows, the architects of DeepSeek have 𝗯𝘆𝗽𝗮𝘀𝘀𝗲𝗱 𝗖𝗨𝗗𝗔 𝗶𝘁𝘀𝗲𝗹𝗳, forsaking Nvidia’s dominion to carve their own path with sheer technical mastery.

If the rumors are true, this band of rogue engineers has achieved 10x the efficiency of their rivals—something that should not be possible by the laws of the land.

Yet, as this war rages, there is a deeper irony. The great houses have all played the game of shadows before.

Did OpenAI not once 𝘀𝗰𝗼𝘂𝗿 𝘁𝗵𝗲 𝗹𝗮𝗻𝗱𝘀 𝗼𝗳 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝗻𝗲𝘁, feasting on the texts of bards and scholars alike? Did they not once laugh in the face of copyright lords, only to now cry foul when another dares the same?

The drama unfolds, and the question remains: Is DeepSeek the Robin Hood of AI, stealing from the rich to gift intelligence to the world? Or are they mere opportunists, cloaked in the garb of revolutionaries?

One thing is certain—the war for AI dominance has only begun. And in this game, as in all others, you either innovate… or you become obsolete.
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*Just for fun. I asked AI to tweak the draft in a 'Game of Thrones' style - not bad at all. Quick guess: R1 or GPT? 😎

For more on AI and news, please check my previous posts.

I share my journey here. Join me and let's grow together. Alex Wang

I just made a full website using AI in a couple of minutes, and it was free! I feel like I've been spoiled by AI tools too much recently😅

10Web AI Website Builder is a tool that helps anyone get a jumpstart on website building and editing with the help of AI.

After answering a few questions about your business you can get a custom website, as well as AI-generated content and image for your website in minutes.

✅ Try it for free by entering your email.

📗 How to do it
- Go to 10web.io and get started
- Select the type of business you have
- Choose a layout design
- Type your website name and description
- Add the services that your website will offer
- Add case studies as a finishing touch

That's it! You now have a full website, from the homepage to the contact page to the gallery.

Try it yourself, and let me know how it goes! :)

Video credit to 10Web.io

Traditionally, data scientists use AI models for analysis, and are great data storytellers at a high level.

AI engineers, more specifically generative AI engineers, are like AI system builders. They use foundation models to build generative AI systems that transform business processes.

*EDA: exploratory data analysis

*Scope: refers to the scope of tasks that can be solved by an individual model

*The image highlights the major differences, but it's not absolute. Data scientists also work on prescriptive use cases, and AI engineers work with structured data.
_____________

I share my learning journey here. Join me and let's grow together.

For more on AI & learning materials, please check my previous posts. Alex Wang

#machinelearning #artificialintelligence #datascience #technology

This is a game-changer for customer support!
 
Create a custom ChatGPT for your data.

Chatbase.co lets you upload your data (files, text, websites, etc...) and create a custom ChatGPT that knows that data and can answer any question about it. You can also customize the chatbot's behaviour and UI and embed it on your website!

This will change the customer support industry!

For more resources in AI/ML/Data Science, please check out my previous posts.

Top AI courses for professionals

Besides the courses I tried and can recommend, I've also curated a list of 5 newly released courses that have great structure and a practical focus. (marked in the image)

Most course lengths are between 3-6 months for part-time study. They are designed for beginners to become job-ready. You can definitely finish earlier if you have some foundation or can spend more than 2 hours per day.

As a bonus, here's an awesome AI workshop coming up, free as well
0) https://bit.ly/3JsflWc

Courses link
1) https://bit.ly/3xGUMCJ

2) https://bit.ly/4d5MtR5

3) https://bit.ly/3QbvbZ9

4) https://bit.ly/3U9KsuR

5) https://bit.ly/3JMdL1P

6) https://bit.ly/3Q8Qjz2

7) https://bit.ly/3w4b8oD

8) https://bit.ly/3UsEJBH

9) https://bit.ly/446cQT6

I used Coursera, but you can find all those courses on other platforms as well by searching for the course name. Some courses may require payment to receive the final certificate, but all of them are free to learn.

ps, If you're new to Coursera, there's a 7-day free trial you can take advantage of!

✔️I share my learning journey here. Join me and let's grow together.

For more on AI and learning materials, please check my previous posts. Alex Wang

#artificialintelligence #technology #machinelearning #datascience #bussiness

An executive’s guide to AI, this is a 12 pages material from McKinsey Analytics.

It introduces the ML AI’s definition and the major types of machine learning in a clear and concise way with sample business use cases.

Specifically, what it is, when to use it, and how it works in algorithms of supervised learning, unsupervised learning, and reinforcement learning.

Also, the AI development timeline from 1805 to recent years.

For ML/ AI/ Data Science learning materials, please check my previous posts.

*All learning materials could be directly downloaded from the post by making it full screen, then clicking the download icon on the top right corner. *

I share my learning journey into Data Science with my amazing LinkedIn friends, please follow me and let's grow together! Alex (Mengyao) Wang

#machinelearning #dataanalysis #datascience #ai #algorithms

Data Analysis with PANDAS CheatSheet

Credit: Arianne Colton, Sean Chen

For learning materials, please check my previous posts.

I share my learning journey into Data Science with my amazing LinkedIn friends, please follow me and let's grow together! Mengyao Wang

#datascience #pandas #python #machinelearning

10 Useful Ways to Visualize your Data (with examples)

It could be directly downloaded from the post by making it full screen, then clicking the download icon on the top right corner.

Credit: Sisense

For more free downloadable ML/ AI/ Data Science learning materials, please check my previous posts.

I share my learning journey into Data Science with my amazing LinkedIn friends, please follow me and let's grow together! Alex (Mengyao) Wang

#dataanalysis #datavisualization  #datascience #technology #dataproducts

Cheat Sheet of NumPy Basics
 
Credit: Author and DataCamp

For more ML/ AI/ Data Science learning materials, please check my previous posts.

All the learning materials/reports could be directly downloaded from the post by making it full screen, then click the download icon on the top right corner.  

I share my learning journey into Data Science with my amazing LinkedIn friends, please follow me and let's grow together! Alex (Mengyao) Wang

#python #dataanalysis #datascience #numpy

Tech is paying big salaries for engineering and developer jobs specializing in AI. OpenAI offers around $1 million to their (L5) AI engineers.

If you're considering starting your AI learning journey, today’s newsletter is for you.

First and foremost, you don't need to know everything right away; you just need to learn enough to get started, find a job, and keep learning while working.

The framework I shared in the newsletter was originally created to help a friend transition into AI jobs without a related background.

The four courses are provided by Stanford University and DeepLearning.AI, and you can find the links in the article.
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I share my learning journey with all my LinkedIn friends, click follow and let's grow together!

For more learning materials on AI, Machine Learning, and Data Science, please check my previous posts. Alex Wang

#artificialintelligence #chatgpt #technology

A collection of the courses I've posted about over the last six months, with a focus on job-ready skills and practical applications.

I use Coursera as my primary platform, and they are currently offering a special time-limited discount for new users - 40% Off Coursera Plus Annual Subscription.

With this, you will gain full access to over 7,000 courses, including popular Professional Certificates from Google and Meta.

You can learn more about the offer here
✔️ Global https://bit.ly/4e8WyNG
✔️ India https://bit.ly/4e8WTzW

*This offer will end on June 23, 2024, so act quickly if you're interested.

Hope those curated courses and certifications can help you stand out in a competitive job market and open up new career opportunities!

#datascience #machinelearning #artificialintelligence #python