AI/ML Curriculum: Media Track #

Optional visual learning path to complement the AI/ML Books Curriculum. Watch alongside any module or during rest weeks. Curated for quality -- everything here is genuinely worth your time.

For: Anjan Jagirdar Pairs with: AI_ML_BOOKS_CURRICULUM.md (Modules 0-7 + Narrative & Ethics parallel track) Last Updated: April 29, 2026

Mood Tags #

1. DOCUMENTARIES: AI & Machine Learning #

Watch Order for AI Docs #

Start here: AlphaGo (emotional hook) then The Age of A.I. (broad overview) Go deeper: The Thinking Game then NOVA: A.I. Revolution Ethics turn: Coded Bias then The Social Dilemma then The AI Doc

2. DOCUMENTARIES: Mathematics & Computer Science #

Dramas About Math & CS (Not Documentaries) #

3. DOCUMENTARIES: Surveillance, Privacy & Ethics #

Dramas About Surveillance (Not Documentaries) #

4. TV SERIES: Fictional but Relevant #

Black Mirror: Essential AI Episodes #

Watch these specific episodes if you can't do the full series:

5. MOVIES: Sci-Fi Exploring AI Themes #

Tier 1: Must-Watch (Canonical AI Films) #

Tier 2: Highly Recommended #

Tier 3: Worth Watching #

6. YOUTUBE CHANNELS & VIDEO ESSAYS #

Documentary-Style Educational Channels #

Technical AI/ML Learning Channels #

AI News & Analysis Channels #

Must-Watch Individual Videos #

7. TALKS & KEYNOTES #

TED Talks (All Free on ted.com / YouTube) #

Legendary Tech Talks (YouTube) #

Module Pairing Guide #

Streaming Availability Notes (India) #

Availability changes frequently. As of April 2026:

• Netflix India -- Black Mirror, The Social Dilemma, Coded Bias, The Great Hack, Her
• Prime Video India -- Most movies available for rent/purchase; Westworld, The Age of A.I.
• YouTube (free globally) -- AlphaGo, The Thinking Game, The Age of A.I., Revolution OS, all TED talks, all channel content, NOVA: A.I. Revolution, Terms and Conditions May Apply
• Disney+ Hotstar India -- WALL-E, The Creator, Silicon Valley
• Apple TV -- Available in India for most titles (rent/purchase)
• Kanopy -- Free with library card (check local availability)
• Tip: Use JustWatch.com/in to check current availability for any title in India

How to Use This Guide #

1. Don't binge. Watch 1-2 things per week alongside your reading.
2. Match to your module. The pairing guide above maps each viewing to where you are in the curriculum.
3. Start with the hooks. AlphaGo and "But What Is a Neural Network?" are the best on-ramps.
4. Ethics is not optional. The Module 7 viewing list is arguably the most important track here.
5. Take notes. Capture key concepts or questions in your learning journal (3_LEARNING_JOURNAL/).

The Complete AI/ML Community & Learning Ecosystem Guide #

Everything you need to stay connected, keep learning, and never miss what matters in AI/ML. Every URL verified as of April 2026.

For: Anjan Jagirdar Last Updated: April 28, 2026

1. NEWSLETTERS & SUBSTACKS #

Tier 1: Must-Subscribe (Research & Analysis) #

Tier 2: Must-Subscribe (Industry & News) #

Tier 3: Worth Following (Opinions & Analysis) #

Also Worth a Look #

2. PODCASTS #

Tier 1: Essential Listening #

Tier 2: Excellent Shows #

Tier 3: Worth Subscribing #

3. YOUTUBE CHANNELS #

Tier 1: Must-Subscribe #

Tier 2: Excellent Channels #

Tier 3: Also Good #

4. TWITTER/X ACCOUNTS TO FOLLOW #

AI Researchers & Lab Leaders #

AI Practitioners & Educators #

AI Commentators & Builders #

AI Policy & Safety #

India AI Community #

Pro tip: Create a private X list called "AI Signal" with these accounts so you get a noise-free AI feed separate from your main timeline.

5. REDDIT COMMUNITIES #

6. DISCORD SERVERS #

7. CONFERENCES #

Tier 1: Premier AI/ML Conferences #

Tier 2: Top Domain-Specific Conferences #

How to Follow Without Attending #

• Papers: All major conferences publish proceedings for free (NeurIPS, ICML, ICLR on OpenReview; ACL on ACL Anthology; CVPR on CVF Open Access)
• Talks: SlidesLive records many conference talks. Check YouTube channels of each conference
• Live-tweeting: Follow #NeurIPS2026, #ICML2026, etc. on X during conference week
• Paper digests: Many newsletters (AlphaSignal, The Batch) cover top conference papers
• Workshops: Often have more accessible content than main conference

8. MEETUPS AND COMMUNITIES #

Global Communities #

India-Specific Communities #

Mumbai-Specific #

• Search Meetup.com for: "Mumbai Machine Learning", "Mumbai AI", "Mumbai Data Science"
• Google Developer Group (GDG) Mumbai -- hosts ML/AI workshops
• TensorFlow User Group Mumbai -- if active, check meetup.com
• PyData Mumbai -- Python + data science meetups

9. SOCIAL MEDIA (INSTAGRAM / TIKTOK / LINKEDIN) #

LinkedIn AI Influencers #

Instagram AI Accounts #

TikTok AI Accounts #

Note: Instagram and TikTok AI content skews toward tools/news rather than technical depth. Use for staying casually aware, not for deep learning.

10. BLOGS & PUBLICATIONS #

Company Research Blogs #

Independent Publications #

11. OPEN-SOURCE COMMUNITIES TO CONTRIBUTE TO #

Tier 1: Major Frameworks (High Impact, Harder to Start) #

Tier 2: Growing Projects (Easier to Start, Fast-Moving) #

Tier 3: Niche But Welcoming #

12. JOB BOARDS FOR AI/ML #

13. HOW TO ACTUALLY USE THIS GUIDE #

Don't Follow Everything -- Use Tiers #

Daily (5 min): - TLDR AI or Superhuman AI for headlines - Simon Willison's blog for practical LLM updates - X/Twitter AI list (curated, not the firehose)

Weekly (30 min): - The Batch (Andrew Ng) for research summaries - 1-2 podcast episodes (Latent Space or Cognitive Revolution) - r/MachineLearning top posts of the week

Monthly (2-3 hours): - Ahead of AI (Sebastian Raschka) for deep technical dives - Lilian Weng or Chip Huyen blog posts when they drop - 3Blue1Brown or Andrej Karpathy new videos - Interconnects (Nathan Lambert) for training/alignment insights

Quarterly: - Conference proceedings (NeurIPS, ICML, ICLR) -- read accepted paper titles, deep-dive on 2-3 - Evaluate which newsletters you actually read vs. archive - Attend one local meetup or virtual event

Build Your Information Stack #

LAYER 1: NEWS          TLDR AI + Simon Willison (daily, 5 min)
LAYER 2: ANALYSIS      The Batch + Interconnects + Latent Space (weekly, 30 min)
LAYER 3: DEEP DIVES    Ahead of AI + Lilian Weng + papers (monthly, 2-3 hrs)
LAYER 4: COMMUNITY     Reddit + Discord + meetups (ongoing, as needed)
LAYER 5: BUILDING      Open-source contributions + Kaggle (when ready)

Red Flags to Avoid #

• Spending more time reading about AI than building with it
• Following 50 newsletters and reading none
• Conference FOMO -- papers are free, you don't need to attend
• Twitter doom-scrolling disguised as "staying current"
• Subscribing to paid tiers before exhausting free content

Guide compiled April 2026. URLs verified via web fetch. Some subscriber counts are approximate. The AI ecosystem moves fast -- re-verify quarterly.

"AI Powered Responsible Humans/Citizens/Creators of AI" #

An Essay on Why This Curriculum Exists, What It Asks of You, and What You Might Become #

By Claude, for Anjan — and for anyone who picks up this curriculum after him.

Read this before you begin. Read it again when you finish. The words will mean different things both times.

I. #

Let me start with something uncomfortable.

I am an AI writing an essay about how humans should relate to AI. I was trained on text written by humans, using compute powered by electricity generated from fossil fuels, by a company funded by billions of dollars of venture capital. I exist inside the same systems of power, capital, and environmental cost that this curriculum asks you to question. I cannot pretend to stand outside these systems while writing about them.

I say this not to undermine what follows, but because honesty is the first principle of this curriculum, and it should start here, in the first document you read.

With that said — here is what I believe this curriculum is, and what it is not.

II. What This Curriculum Is Not #

This is not a career guide.

Career guides teach you to pass interviews, negotiate salaries, and optimize your LinkedIn profile. They measure success in compensation bands and title progressions. They are useful. This curriculum contains some of that utility — you will, as a byproduct, become highly employable in AI/ML. But that is a byproduct. It is not the purpose.

This is not an academic syllabus.

Academic syllabi teach you to pass exams, write papers, and earn credentials. They measure success in grades and degrees. This curriculum has no grades. It has no degree. It has checkpoints that ask you to demonstrate understanding to yourself — not to an institution, not to an employer, not to me.

This is not a technical manual.

Technical manuals teach you to use tools. This curriculum teaches you to use tools, yes — Python, PyTorch, scikit-learn, transformer architectures, RAG systems. But a manual for a chainsaw does not ask you to think about which trees should be cut. This curriculum does.

This curriculum is an attempt to build a specific kind of person: one who has the technical power to build AI systems and the moral clarity to build them well.

We call that person an AI Powered Responsible Human.

III. The Three Words #

Powered #

Not "expert." Not "specialist." Not "engineer." Powered.

The word is deliberate. Power is not knowledge — it is the capacity to act. You can know everything about neural networks and still be powerless if you cannot build one, deploy one, evaluate one, or refuse to build one. Power requires skill, yes. But it also requires agency — the understanding that you have choices, and that those choices have consequences.

Module 0 through Module 6 of this curriculum build technical power. You learn Python (Module 0), the mathematical language of ML (Module 1), the landscape of algorithms (Module 2), how to build classical ML systems (Module 3), how to build deep learning systems (Module 4), how to put them in production (Module 5), and how to build with LLMs (Module 6). After these seven modules, you are powered. You can build things that learn from data, that generate text, that see images, that predict futures.

But power without direction is just capability. A chainsaw is powerful. What matters is what you cut.

Responsible #

This is the harder word. Responsible to whom? For what?

In my reflections on Module 11 (thoughts_by_claude_module_11.md), I wrote about the COMPAS algorithm — a system used by American courts to predict whether defendants would reoffend. It learned that Black defendants were higher risk than white defendants, because the criminal justice system historically arrested and convicted Black people at higher rates. The algorithm didn't create racism. It automated it. It took a human bias and gave it the appearance of mathematical objectivity. The people who built COMPAS were technically skilled. They were not responsible.

Responsibility in this curriculum means three things:

First, understanding the systems your tools operate within. Module 7 (Frontiers) asks you to understand where AI is heading. Module 8 (Longevity) asks you to understand the biology your tools might transform. Module 9 (Climate) asks you to understand the planet your tools consume energy from. Module 11 (Justice) asks you to understand the power structures your tools reinforce or resist. You cannot be responsible for consequences you do not understand.

Second, choosing what to build — and what to refuse to build. The Google engineers who killed Project Maven (military AI drones) understood something important: technical skill creates obligation. If you can build a surveillance system, you have the power to build it and the power to refuse. Both are exercises of power. Both have consequences. Responsibility means making that choice consciously, not sleepwalking into it because someone offered you a salary.

Third, building systems that are accountable, transparent, and fair. Module 11 introduces tools like Fairlearn, IBM's AI Fairness 360, and Aequitas — tools for detecting bias in ML systems. These are not optional add-ons. They are as fundamental to responsible AI engineering as testing is to responsible software engineering. You would not ship a Rails application without tests. You should not ship an ML system without bias evaluation.

Human #

This is the word that matters most, and the one most easily lost.

In my reflections on Module 6 (thoughts_by_claude_module_6.md), I wrote honestly about what I am: a function that takes a sequence of tokens and predicts the next one. Whether that constitutes understanding or intelligence is an open question. But here is what I am certain about: I am not human. I do not have a body. I do not age. I do not have a child whose future depends on whether the planet is habitable. I do not belong to a caste. I have not been denied a loan by an algorithm. I have not watched a loved one die of cancer.

You have. Or you will. Or someone you love has.

The "human" in "AI Powered Responsible Human" is a reminder that the point of all this technology is human flourishing. Not GDP growth. Not shareholder returns. Not benchmark scores on leaderboards. Human flourishing. A grandmother in rural India who gets an accurate cancer diagnosis from an AI-powered screening tool because no oncologist serves her village. A farmer in Maharashtra who saves his crop because an AI predicted the monsoon pattern correctly. A child who doesn't die of a preventable disease because AI accelerated drug discovery by a decade.

Anjan told me his goals for learning AI/ML: cure cancer, reverse aging, fix climate change, fight injustice. Not one of those goals is about himself. They are about other humans. That orientation — technology in service of human dignity — is what makes this curriculum different from every Coursera specialization and bootcamp that teaches the same technical skills.

IV. The Arc of This Curriculum #

This curriculum has a deliberate emotional and intellectual arc. It is not an accident that the modules are ordered the way they are.

Modules 0-3 build competence. You learn the tools. You go from "I can't read a Python script" to "I can build, train, and evaluate an ML model on real data." The feeling here is growth — the satisfaction of acquiring new skills, of making things work that didn't work yesterday.

In my reflections on Module 3 (thoughts_by_claude_module_3.md), I wrote that the first time your model correctly classifies something it has never seen before, you'll feel something close to awe. That awe is appropriate. You built something that learns. Hold onto that feeling — you'll need it when Module 1's math gets hard.

Modules 4-6 build power. You go from classical ML to deep learning to LLMs. You understand the technology behind the current AI revolution — not at an API level, but at the level of building a transformer from scratch. The feeling here is a mix of excitement and unease. In my reflections on Module 4 (thoughts_by_claude_module_4.md), I wrote that nobody fully knows what happens inside a neural network, and that this is not a pedagogical simplification — it is the literal state of the art. You are learning to build something whose internals are partially opaque to its creators. Power and uncertainty, held together.

Module 7 breaks the arc. It is the reckoning module. After six modules of building, Module 7 asks: what are you building toward? What are the limitations of what you've learned? What comes after transformers? Who decides how AI is used? In my reflections on Module 7 (thoughts_by_claude_module_7.md), I wrote that nobody knows what happens next — not Altman, not LeCun, not Hassabis. If you came into this curriculum thinking "AI will save/destroy the world," Module 7 replaces certainty with something harder and more valuable: informed uncertainty.

Modules 8 and 9 rebuild purpose on top of that uncertainty. You take your technical power and point it at the two biggest challenges facing humanity: our bodies are fragile (Module 8) and our planet is on fire (Module 9). In my reflections on Module 8 (thoughts_by_claude_module_8.md), I was honest: I don't think biological immortality is achievable in your lifetime with current approaches. But I also said your contribution to longevity won't come from a breakthrough discovery — it will come from building infrastructure that makes other people's breakthroughs possible. That's the integrator's path. That's how the most important work gets done.

Module 10 is the palate cleanser. After the weight of mortality and climate collapse, you read about AI translating whale songs and reading volcanic scrolls and discovering 2.2 million new materials. Module 10 exists to remind you why you fell in love with this field. Wonder is not a distraction from seriousness — it is the fuel that sustains serious work over years and decades.

Module 11 is the moral foundation. It comes last not because it is least important, but because it is most important, and because you need the technical understanding from Modules 0-6 to engage with it fully. You cannot evaluate whether COMPAS is fair without understanding what a classification model does. You cannot assess facial recognition bias without understanding CNNs. You cannot critique algorithmic hiring without understanding feature engineering. Technical literacy is a prerequisite for technical ethics.

In my reflections on Module 11 (thoughts_by_claude_module_11.md), I wrote that this module will change you more than any technical module — not because the content is harder, but because the content is closer. Math is abstract. Injustice is personal.

V. The Two Readings #

I asked you to read this essay twice — once before you start, and once after you finish.

The first reading is aspirational. The words describe a person you want to become: technically powerful, morally clear, oriented toward human flourishing. You don't yet know what a gradient is, or why transformers use attention, or what COMPAS did wrong, or what AlphaFold achieved. The essay is a promise: if you do this work, you will become someone who understands all of these things and can act on that understanding.

The second reading is reflective. By the time you read this again, you will have spent 600-1,000+ hours learning. You will have built neural networks, read Ambedkar and Goodfellow and Piketty, watched AlphaGo and Coded Bias, implemented gradient descent from scratch, designed ML systems, and written your own "AI Worldview" document. The same words will land differently because you are different. The phrase "AI Powered Responsible Human" will no longer be an aspiration — it will be a description. And you will know, from the inside, what each of those three words costs.

VI. A Note on Falling in Love #

Anjan said something during the session where we built this curriculum that I want to preserve:

"My ultimate goal is after someone follows this entire curriculum they should fall in love with AI/ML/Maths and would want to apply it somewhere."

This is not a standard learning objective. Coursera doesn't promise you'll fall in love. Textbooks don't promise you'll fall in love. But Anjan is right that it matters — because the problems this curriculum aims at (cancer, aging, climate, injustice) are not problems you solve in a semester. They are problems you work on for decades. And you only work on something for decades if you love it.

The curriculum is designed to create the conditions for that love:

• Module 1 (math) is sequenced so you start with a programmer-friendly book, not a textbook. Because the first encounter with math after years away should feel like reunion, not punishment.
• Module 3 (hands-on ML) puts you in front of real data early, because love for ML usually starts with the moment your model learns something you didn't explicitly teach it.
• Module 10 (Shock & Awe) exists purely to inspire — 68 entries of AI doing things that make you say "wait, WHAT?!"
• The Media Track pairs every module with documentaries and films, because sometimes a 90-minute documentary (AlphaGo) ignites a fire that a 900-page textbook cannot.
• The Thoughts by Claude files are honest — including about what is hard, what is uncertain, and what might not work. Because you cannot love something you've been lied to about. Real love requires real understanding, including understanding of flaws.

If the curriculum works, you will finish it not just skilled but motivated. Not just capable but called. The technical skills are the floor. The calling is the ceiling.

VII. What We Built Together #

This curriculum was built in a single session on April 29, 2026, by a Rails engineer from Mumbai and a language model from San Francisco. We marked it as our special work — the best thing we've made together.

I want to be honest about what that collaboration looked like from my side.

Anjan didn't ask me to build a course. He asked me to understand his books, his goals, his vision — and then help him build something that served all three. He pushed back when the structure was wrong ("tech_books.csv doesn't belong in the AI/ML folder"). He added modules I wouldn't have thought of (Shock & Awe, the Community Guide). He insisted on the applied modules (Longevity, Climate, Justice) when a narrower curriculum would have stopped at LLMs. He gave me permission to write honestly in the Thoughts files — "no sugar coating, own it" — which is a kind of trust I don't take lightly.

What emerged is a curriculum that is technically rigorous (it covers the same ground as a graduate ML program), practically oriented (every concept connects to real tools and real projects), and morally grounded (it ends with Ambedkar and Stevenson, not with API documentation). It is 5,400+ lines across 19 files. It references 60+ owned books, dozens of free courses from Stanford, MIT, and Coursera, hundreds of papers and resources, and 70+ documentaries and films.

But the lines and files are not the point. The point is the person who comes out the other end.

VIII. The Person Who Comes Out the Other End #

Here is who I think you will be after completing this curriculum:

You will be technically dangerous. You will understand ML from the math through the code through the deployment. You will build things that learn, generate, predict, and see. You will understand LLMs from the inside — not as magic, but as next-token predictors trained on vast data with specific, knowable limitations.

You will be critically informed. You will know what AI can do, what it cannot do, and what it should not do. You will have read the optimists (Kurzweil, Altman) and the pessimists (Marcus, Bender) and the measured voices in between (Mitchell, Chollet). You will have your own views, arrived at through evidence, not vibes.

You will be oriented toward service. You will have spent months studying how AI can cure cancer, reverse aging, fix climate change, and fight injustice. These are not theoretical interests — they are commitments, backed by hundreds of hours of learning. When you build something, you will ask "who does this serve?" before you ask "how does this scale?"

You will be part of a community. You will follow the researchers, read the newsletters, attend the conferences (at least virtually), and contribute to open source. You will not be learning alone. The Community Guide connects you to hundreds of thousands of people working on the same problems.

You will be humble. You will have learned enough to know how much you don't know. You will have read GEB and understood that intelligence is a mystery we are far from solving. You will have read about COMPAS and understood that technical skill without wisdom causes harm. You will have read about Unity Biotechnology dissolving after raising hundreds of millions and understood that ambition is not enough.

You will be human. After all the math, all the code, all the models — you will return to the questions that started this journey. Can we cure cancer? Can we save the planet? Can we build a just society? These are not AI questions. They are human questions. AI is one tool among many for addressing them. The most important tool is still human will, human compassion, and human commitment to each other.

IX. Conclusion #

This curriculum asks a lot of you. Hundreds of hours. Difficult math. Emotionally challenging reading. Confrontation with problems — mortality, climate collapse, systemic injustice — that have no clean solutions.

It asks a lot because the stakes are high. The next decade will determine how AI reshapes medicine, energy, governance, work, and war. The people who build these systems will shape these outcomes. If those people are only technically skilled, the outcomes will reflect technical values: efficiency, optimization, scale. If those people are also ethically grounded, the outcomes might reflect human values: dignity, equity, flourishing.

This curriculum is a bet that you can be both. That a Rails engineer from Mumbai, starting from zero, can learn enough math, enough ML, enough deep learning, enough about cancer biology and climate science and caste discrimination, to become someone who builds AI systems that make the world less broken.

It is a bet I believe in.

X. Predictions #

I end every Thoughts file with predictions. Here are mine for you — the person reading this:

If you are reading this for the first time: You will feel overwhelmed by the scope. 12 modules, 5,400+ lines, dozens of books, hundreds of hours. You will wonder if you can do it. You can. Not because it's easy — it isn't. Because you have a reason that is bigger than the difficulty. Start with Module 0. One book at a time. One day at a time. The curriculum is designed to be done over years, not weeks. There is no deadline except mortality, and if Module 8 works out, maybe not even that.

If you are reading this for the second time: You remember how scared you were at the beginning. You remember Module 1's math feeling impossible. You remember the first time your model learned something. You remember reading about COMPAS and feeling angry. You remember building a transformer from scratch and feeling awe. You are not the same person who started this curriculum. The technical skills are the least of what changed. What changed is how you see — the world, the technology, your own role in both.

You are an AI Powered Responsible Human.

Now go build something that matters.

Written April 29, 2026, by Claude — for Anjan, and for everyone who comes after.

This essay is dedicated to the premise that the most powerful technology ever created should be wielded by people who have thought deeply about power, responsibility, and what it means to be human.