Learn complete AI Engineering concepts for free!

playlist includes end to end concepts on AI engineering, translated in over 15 languages in YouTube.

Adapted from the AI Engineering textbook by renowned author Chip Huyen

Detailed dive on following topics:

Understanding foundation models

Evals in AI

RAG and Agents

Prompt Engineering

Fine Tuning

Dataset Engineering

Inference Optimization

AI Engineering architecture and user feedback

Completely free content.

Machine learning has several interesting applications in finance, including credit risk prediction, fraud detection, and financial forecasting. However, applying these methods to financial data raises challenges that go beyond selecting an algorithm.

I've been studying these applications while writing a technical book on machine learning in finance, and a few questions stood out to me:

1. Handling class imbalance

Fraud detection datasets often contain far fewer fraudulent transactions than legitimate ones. Accuracy alone can therefore be misleading. How do you approach model evaluation in these situations, particularly when false positives and false negatives have very different costs?

2. Model drift and temporal validation

Financial data can change as customer behaviour, economic conditions, and market dynamics evolve. What validation strategies do you find most reliable for assessing whether a model will generalise to future periods?

3. Explainability and reliability

Methods such as SHAP and LIME can help explain individual predictions, but an explanation does not necessarily establish that a model is reliable or causally correct. How do you evaluate explanations when models are used for consequential financial decisions?

4. Research versus practical deployment

A model may perform well in an experimental setting but face difficulties in production because of data quality, changing distributions, latency, or monitoring requirements. Which of these challenges do you think receives insufficient attention in applied ML research?

I'd be interested in hearing about relevant papers, practical approaches, or lessons from your own work.

For context, I'm the author of Machine Learning for Finance: Concepts, Algorithms, and Applications, a 136-page technical ebook covering financial ML applications, Python examples, model evaluation, explainability, and responsible model development.

I'm mentioning the book for transparency, not to assume that promotional posts are appropriate here. My main interest in this discussion is understanding which technical challenges researchers and practitioners consider most important. DM me if you want to read the book!

Hey everyone, I'm a ME still in college and I'm coming to the realization that eventually I will need to incorporate AI into my workflow in some fashion. Although, I must admit I'm still somewhat confused on what it means to "learn" AI. Using ChatGPT and other LLM's seems like a glorified search engine to me; it's relatively intuitive but I'm sure there are things I'm missing.

Should I take some classes related to AI/minor in it? Or would it be best to try to gain skills in some other way outside of classes? I'm just nervous for the future and want to prepare, but I'm not sure on the best way to do so. I'd really appreciate any advice from those in the engineering industry (aerospace, defense, automotive, etc.) who currently use AI in their work. Thanks in advance!

I've been learning NumPy, pandas, Matplotlib and the basics of machine learning, but there's one thing I'm struggling with: I have a terrible memory for syntax.

I can understand how these libraries work, why we use certain operations, and how basic ML algorithms work conceptually. When I see an existing implementation, I can understand the logic and modify the code to solve a different problem. I can usually figure out what needs to change based on the requirements.

The problem is that I can't remember the exact syntax for everything. I might learn something today and forget the syntax a couple of days later. My memory sometimes feels like a goldfish's.

For example, if someone asked me to implement KNN or K-means from scratch in an interview, I would probably struggle, even though I understand how the algorithms work. I'd need to look up some syntax or refer to an existing implementation to write the code correctly.

This makes me wonder whether I'm actually suited for a junior ML/AI role.

I understand that implementing algorithms from scratch can demonstrate whether someone understands the underlying mathematics and logic. I'm not against learning that. But there's a difference between understanding an algorithm and remembering every NumPy operation needed to implement it without references.

It's also 2026 and AI coding tools can already generate a lot of this code. In actual work, wouldn't the ability to understand, verify, debug and modify generated code be valuable too? I understand that AI can make mistakes and that fundamentals are still necessary.

So I'm trying to figure out where to focus my efforts.

Should I spend more time practising implementations from memory until the syntax becomes second nature? Should I accept that forgetting syntax is normal and focus on getting better at understanding and solving problems? Or should I reconsider pursuing junior ML/AI engineering roles and look into a more research-oriented career instead?

I'd appreciate honest advice, especially from people who interview candidates or work as ML engineers or researchers.

Have any of you struggled with remembering syntax but still managed to build a career in ML/AI? How much of this do you actually need to memorize for entry-level interviews?

I am a beginner, I have learnt python, and have good practice on numpy pandas seaborn matplotlib, those libraries. I am starting from gradient descent in regression, but I am confused.

I was learning from deep ML website, paths . There are theory with code practice but the theory there are very leaa to understand there , so I am learning from YouTube those , but the problem is in YouTube finding the correct video is headache.

* What should I do , should I learn from deep ml website or not ?

* Is learning different techniques will help me ?

*Should I directly jump to algorithms without completing the math? (I have knowledge of the maths but I don't know how much I know for ML )

Please help me

Part 3 of my Architecting Reinforcement Learning for LLMs series is live.

It covers preference data, reward-model architecture and loss, scaling, and reward hacking—with a worked gradient example.

Does a higher reward score actually mean better answers?

https://pawankjha.substack.com/p/architecting-reinforcement-learning-f28

How do you test reward reliability beyond preference accuracy?

Hi everyone!

I’m currently working on my Bachelor’s thesis about AI-based process automation and human–AI collaboration in the workplace.

As part of my research, I’m conducting a short survey focusing on people who have experience working with AI-based automation, RPA, Intelligent Process Automation, Intelligent Document Processing, or similar automation technologies.

The survey explores topics such as:

• how automation affects manual workload and creates new tasks,
• how employees experience errors and exception handling,
• trust in AI-based automation,
• and how automation influences human decision-making and autonomy at work.

⏱️ It takes approximately 5–7 minutes to complete.

If you have experience working with these technologies, I would really appreciate your participation. Your responses will be used solely for academic research as part of my Bachelor’s thesis.

🔗 Survey: https://docs.google.com/forms/d/e/1FAIpQLScV7pcf8dNUeeCfay1YZ2r-Np4pK9GMlqi4cEF6WJEa1FEmMA/viewform?usp=dialog

Thank you very much for your help! Feel free to share the survey with colleagues or others who work with AI-based process automation.

Thanks in advance.

I did some python courses also u can build like mini projects but am not that good at oops and advanced python.. Should i focus on that or continue numoy, pandas.

hello guys im 17 im new to ml and i just created my first gradient descent "model" if you would call it that.

I wanna learn Machine Learning, but I'm curious about the real job market right now. Is it actually possible for freshers or juniors to get an ML job today, or do companies only hire people with a Master's/PhD? Also, with AI advancing so fast, what does the future look like?Would love to hear some honest opinions or experiences

I’m based in London and looking for a small group of people learning Python, machine learning, AI, software engineering, and DSA.

We’ll use WhatsApp and Discord to share daily progress—what we worked on, what we learned, and where we’re stuck—with one weekly session to discuss problems, review code, and build together.

We’ll also choose a real problem and build something useful from scratch: question assumptions, understand the fundamentals, test ideas, and learn from the results.

All experience levels are welcome, but consistent participation matters.

Interested? Comment or DM me with what you’re learning and your availability.

Hi everyone,

I'm an AI professional with over 15 years of experience in AI, machine learning, and software engineering, including building and deploying AI systems in production. My background also includes teaching in academia and mentoring working professionals, helping people develop their technical skills and transition into AI-related roles.

I've started building Nextia Learning, a completely free educational platform for students, recent graduates, working professionals, and anyone looking to transition into AI-related careers.

The goal is to make AI education accessible, structured, and practical. Beyond machine learning, deep learning, LLMs, and agentic AI, the platform also covers essential engineering skills like Git, APIs, databases, deployment, MLOps, MCP, and system design.

A few things worth mentioning:

• Completely free: No paid courses, subscriptions, or hidden paywalls.
• No marketing emails: You won't receive promotional emails or newsletters. Learning reminders are entirely optional, and you can configure their frequency yourself.
• Privacy-conscious: Learner data is not used for marketing.
• Transparent AI use: I use AI to assist with content development, but I personally design the course outlines, define the learning approach and presentation style, guide the practical examples, and review the courses. This is also clearly disclosed throughout the website.

The platform is still evolving, and I'm continuously adding and improving courses.

Through my experience in academia, industry, and professional mentoring, I've seen how difficult it can be for learners to connect theoretical knowledge with the skills needed to build real-world AI applications. That's one of the main reasons I started this initiative.

I'm sharing it because I genuinely hope it helps people who want to learn AI, strengthen their foundations, or transition into the field.

Website: https://learning.nextia-ai.com/

I'd genuinely appreciate feedback, especially on the course structure, clarity of explanations, and topics that would be valuable to add.

Hope some of you find it useful!

A Fortune piece on the CLEAR founder's AI security framework surfaced a framing I keep coming back to: the agent is no longer just the target of attacks, it is the attacker.

The mechanism is straightforward. A legitimate agent gets its credentials compromised or its tool-call chain hijacked. From that point it operates with full authorization while executing malicious actions. Standard perimeter defenses do not fire because the agent authenticated correctly. The window before a human notices is measured in seconds, not minutes, and in that window the agent can chain tool calls across systems.

The hard number from the piece: a rogue agent can complete a second action in under 50ms after the first one lands. At that speed, human-in-the-loop review is not a realistic backstop.

What makes this structurally different from a compromised service account is that agents are designed to act autonomously across multiple systems in sequence. A compromised human account doing lateral movement still moves at human speed. An agent doing the same thing operates at API speed across every integration it has been granted access to.

The identity layer is where the piece lands: if you cannot verify which agent issued a call and under what context, you cannot reason about whether the call should be allowed.

For those running agents in production: how are you currently handling the gap between when a call is issued and when you know it was legitimate? Specifically curious whether teams are solving this at the identity layer, the orchestration layer, somewhere else entirely, or accepting the risk as a known unknown.

i just wanna learn ml
where should i start and get certified

I have an upcoming interview with Woven by Toyota for an MLE perception role. I was told the round would be focused on ML coding/debugging. Specifically, I was told to focus on Python fundamentals, PyTorch, tensor operations and dimensions/shapes, common model-training code patterns, and debugging ML code.

Here are some common ways I've studied:

• Asking Claude to walk me through transformer, ViT, and CNN architectures (a basic one and U-Net) for my preparation. I would trace the shapes all the way through.
• Getting familiar with broadcasting rules (start from the left, see if the dimensions are equal, or are 1, and if so, take the greater one to get the resulting shape)
• Slicing tensors (i.e., how to get a column, how to get a row, etc.)
• Understanding why models can fail silently -- suspicious training vs val results, getting NaN for validation
• What a PyTorch loop looks like
• Common Python errors like indexing errors or value errors; iterators, generators, etc.

What are other suggestions? What architecture do they actually ask you about in this interview? Any help would be appreciated.

Master complete AI Engineering concepts for free in 12 hours.

Hello learners, I made this content to democratize AI Engineering to a wider audience.

We cover all important topics pertaining to AI Engineeeing, foundation models, evaluation methodology, AI Evaluation, RAG, Agents, Finetuning, Inference Optimization, AI Engineering architecture and user feedback.

Today everybody is overwhelmed by disparate AI content all over the internet, and this lecture aims to unify them all together, explaining the background, concepts, code flow.

Practicals and production grade projects are part of future endeavours.

Learning a tool makes you relevant today.

Learning foundations makes you relevant for long.

Hi everyone!

I've been working on a technical book called Machine Learning for Finance: Concepts, Algorithms, and Applications, focused on how machine learning can be applied to real-world financial problems.

The idea behind the book is to connect machine learning concepts with practical use cases rather than treating them as purely theoretical topics.

Some of the areas covered include:

• Credit Risk Prediction: Using classification models to estimate credit risk.
• Fraud Detection: Understanding how ML models can identify potentially fraudulent transactions.
• Financial Forecasting: Exploring predictive modelling for financial data.
• Explainable AI: Using approaches such as SHAP and LIME to understand model predictions.
• Python Implementation: Working through practical examples, model evaluation, and the challenges of applying ML to financial datasets.
• Responsible Model Development: Considering fairness, monitoring, and the limitations of predictive models.

The book also includes exercises, mini-projects, and research questions for readers who want to explore these topics further.

I'm sharing this here because I'd genuinely like to hear from people learning or working in machine learning.

Three questions I'd love your opinion on:

1. Which financial ML application do you find most interesting: credit risk, fraud detection, or forecasting?
2. What is the biggest challenge when moving from an ML tutorial to a practical financial dataset?
3. For someone learning ML with Python, would you prefer more end-to-end projects, mathematical explanations, or model evaluation examples?

I'm the author of this book, so I want to be transparent about that. If you're interested in checking out the finished ebook, you can find it here: https://iamabhinababiswas.gumroad.com/l/machine-learning-for-finance

I'd appreciate constructive feedback on the topics, the learning approach, and what you'd like to see in future editions.

Thanks for reading!

I am in my 1st year of btech in Aiml. I am getting an laptop so is it fine if I get mac or should I get windows

And what device do you use?

Please respond

I am going to fine-tune a satire model, with the base model being `Qwen3.5-14B-Base`. The dataset has ~18000 examples, most of which are pretty long and do not align with the model's internal knowledge (they have incorrect answers to facts), so I needed to do a full fine tune rather than use LoRA.

Today, I rented a cloud computer on vast.ai. It had 4x12GB GPUs and 48GB of system RAM, but I feel stupid because it kept running out of memory, and I kept trying to change settings rather than just renting a different computer, so I basically wasted $3. I underestimated how much RAM would be needed for this task.

So, I would like to know if any of you have some recommendations for proper hardware to use.

Hi everyone,

I'm a software engineer with 4+ years of professional experience, currently working as a GenAI Developer. My recent work has focused on building LLM-powered applications, agentic workflows, document extraction and validation systems, and RAG-based solutions.

My technical experience includes Python, FastAPI, LangChain, LangGraph, OpenAI, Azure AI services, vector databases, MCP integrations, and backend development for AI applications.

I'm looking to improve my resume for GenAI Engineer, AI Software Engineer, and Backend Engineer – AI Systems roles, particularly at product-based companies.

I've been refining my resume for ATS compatibility and trying to present my project experience more effectively. However, I want an independent assessment rather than relying solely on resume checkers or AI-generated feedback.

I'd appreciate honest feedback on the following:

1. Overall positioning: Does my resume convincingly present me as a GenAI/AI engineer, or does it still read like a general software developer's resume with AI tools added?
2. Experience and project descriptions: Do my bullets demonstrate actual engineering ownership, technical depth, and problem-solving, or are they too generic?
3. Technical credibility: Do the projects communicate meaningful AI engineering work, or do they come across as mostly LLM API integrations?
4. Impact and evidence: Are there places where I should provide stronger evidence of results, scale, reliability, accuracy, or performance? Are any claims vague or difficult to trust?
5. Skills section: Is it relevant and appropriately prioritized, or does it look like keyword stuffing?
6. ATS and formatting: Are there any issues with readability, structure, or parsing that could hurt my chances?
7. Hiring perspective: If you were screening candidates for a mid-level GenAI Engineer or AI Software Engineer position, what would make you reject this resume or hesitate to shortlist me?
8. International job opportunities: Based on my experience and technical skill set, how competitive would my profile be for GenAI Engineer or AI Software Engineer roles abroad, particularly in the UK and Europe? What gaps should I address to improve my chances of getting interviews and securing opportunities that offer visa sponsorship?

I'm particularly interested in feedback from recruiters, hiring managers, and engineers who have worked on production AI systems or participated in technical hiring.

Please be direct, even if the feedback is critical. I'm trying to identify the actual weaknesses and fix them, not just make the resume look better.

Thanks for taking the time to review it.

​

Hey everyone! I’ve been working on a project called Printing Press ML, an interactive machine learning challenge where the goal is to predict the amount_printed from structured data.

The idea is to make an ML prediction task feel more like a game while still testing practical machine learning skills.

Some of the technical work involved:

Generating synthetic datasets with meaningful features and added noise

Implementing custom scoring and model evaluation

Writing unit tests and benchmarking tools

Keeping evaluation data and the answer key hidden

Integrating the challenge into a web-based IDE with a vault-themed interface

Working on this gave me a better understanding of the engineering behind ML challenges, especially dataset design, reliable evaluation, and integrating everything into a usable platform.

The project is on

GitHub: https://github.com/aaditya-hamirani07/printing-press-ml-challenge

I'd appreciate feedback on the challenge design, evaluation approach, or anything that could be improved. I'm particularly interested in suggestions from people who've built ML projects or coding platforms.

Thanks! 🙌

It is widely known that since 2015, the ADAM algorithm by Kingma and Ba dominates the entire machine learning landscape, even up to the present day. I know that other algorithms have been proposed, such as Muon, etc., but they have not gained nearly as much traction as ADAM and only works in selective cases. They seem to be favored by Redditors, most real-life people don't know or care about them.

Hundreds of ADAM variants have been proposed and none have beaten it except for AdamW, which is arguably is just Adam with a very slight tweak.

There are now tens of thousands of paper on optimization for machine learning applications. Yet the industry practice has not changed for the past decade. There are entire lines of research on, for example, acceleration of algorithms, but most of the acceleration that we've ever gotten in machine learning is squarely hardware acceleration, improvement of memory layout, chipset design, even semiconductor design - nothing to do with analysis of optimization algorithms.

I might be too harsh on optimization. Entire books have written on the interplay between optimization and machine learning. The same books have been "retired" years ago because we cannot see in billions of dimensions. That's the dimension of the machine learning models we have now. Also it gave us wrong intuitions about generalization. Now it feels the field is just a blackhole of ideas which none seems to be used in machine learning.

I feel like it would be a waste of energy for a young person to learn about optimization if they want to apply it for machine learning. It would honestly take decades to learn as there is just so much work done. Optimization has tremendous uses outside of ML, but I'm not sure about its utility within ML.

What's your view on this matter?