১০০% বিনামূল্যে
ওপেন সোর্স
সাইনআপ ছাড়া

হয়ে ওঠো
AI/ML বিশেষজ্ঞ
একেবারে শূন্য থেকে। শূন্য → গবেষণা প্রকাশনা · ৪৮ সপ্তাহ · সম্পূর্ণ বিনামূল্যে

সম্পূর্ণ কাঠামোবদ্ধ ও বিনামূল্যের একটি পূর্ণাঙ্গ পাঠ্যক্রম — Python-এর মূল বিষয় থেকে শুরু করে LLM, ডিপ লার্নিং এবং তোমার প্রথম গবেষণাপত্র প্রকাশ পর্যন্ত। প্রতিটি টুল ওপেন-সোর্স। প্রতিটি GPU বিনামূল্যে।

48
সপ্তাহ
10
ধাপ
60+
রিসোর্স
$0
খরচ
⚡ শেখা শুরু করো 📚 রিসোর্স দেখো
AIv3.0 free
Python PyTorch HuggingFace NumPy Pandas LLMs RAG Colab GPU Kaggle CNNs Transformers LoRA FAISS arXiv Streamlit LaTeX MLflow Optuna
✓ Colab GPU — সম্পূর্ণ বিনামূল্যে
✓ Kaggle ডেটাসেট — কোনো খরচ নেই
✓ HuggingFace Spaces — ফ্রি ডেপ্লয়
✓ Ollama — লোকাল LLM, শূন্য API খরচ
✓ arXiv — বিনামূল্যে পেপার প্রকাশ
✓ Overleaf — ফ্রি LaTeX এডিটর
// সম্পূর্ণ পাঠ্যক্রম

10 Phases. One Complete Path.

Click any phase to expand. Free video + resource links inside every week.

Phase 01 Python, Colab & Dev Environment
Wk 1–33 weeks
+
Week 01
Python Fundamentals
  • Variables, loops, functions, OOP
  • Comprehensions, generators
  • File I/O & exceptions
  • pip & virtual environments
▶ CS Dojo📖 Docs
Week 02
Google Colab & Kaggle GPU
  • Colab GPU runtime setup
  • Kaggle free GPU & datasets
  • Drive persistence
  • Exporting results
▶ Tutorial🎓 Kaggle
Week 03
Git & Project Structure
  • Git init, commit, push, PR
  • Branching strategies
  • ML project folder layout
  • README & docs
▶ Crash Course⭐ GitHub
Phase 02 NumPy, Pandas & Visualization
Wk 4–63 weeks
+
Week 04
NumPy Essentials
  • Arrays, dtypes, broadcasting
  • Indexing, slicing, boolean masks
  • Linear algebra: matmul, einsum
  • Vectorization & memory layout
▶ Tutorial📖 Docs
Week 05
Pandas Mastery
  • DataFrame, loc/iloc, filtering
  • GroupBy, merge & joins
  • Pivot, melt, reshape
  • Missing values & imputation
🎓 Kaggle📖 Docs
Week 06
Matplotlib & Seaborn
  • Subplots, styling, annotations
  • KDE, violin, box plots
  • Categorical & scatter plots
  • Publication-quality figures
▶ Tutorial📖 Seaborn
Phase 03 Data Cleaning, EDA & Feature Engineering
Wk 7–93 weeks
+
Week 07
Data Cleaning
  • MCAR, MAR, MNAR patterns
  • Outlier detection & treatment
  • Advanced imputation
  • Data quality audit
🎓 Kaggle
Week 08
Feature Engineering
  • Encoding: ordinal, one-hot, target
  • Scaling & normalization
  • Datetime features
  • Domain-specific features
🎓 Kaggle FE▶ Tutorial
Week 09
Exploratory Data Analysis
  • Uni/bivariate analysis
  • Correlation heatmaps
  • Automated EDA: ydata, Sweetviz
  • Storytelling with data
⭐ ydata
Phase 04 Classical Machine Learning
Wk 10–156 weeks
+
Week 10–11
Regression & Classification
  • Linear, Ridge, Lasso, ElasticNet
  • Logistic regression, KNN, SVM
  • Decision trees, Naive Bayes
  • Precision, recall, F1, ROC-AUC
▶ StatQuest📖 sklearn
Week 12–13
Ensemble Methods
  • Random Forest & bagging
  • XGBoost, LightGBM, CatBoost
  • Stacking & blending
  • Feature importance
📖 XGBoost🎓 Kaggle
Week 14–15
Unsupervised & Tuning
  • K-Means, DBSCAN, hierarchical
  • PCA, t-SNE, UMAP
  • Cross-validation strategies
  • Optuna hyperparameter search
▶ Clustering⭐ Optuna
Phase 05 Deep Learning with PyTorch
Wk 16–227 weeks
+
Week 16–17
Neural Networks & Autograd
  • Perceptrons, backprop, activations
  • PyTorch tensors & autograd
  • Training loops & optimizers
  • GPU training on Colab free tier
📖 PyTorch▶ 3B1B
Week 18–19
CNNs & Computer Vision
  • Conv layers, pooling, ResNet
  • Transfer learning & fine-tuning
  • Image classification & detection
  • torchvision free datasets
📖 CS231n🎓 Kaggle
Week 20–22
RNNs & Best Practices
  • RNN, LSTM, GRU, Seq2seq
  • Batch norm, dropout, weight init
  • LR schedulers, mixed precision
  • Gradient clipping
▶ LSTM📖 d2l.ai
Phase 06 NLP & Transformers
Wk 23–286 weeks
+
Week 23–24
NLP Fundamentals
  • Tokenization, BoW, TF-IDF
  • Word2Vec, GloVe embeddings
  • Text classification & sentiment
  • spaCy & NLTK
▶ NLP Hero📖 NLTK
Week 25–26
Transformer Architecture
  • Self-attention & multi-head
  • Positional encoding
  • BERT & GPT deep dives
  • "Attention is All You Need" walkthrough
📖 Illustrated📄 Paper
Week 27–28
HuggingFace & Fine-tuning
  • Transformers library & pipelines
  • Fine-tune BERT for text tasks
  • Datasets & tokenizers
  • Push to HF Hub (free)
🎓 HF Course🤗 Hub
Phase 07 Large Language Models (LLMs)
Wk 29–346 weeks
+
Week 29–30
LLM Fundamentals & Prompting
  • GPT architecture deep dive
  • Pre-training & emergent abilities
  • Prompt engineering techniques
  • Run Mistral/Llama with Ollama
🎓 DeepLearning.AI🦙 Ollama
Week 31–32
Fine-tuning: LoRA & QLoRA
  • PEFT: LoRA, QLoRA, prefix tuning
  • Fine-tune on Colab free GPU
  • RLHF basics
  • ROUGE / BERTScore evaluation
⭐ PEFT📖 TRL
Week 33–34
RAG & LLM Applications
  • Vector DBs: Chroma, FAISS
  • Retrieval-Augmented Generation
  • LangChain & LlamaIndex
  • Build a doc Q&A app — free
📖 LangChain⭐ Chroma
Phase 08 MLOps & Free Deployment
Wk 35–384 weeks
+
Week 35–36
Experiment Tracking
  • MLflow (open-source)
  • W&B free tier
  • DVC for data versioning
  • Automated pipelines
📖 MLflow📊 W&B
Week 37–38
Deploy for Free
  • FastAPI model serving
  • Docker basics
  • HuggingFace Spaces (free)
  • Gradio & Streamlit demos
🤗 Spaces🎈 Streamlit
Phase 09 Reading & Conducting AI Research
Wk 39–424 weeks
+
Week 39–40
Reading Research Papers
  • How to read ML papers fast
  • arXiv navigation & alerts
  • Paper notes & summaries
  • Connected Papers tool
📄 arXiv📊 PwC
Week 41–42
Research Methodology
  • Hypothesis & experiment design
  • Statistical significance
  • Ablation studies
  • Writing a literature review
🔗 Connected Papers
Phase 10 Thesis Writing & First Publication
Wk 43–486 weeks
+
Week 43–44
LaTeX & Academic Writing
  • Overleaf (free) & LaTeX basics
  • IEEE & ACL templates
  • BibTeX citations
  • Figures, tables, equations
📖 Overleaf
Week 45–46
Capstone Research Project
  • Define a novel ML problem
  • Full experiment + ablation study
  • Write complete paper draft
  • Community peer feedback
📊 SOTA
Week 47–48
Submit & Publish
  • Submit preprint to arXiv
  • OpenReview workshops
  • Responding to reviewers
  • Build your Google Scholar profile
📄 arXiv🔍 OpenReview
// বিনামূল্যের রিসোর্স লাইব্রেরি

৬০+ রিসোর্স।
প্রতিটি সম্পূর্ণ বিনামূল্যে।

ধরন অনুযায়ী ফিল্টার করো। প্রতিটি কার্ডে কঠিনতা, কী শিখবে এবং সরাসরি লিংক আছে। কোনো পেওয়াল নেই।

Course
Kaggle Learn — Full Track
Micro-courses covering Python, Pandas, ML, Deep Learning, NLP, SQL, and Feature Engineering. Each course gives a free certificate and takes 3–5 hours.
Free certificate included
PythonMLDLNLP
by KaggleOpen →
Course
DeepLearning.AI Short Courses
Free short courses (2–4 hrs each) on ChatGPT APIs, LangChain, LLMOps, RAG, Fine-tuning, Prompt Engineering, and more. By Andrew Ng's team.
LLMs, RAG, Fine-tuning
LLMsRAGPrompting
by DeepLearning.AIOpen →
Course
HuggingFace NLP Course
Complete free NLP course covering Transformers, tokenizers, fine-tuning BERT/GPT, datasets, and deployment. Interactive notebooks, all free.
Transformers from scratch
BERTGPTTokenizers
by HuggingFaceOpen →
Course
Fast.ai — Practical Deep Learning
Top-down DL course using PyTorch. Free Jupyter notebooks, incredible forum. Takes you from basics to cutting-edge models. Loved by practitioners worldwide.
Practical DL, real models
PyTorchCNNsNLP
by Jeremy HowardOpen →
Course
CS231n — Visual Recognition
Stanford's legendary computer vision course. Full lecture notes, slides, and coding assignments free online. The gold standard for learning CNNs.
CNNs, detection, segmentation
CNNsResNetDetection
by StanfordOpen →
Course
CS224n — NLP with Deep Learning
Stanford's NLP with deep learning course. Free lecture slides, videos on YouTube, and assignments covering word vectors, transformers, and generation.
NLP research-level knowledge
NLPTransformersGeneration
by StanfordOpen →
Course
Full Stack Deep Learning
Free course covering the full ML lifecycle — from training to production deployment. Labs, videos, and infrastructure material all available free.
Production ML, MLOps
MLOpsProductionDeploy
by FSDL TeamOpen →
Video
3Blue1Brown — Neural Networks
Breathtaking animated series explaining neural networks, backpropagation, and gradient descent with mathematical precision. Essential first watch for every learner.
Visual intuition for deep learning
MathBackpropIntuition
by Grant SandersonWatch →
Video
StatQuest with Josh Starmer
The clearest explanations of ML algorithms on the internet. Every video builds intuition from scratch: SVMs, random forests, PCA, UMAP, Transformers, and more.
Deep algorithmic understanding
StatsMLDL
by Josh StarmerWatch →
Video
Andrej Karpathy — Neural Nets: Zero to Hero
Build GPT from scratch, character-level language models, and neural nets step by step in Python. The most hands-on LLM tutorial that exists, from an OpenAI co-founder.
Build GPT from scratch
GPTLLMsFrom Scratch
by Andrej KarpathyWatch →
Video
Sentdex — Python ML Tutorials
Massive playlist of practical ML and DL tutorials using scikit-learn, PyTorch, and TensorFlow. Great for seeing real code applied to real problems.
Practical code walkthroughs
sklearnPyTorchProjects
by Harrison KinsleyWatch →
Video
Yannic Kilcher — Paper Explanations
Deep dives into landmark ML papers: GPT-4, AlphaFold, Diffusion models, LLaMA, and more. Perfect for Phase 9 when you start reading research papers seriously.
ML paper comprehension
ResearchPapersLLMs
by Yannic KilcherWatch →
Video
CS Dojo — Python for Beginners
Friendly, well-paced intro to Python. Great as your Phase 01 companion. Covers all the fundamentals in short digestible episodes.
Python zero to comfortable
PythonBeginner
by CS DojoWatch →
Book
Dive Into Deep Learning (d2l.ai)
Free interactive DL textbook with live PyTorch + MXNet code, Colab notebooks, and math. Used at Stanford, MIT, and Carnegie Mellon. Covers everything from linear algebra to Transformers.
University-grade DL textbook
PyTorchMathTransformers
by Aston Zhang et al.Read →
Book
Hands-On ML with Scikit-Learn & TF
The most practical ML book available. All notebooks are free on GitHub. Covers classical ML, CNNs, RNNs, and more with real code examples.
Practical ML from cover to cover
sklearnTFPractical
by Aurélien GéronRead →
Book
The Little Book of Deep Learning
A compact, beautifully typeset free PDF covering the entire DL landscape — from tensor operations to Transformers and diffusion models. 200 pages of pure signal.
DL overview, print-worthy
DLTransformersDiffusion
by François FleuretRead →
Book
Illustrated Machine Learning
A free visual guide by Jay Alammar — the Illustrated Transformer, Illustrated BERT, Illustrated Word2Vec, and more. Some of the clearest visual ML explanations online.
Visual intuition for Transformers
BERTGPTVisuals
by Jay AlammarRead →
Book
Mathematics for Machine Learning
Free PDF covering linear algebra, calculus, probability, and optimisation specifically for ML. Used as a companion for understanding why algorithms work.
Math foundations for ML
Linear AlgebraCalculusProbability
by Deisenroth, Faisal, OngRead →
Tool
Google Colab — Free GPU Notebooks
Run Jupyter notebooks with free T4/A100 GPU in your browser. No setup, no installation. The backbone of this entire curriculum — use it for all your training runs.
Free GPU cloud compute
GPUJupyterCloud
by GoogleOpen →
Tool
Kaggle — Free GPU + Datasets
30 hours/week of free GPU/TPU compute, thousands of public datasets, and a global competition platform. Your primary dataset source throughout the curriculum.
Free GPU + 50,000+ datasets
GPUDatasetsCompetitions
by Google / KaggleOpen →
Tool
HuggingFace Hub + Spaces
Host models, datasets, and live Gradio/Streamlit apps for free. 300,000+ free models to download and fine-tune. Your portfolio platform for Phase 6 onward.
Free model hosting & deployment
ModelsDeployGradio
by HuggingFaceOpen →
Tool
Ollama — Local LLMs, Zero Cost
Run Llama 3, Mistral, Gemma, Phi-3 and more entirely on your own machine. No API keys, no billing. Essential for Phase 7 RAG projects without any cost.
Local LLMs, no API cost ever
LLMsLocalRAG
by OllamaOpen →
Tool
Weights & Biases (Free Tier)
The best experiment tracker for ML. Free tier includes unlimited projects, interactive charts, hyperparameter sweeps, and model versioning. Used by every serious ML team.
Experiment tracking & sweeps
MLOpsTrackingSweeps
by Weights & BiasesOpen →
Tool
Overleaf — Free LaTeX Editor
Write research papers in LaTeX, right in your browser. Free tier includes all templates (IEEE, ACL, NeurIPS). Your tool for Phase 10 thesis and paper writing.
Write papers like a researcher
LaTeXPapersIEEE
by OverleafOpen →
Tool
Streamlit — Free App Deployment
Turn Python scripts into shareable web apps in minutes. Free Community Cloud hosting. Deploy your ML demos, dashboards, and chatbots publicly with one command.
Share ML apps instantly
DeployAppsDashboard
by StreamlitOpen →
Research
Papers With Code
Find the latest ML papers with open-source code, track state-of-the-art benchmarks, and discover trending methods. Use this daily once you reach Phase 9.
Stay current with SOTA research
SOTABenchmarksCode
by Papers With CodeOpen →
Research
arXiv — CS.LG & CS.AI
Every ML paper published lands here for free. Subscribe to daily/weekly digests for cs.LG, cs.CL, stat.ML. The single most important research resource in ML.
Read every ML paper ever
Preprintscs.LGFree
by Cornell UniversityOpen →
Research
Connected Papers
Enter any ML paper and get a visual graph of related papers — citations, co-citations, and field clusters. Invaluable for literature reviews in Phase 9.
Map a research field quickly
Literature ReviewGraph
by Connected PapersOpen →
Research
OpenReview — Free Submission
Submit your research paper to NeurIPS, ICLR, ACL workshops and other top venues for free. All reviews are public. Great for your Phase 10 first publication.
Publish at top ML venues free
NeurIPSICLRACL
by OpenReviewOpen →
Research
Semantic Scholar
AI-powered free research tool. Find papers, get summaries, track authors, and discover citation networks across all of computer science. Better search than Google Scholar for ML.
Smart ML paper discovery
SearchCitationsAI-powered
by Allen InstituteOpen →
// শূন্য খরচের টুল

প্রতিটি টুল। চিরকাল বিনামূল্যে।

ওপেন-সোর্স বা স্থায়ীভাবে ফ্রি টায়ার। কোনো ক্রেডিট কার্ড লাগবে না।

🐍
Python 3
Language
☁️
Google Colab
GPU Notebooks
🏆
Kaggle
Datasets + GPU
🔢
NumPy/Pandas
Data Science
📊
Matplotlib
Visualization
🤖
Scikit-Learn
Classical ML
🔥
PyTorch
Deep Learning
🤗
HuggingFace
Transformers/LLMs
🦙
Ollama
Local LLMs
🎈
Streamlit
App Deployment
📜
Overleaf
LaTeX / Papers
📈
MLflow
Experiment Track
// শেখার যাত্রা

৫টি অধ্যায়। তোমার গতিতে।

সব ধাপ দেখতে স্ক্রোল করো। কোনো ডেডলাইন নেই — সব কিছু নিজের গতিতে।

01
// ভিত্তি
Python & Data
সপ্তাহ ১–৯ · ৩টি ধাপ
02
// ক্লাসিক্যাল ML
ML অ্যালগরিদম
সপ্তাহ ১০–১৫ · ১টি ধাপ
03
// ডিপ লার্নিং
PyTorch & DL
সপ্তাহ ১৬–২২ · ১টি ধাপ
04
// NLP ও LLM
Transformers
সপ্তাহ ২৩–৩৪ · ২টি ধাপ
05
// গবেষণা
পেপার প্রকাশ করো
সপ্তাহ ৩৫–৪৮ · ৩টি ধাপ
// হাতে-কলমে প্রজেক্ট

বাস্তব জিনিস তৈরি করো।
সব কম্পিউট বিনামূল্যে।

প্রতিটি প্রজেক্টে বিনামূল্যে GPU, ডেটাসেট ও হোস্টিং ব্যবহার হয়।

শিক্ষানবিশ
Titanic Predictor
Classic EDA + classification. Feature engineering, logistic regression, and evaluation on Kaggle's free dataset.
PandassklearnKaggle
শিক্ষানবিশ
House Price Regression
Full pipeline: data cleaning → feature engineering → XGBoost → Kaggle competition submission.
XGBoostPandasColab
মধ্যবর্তী
Image Classifier (CNN)
Train ResNet on CIFAR-10 with Colab GPU. Transfer learning, data augmentation, TensorBoard logging.
PyTorchtorchvisionColab GPU
মধ্যবর্তী
Sentiment Analysis App
Fine-tune BERT for Bengali/English sentiment. Deploy as a Gradio app on HuggingFace Spaces — free.
BERTHuggingFaceGradio
উন্নত
RAG Document Chatbot
PDF Q&A system using LangChain + Chroma + Mistral via Ollama. Zero API cost, runs locally or on Colab.
LangChainChromaOllamaStreamlit
উন্নত
First ML Research Paper
Design a novel experiment, run ablations, write in LaTeX on Overleaf, submit to arXiv + OpenReview workshop.
LaTeXOverleafarXivOpenReview
// কমিউনিটি

সবার সাথে শেখো।

সব কমিউনিটি বিনামূল্যে। অগ্রগতি শেয়ার করো, প্রশ্ন করো, একসাথে বাড়ো।

💬
HuggingFace Discord
ML community, model sharing, Q&A.
🤖
r/MachineLearning
Research, discussions, career advice.
🏆
Kaggle Forums
Competition tips & notebooks.
📄
Papers With Code
Track SOTA, find implementations.
// সাধারণ জিজ্ঞাসা

প্রশ্নের উত্তর।

এটা কি সত্যিই ১০০% বিনামূল্যে? +
Yes — every resource, tool, dataset, GPU, and deployment platform linked here is completely free. Colab free GPU, Kaggle free compute, HuggingFace Spaces for deployment, Ollama for local LLMs. No credit card, ever.
কি শক্তিশালী কম্পিউটার দরকার? +
No. All GPU-heavy training runs on Google Colab or Kaggle in your browser. A basic laptop with internet works perfectly. We chose free cloud compute specifically so hardware is never a barrier.
পূর্বশর্ত কী? +
Just basic computer literacy. Phase 01 starts from absolute zero with Python. High school algebra is helpful for ML math but we introduce every concept as needed. No prior coding experience required.
শেষ করতে কতদিন লাগে? +
~48 weeks studying 10–15 hours per week. But it's fully self-paced — no deadlines, no cohorts. Skip phases you know, spend more time on harder ones. Everything stays free and accessible forever.
সার্টিফিকেট নেই — দক্ষতা কীভাবে প্রমাণ করব? +
By building real things: a GitHub portfolio, public models on HuggingFace Hub, live Streamlit apps, and optionally a published arXiv preprint. These are far more credible to employers and grad schools than certificates.
বাংলাদেশের শিক্ষার্থীদের জন্য ভালো? +
Absolutely — designed with Bangladeshi learners in mind. Every tool works without VPN. The research track is especially valuable for those targeting grad school abroad, and all content is accessible on slow connections.
// আজই শুরু করো

কোনো সাইনআপ নেই।
কোনো অপেক্ষা নেই। শুধু শেখো।

উপরের ধাপ ১ খোলো অথবা সরাসরি Kaggle Learn-এ যাও। চিরকাল বিনামূল্যে।

⚡ ধাপ ০১ খোলো 🎓 Kaggle-এ শুরু করো
// নির্মাতা পরিচিতি

একজন শিক্ষার্থীর হাতে তৈরি,
শিক্ষার্থীদের জন্য।

এই কোর্স ও প্ল্যাটফর্ম সম্পূর্ণ একা তৈরি করা হয়েছে — কমিউনিটির জন্য একটি বিনামূল্যের উপহার হিসেবে।

Mushfiqul Alam Nasif
✦ কোর্স নির্মাতা ও পাঠ্যক্রম ডিজাইনার

Mushfiqul
Alam Nasif

কম্পিউটার বিজ্ঞান ও প্রকৌশল (CSE)

বিশ্বমানের AI/ML শিক্ষা সবার কাছে পৌঁছে দেওয়ার স্বপ্ন নিয়ে কাজ করছি — বিশেষত বাংলাদেশের সেই শিক্ষার্থীদের জন্য যারা খরচের বাধার মুখোমুখি। এই পুরো পাঠ্যক্রম, রিসোর্স লাইব্রেরি ও প্ল্যাটফর্ম একটি বিনামূল্যের পাবলিক রিসোর্স হিসেবে ডিজাইন ও তৈরি করা হয়েছে। কোনো পেওয়াল নেই।

48
সপ্তাহ
60+
রিসোর্স
$0
তোমার খরচ
github.com/nasif120696-collab ⚡ View My Curriculum