Where can I find the pdf version of this book for free?

Looking for genuinely valuable courses in:

• AI/ML
• Deep Learning
• Generative AI
• Agentic AI
• LLMs & RAG
• MLOps

I don’t want random “certificate” courses. I want courses that:

• Help build a strong GitHub/portfolio
• Are respected by recruiters/startups
• Include real-world projects
• Teach practical implementation properly

Please suggest the BEST courses you’ve personally found useful (paid or free).

I keep seeing mixed messages about breaking into AI/ML. Some say the field is wide open for self-taught people with good projects, others claim you need at least a Master's to even get interviews.

For those currently job hunting or working in the industry. Are companies actually filtering out candidates without advanced degrees?

What's the realistic path for someone with:

• Strong portfolio (deployed models, Kaggle, etc.)
• No formal ML education beyond MOOCs/bootcamps

1. Is the market saturation different for:
   • Traditional ML roles vs LLM/GenAI positions
   • Startups vs big tech vs non-tech companies

Genuinely curious what the hiring landscape looks like in 2025.

EDIT: Thank you so much you all for explaining everything and sharing your experience with me, It means a lot.

ISLP book, I finished the first 2 chapters, but this book is not easy, and I want some guys to study this book together. Any tips to study this book?

Over the last few months, I realized I was spending more time looking for resources than actually learning Machine Learning.

Every week it was another roadmap, another YouTube playlist, another "complete ML course," or another GitHub repo.

Eventually I had 100+ bookmarks, dozens of open tabs, and somehow still felt like I wasn't making much progress.

So I decided to stop collecting resources and build one list that I could keep coming back to.

Learning

• Fast.ai – Practical deep learning with a "learn by building" approach.
• Full Stack Deep Learning – Production ML, deployment, and modern AI systems.
• Made With ML – ML engineering, MLOps, and production best practices.

Models & Datasets

• Hugging Face – Models, datasets, Spaces, and transformers.
• Kaggle – Competitions, datasets, notebooks, and discussions.
• OpenML – Public datasets and ML benchmarks.
• UCI ML Repository – Classic datasets used in countless tutorials and papers.

Research

• Papers with Code – Research papers with open-source implementations.
• arXiv – The latest ML and AI research.
• Lil'Log (Lilian Weng) – One of the best blogs for understanding modern AI concepts.

Building Intuition

• TensorFlow Playground – Visualize how neural networks actually learn.
• 3Blue1Brown – Fantastic explanations of linear algebra, calculus, and neural networks.

Learn by Building

Instead of watching another course:

• Build on real datasets.
• Reproduce a paper.
• Read open-source ML code.
• Deploy your projects.
• Write about what you learn.

Those five things have probably taught me more than hours of tutorials.

I'm sure this list is missing a lot.

I'm not looking for the usual recommendations like Andrew Ng, Coursera, or CS229.

I'm looking for the resources that made you think:

"I wish I'd discovered this six months earlier."

Could be a:

• GitHub repository
• Blog
• Newsletter
• YouTube channel
• Book
• Interactive website
• Dataset
• Discord community
• Anything else

I'd love to turn this thread into something beginners can bookmark and keep coming back to.

I know i wasted 2-3 years already, but as the saying goes its never too late. I am a beginner and i want to learn Ai, work on projects and use multiple tools. Right now i use LLMs like Gemini, Gpt, Claude for brainstorming, content creation, and research but that's just the surface level utilization of tools as per me. So yes please suggest one that has tools and projects which will be useful.

Apart from this if there also any communities or blogs or people i should follow do suggest, thanks. I know i can find this researching too but you guys know so much already so i felt its best if i hear from you

Wondering for a good resources to prepare for the interview, I know python and DSA, but unsure of the ML part... If anyone got In please suggest. I have 23 days to prepare.

Long story short, I had a MLbeginner project in which I had to train a dataset consisitinf of 440k rows and 15 columns on xg boost,,, I did this and made a pipeline of hyperparameter tuning in which I am doing out of fold target encoding on two columns with k fold cross validation with k=5 and then I am doing randomised search Cv(attaching the parameters I am using) AND ITS TAKING HOURS TO RUN and it has not run yet .. I am not sure what to do really. I have a laptop which has an i7 13650 hx and rtx 4060 for gpu but the kernel isn't utilising gpu at all and I have the deadline as today, if someone can help pls do help

And yes I am using device=cuda and tree method= hist

How can I fasten this up, is my code or something wrong and how do I actually use my rtx 4060 gpu so it runs??

I am running it in my vs code and the kernel it shows is gpu_env(Python 3.10.20)

So I've decided I wanna get into ML seriously, but holy shit there's so much stuff to learn.

I started watching courses and then suddenly everyone's talking about linear algebra, calculus, statistics, cost functions, gradient descent, and I'm just sitting there like... when did this become a math degree? 😭

I'm looking for someone who's either:

• also starting out and wants to learn together,
• already learning ML,
• or has been through this and doesn't mind giving some advice here and there.

I'm not looking for someone to teach me everything for free or anything. Just thought it'd be cool to have someone to discuss stuff with, stay accountable, maybe work on projects together, or even just share resources.

If you've got any tips for someone starting from basically zero, I'd love to hear them too.

Thanks :)

Was thinking to take this course for statistics and probability is it enough to get started with ML.

Also is it beginner friendly? I have knowledge till high-school level.

Hey !

I'm starting my ML journey now (July 2026) with the goal of building AI chatbots and training LLMs. I've seen tons of roadmaps from experts, but I want to hear from people who were in my shoes RECENTLY.

If you started learning ML in the past 2-3 years and either:

- Got your first ML job

- Landed an internship

- Built something real that got noticed

- Or even just made significant progress

I'd love to know:

1. WHAT RESOURCES ACTUALLY WORKED?- Which courses did you finish vs. abandon?- What was worth the time vs. waste of time?
2. YOUR REAL TIMELINE- How many months from "zero" to "job-ready"?- How many hours per week did you actually study?
3. THE HARD TRUTHS- What did you think would matter but didn't?- What caught you by surprise?- What would you do differently if you started today?
4. YOUR FIRST PROJECT- What was the first thing you built that made you feel "I got this"?- Did you put it on GitHub? Did anyone care?
5. THE MATH QUESTION- Did you do full math courses or just "enough to understand"?- How much math do you actually use day-to-day?
6. REMOTE JOB HUNTERS - THIS ONE'S FOR YOU!- Did you get a remote ML job? How?- Is it realistic for a self-taught ML engineer to land remote work?- What made you stand out against local candidates?- Did you need to prove yourself with freelance/contract work first?- Any platforms that actually worked for remote ML gigs? (Upwork, Toptal, etc.)- Time zone issues - how did you handle that?- Was the pay fair compared to on-site roles?

I'm not looking for perfection - I want real stories from real people who figured it out. The good, the bad, and the "I wish someone told me this earlier."

Thanks for any honest answers! 🙏

I personally prefer documentation over videos but wanted to know which would be the best source.

Hey everyone,

I've recently started learning machine learning, and instead of building the usual tutorial projects (house price prediction, sentiment analysis, etc.), I want to work on projects that would actually make my resume stand out.

If you were hiring a junior ML engineer, what kind of projects would catch your attention?

I'm looking for ideas that solve real problems and teach skills that companies actually care about

Would love to hear your suggestions or even projects that helped you land a job. Thanks!

Everyone tells to take this course but in coursera this course is totally locked so I took it from official website (without assignment) is this orignal or are the video content locked.

I'm in my final year of CS and I'm about to spend the next 6 months learning ML as seriously as I can.

Instead of asking "Which course is best?", I wanted to ask something different.

If you had to start from absolute scratch today...

What would you do differently?

What would you skip?

What would you spend MORE time on?

Looking back, what's the biggest mistake beginners make?

I'd love to learn from people who've already been through it.

🚀 AI/ML Study Group (4–5 People)

Hey everyone!

I’m creating a small group of 4–5 people who are serious about learning AI, Machine Learning (ML), and Deep Learning (DL) together.

💡 Goal:

- Build a strong understanding of AI/ML concepts

- Learn in depth (not just theory, but real understanding)

- Stay consistent and disciplined

📚 Learning Source:

- Currently learning from CampusX

🤝 Looking for:

- People genuinely interested in AI/ML

- Ready to learn daily and stay consistent

- Open to discussions, doubts, and teamwork

- Supportive mindset — we motivate each other when someone feels low

👥 Plan:

- Small, focused group (max 4–5 members)

- Regular discussions + doubt solving

- Practice + small projects together

- Help each other stay motivated 💪

📩 DM me if you’re interested!

https://discord.gg/V7PEtVJAz join this

Let’s grow together in AI 🚀

Hi everyone,

I'm trying to train a DL model for a binary classification problem. There are 1300 records (I know very less, however it is for my own learning or you can consider it as a case study) and 48 attributes/features. I am trying to understand the training and validation loss in the attached image. Is this correct? I have got the 87% AUC, 83% accuracy, the train-test split is 8:2.

Can anyone recommend a free, structured course roadmap to become an AI/ML Engineer and eventually an AI Researcher?

I'm looking for courses and resources that cover the following in order:

1. Python programming

2. Mathematics for AI/ML (Linear Algebra, Calculus, Probability, Statistics)

3. Machine Learning fundamentals

4. Deep Learning (PyTorch/TensorFlow, CNNs, RNNs, Transformers, LLMs)

5. Building real-world projects and research skills

6. MLOps and Deployment (FastAPI, Docker, Cloud, model serving)

I would highly appreciate recommendations for free courses, playlists, books, and project roadmaps from beginner to advanced level. A structured path with timelines and milestones would be very helpful.

My long-term goal is to become an AI Engineer and contribute to AI research.

25 yo from India. I don't have a lot of requirements other than you being a beginner like me and preferably a university student looking for jobs in this field. Lets crack this domain together!

EDIT: Hey guys, I am planning to create a discord group for all of us, dm me your id and I will add you.

EDIT 2: Thanks for reaching out guys. I have created a group for all of us. Please do join if you are really serious about getting into ML and would be consistent.

The link: https://discord.gg/STTbbGrK

Hey everyone,

I’m planning to spend the next 2–3 months fully focused on Machine Learning. I already know Python, NumPy, Pandas, Matplotlib, Plotly, and the math side (linear algebra, probability, calculus basics), so I’m not starting from zero. The only part I really want to dive into now is Machine Learning itself.

What I’m looking for are resources that go deep and clear all concepts properly — not just a surface-level intro. Something that makes sure I don’t miss anything important, from supervised/unsupervised learning to neural networks, optimization, and practical applications.

Could you suggest:

Courses / books / YouTube playlists that explain concepts thoroughly.

Practice resources / project ideas to actually apply what I learn.

Any structured study plan or roadmap you personally found effective.

Basically, if you had to master ML in 2–3 months with full dedication, what resources would you rely on?

Thanks a lot 🙏

How is Stanford yt online course for leaning ML?

Hi everyone,

Given how rough the tech job market is right now, I want to be very strategic about upskilling instead of collecting random certificates.

I have a background in data analytics + machine learning, and I’m targeting AI / ML Engineer, Applied Scientist, or Data Scientist roles in the US. I already have solid fundamentals in:

• Python, SQL
• ML models (regression, tree models, boosting, clustering, NLP basics)
• Data pipelines, dashboards, and analytics
• Some production exposure (model training + evaluation + deployment concepts)

My question is:
Which AI/ML certifications actually improve hiring outcomes in 2025–2026?

Not looking for:

• Basic Coursera beginner certificates
• Generic “AI for everyone” type courses

Looking for:

• Certifications that recruiters and hiring managers genuinely value
• Programs that signal real-world ML engineering skills
• Credentials that actually move resumes forward

Would love insights from:

• Hiring managers
• Recruiters
• People who recently landed AI/ML roles
• Engineers working in production ML

Also:
Do certifications even matter anymore, or are strong projects + GitHub + experience still king?

Thanks in advance!!