https://github.com/armankhondker/awesome-ai-ml-resources
Learn AI/ML for beginners with a roadmap and free resources.
https://github.com/armankhondker/awesome-ai-ml-resources
List: awesome-ai-ml-resources
artifical-intelligense machine-learning roadmap
Last synced: 3 months ago
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Learn AI/ML for beginners with a roadmap and free resources.
- Host: GitHub
- URL: https://github.com/armankhondker/awesome-ai-ml-resources
- Owner: armankhondker
- License: mit
- Created: 2025-02-09T00:12:17.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-02-20T17:09:43.000Z (3 months ago)
- Last Synced: 2025-02-20T18:24:22.126Z (3 months ago)
- Topics: artifical-intelligense, machine-learning, roadmap
- Homepage: https://www.aimlengineer.io
- Size: 65.4 KB
- Stars: 2,042
- Watchers: 35
- Forks: 239
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Changelog: newsletters.md
- License: LICENSE
Awesome Lists containing this project
- ultimate-awesome - awesome-ai-ml-resources - Learn AI/ML for beginners with a roadmap and free resources. . (Other Lists / Julia Lists)
- awesome-hacking-lists - armankhondker/awesome-ai-ml-resources - Learn AI/ML for beginners with a roadmap and free resources. (Others)
README
# Awesome AI/ML Resources
This repository contains free resources and a roadmap to learn Machine Learning and Artificial Intelligence in 2025.
## 📌 AI/ML Key Concepts
- [Supervised Learning](https://medium.com/@kodeinkgp/supervised-learning-a-comprehensive-guide-7032b34d5097)
- [Unsupervised Learning](https://cloud.google.com/discover/what-is-unsupervised-learning?hl=en#what-is-unsupervised-learning)
- [Reinforcement Learning](https://spinningup.openai.com/en/latest/user/introduction.html#what-this-is)
- [Deep Learning](https://www.datacamp.com/tutorial/tutorial-deep-learning-tutorial)
- [Natural Language Processing (NLP)](https://medium.com/@ageitgey/natural-language-processing-is-fun-9a0bff37854e)
- [Computer Vision](https://www.geeksforgeeks.org/computer-vision/)
- [Generative adversarial networks (GANs)](https://aws.amazon.com/what-is/gan/)
- [Dimensionality Reduction](https://scikit-learn.org/stable/modules/decomposition.html)
- [Clustering Algorithms](https://scikit-learn.org/stable/modules/clustering.html)
- [Bayesian Inference](https://www.statlect.com/fundamentals-of-statistics/Bayesian-inference#:~:text=Bayesian%20inference%20is%20a%20way,that%20could%20generate%20the%20data.)
- [Time Series Analysis](https://otexts.com/fpp3/)
- [Self-Supervised Learning](https://lilianweng.github.io/posts/2021-05-31-self-supervised-learning/)
## 🛠️ AI/ML Building Blocks
- [Linear Algebra for Machine Learning](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/)
- [Probability & Statistics](https://www.youtube.com/watch?v=2MuDZIAzBMY&list=PLoROMvodv4rOpr_A7B9SriE_iZmkanvUg)
- [Calculus for Optimization](https://www.khanacademy.org/math/multivariable-calculus)
- [Python for Machine Learning](https://www.coursera.org/learn/ai-python-for-beginners)
- [Optimization Techniques](https://www.geeksforgeeks.org/optimization-algorithms-in-machine-learning/)
- [Data Preprocessing & Feature Engineering](https://www.geeksforgeeks.org/what-is-feature-engineering/)
- [Model Evaluation & Metrics](https://scikit-learn.org/stable/modules/model_evaluation.html)
- [Regularization Techniques](https://www.geeksforgeeks.org/regularization-in-machine-learning/)
- [Loss Functions](https://www.datacamp.com/tutorial/loss-function-in-machine-learning)
- [Activation Functions](https://ml-cheatsheet.readthedocs.io/en/latest/activation_functions.html)
- [Hyperparameter Tuning](https://www.geeksforgeeks.org/hyperparameter-tuning/)
## 👨🏽💻 AI/ML Roles
- [Machine Learning Engineer](https://www.coursera.org/articles/what-is-machine-learning-engineer)
- [Data Scientist](https://www.coursera.org/articles/what-is-a-data-scientist)
- [Software Engineer (AI)](https://www.coursera.org/articles/ai-engineer)
- [ML/AI Platform Engineer](https://ml-ops.org/)
- [ML/AI Infrastructure Engineer](https://www.databricks.com/glossary/mlops)
- [Framework Engineer](https://careers.qualcomm.com/careers/job/446698240161)
- [Solution Architect](https://www.coursera.org/articles/solutions-architect)
- [Developer Advocate](https://www.freecodecamp.org/news/what-the-heck-is-a-developer-advocate-87ab4faccfc4/)
- [Solutions Engineer](https://www.coursera.org/articles/solutions-engineer)
- [Applied Research Scientist](https://www.indeed.com/career-advice/finding-a-job/data-scientist-vs-research-scientist-vs-applied-scientist)
- [Research Engineer](https://www.indeed.com/career-advice/finding-a-job/research-engineers)
- [Research Scientist](https://www.coursera.org/articles/research-scientist)
## 🚗 AI/ML Roadmap
1. Learn Python and Core Libraries
- [Intro Python](https://cs50.harvard.edu/python/2022/)
- [Advanced Python](https://www.edx.org/learn/artificial-intelligence/harvard-university-cs50-s-introduction-to-artificial-intelligence-with-python)
- [NumPy: Numerical computing and arrays](https://numpy.org/devdocs/user/quickstart.html)
- [Pandas: Data manipulation and analysis](https://www.w3schools.com/python/pandas/default.asp)
- [Matplotlib & Seaborn: Data visualization](https://matplotlib.org/stable/tutorials/index.html)
- [scikit-learn: Implement ML algorithms](https://scikit-learn.org/1.4/tutorial/index.html)
2. Build a Strong Math Foundation
- [Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/)
- [Probability & Statistics](https://web.stanford.edu/class/stats116/syllabus.html)
- [Calculus](https://www.khanacademy.org/math/multivariable-calculus)
3. Learn Machine Learning Fundamentals
- [Google Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course)
- [Machine Learning by Andrew Ng](https://www.coursera.org/learn/machine-learning)
- [Read Hundred-Page ML Book](http://ema.cri-info.cm/wp-content/uploads/2019/07/2019BurkovTheHundred-pageMachineLearning.pdf)
4. Build Practical Experience
- [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)
- [Practical Deep Learning for Coders](https://course.fast.ai/)
- [Structured Machine Learning Projects](https://www.coursera.org/learn/machine-learning-projects)
- [Build GPT](https://www.youtube.com/watch?v=kCc8FmEb1nY&t=1331s)
5. Deepen Knowledge in Specialized Areas
- [Natural Language Processing](https://huggingface.co/learn/nlp-course/chapter1/1)
- [Reinforcement Learning](https://huggingface.co/learn/deep-rl-course/unit0/introduction)
- [Computer Vision](https://www.kaggle.com/learn/computer-vision)
- [Deep Learning](https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PLSVEhWrZWDHQTBmWZufjxpw3s8sveJtnJ&index=1)
- [Transformers](https://huggingface.co/learn/nlp-course/chapter1/1)
6. Learn about MLOps
- [Intro to MLOps](https://ml-ops.org/)
- [Three levels of ML](https://ml-ops.org/content/three-levels-of-ml-software)
- [Fullstackdeeplearning](https://fullstackdeeplearning.com/course/2022/)
7. Read Interesting Research Papers
- [ArXiv for Research Papers](https://arxiv.org/)
8. Prepare for AI/ML Job Interviews
- [Introduction to Machine Learning Interviews](https://huyenchip.com/ml-interviews-book/)
- [Designing Machine Learning Systems](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/)
## 📚 Courses
- [Machine Learning by Andrew Ng (Coursera)](https://www.coursera.org/learn/machine-learning)
- [AI For Everyone by Andrew Ng (Coursera)](https://www.coursera.org/learn/ai-for-everyone)
- [Deep Learning Specialization (Coursera)](https://www.coursera.org/specializations/deep-learning)
- [Machine Learning with Python (edX - IBM)](https://www.edx.org/course/machine-learning-with-python-a-practical-introduct)
- [Reinforcement Learning Specialization (Coursera)](https://www.coursera.org/specializations/reinforcement-learning)
- [CS231n: Convolutional Neural Networks for Visual Recognition (Stanford)](https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PLSVEhWrZWDHQTBmWZufjxpw3s8sveJtnJ&index=1)
- [RL Course by David Silver](https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ)
- [Natural Language Processing with Deep Learning (Stanford - CS224n)](https://www.youtube.com/watch?v=rmVRLeJRkl4&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4&index=1)
- [Fast.ai’s Practical Deep Learning for Coders](https://course.fast.ai/)
## 🎓 Certifications
- [AWS Certified Machine Learning Engineer – Associate](https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/)
- [Microsoft Certified: Azure AI Engineer Associate](https://learn.microsoft.com/en-us/certifications/azure-ai-engineer/)
- [Stanford AI and Machine Learning Certificate](https://online.stanford.edu/programs/artificial-intelligence-professional-program)
## 📕 Books
- [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)
- [AI Engineering: Building Applications with Foundational Models](https://www.oreilly.com/library/view/ai-engineering/9781098166298/)
- [Introduction to Machine Learning Interviews](https://huyenchip.com/ml-interviews-book/)
- [Designing Data Intensive Applications](https://www.oreilly.com/library/view/designing-data-intensive-applications/9781491903063/)
- [Designing Machine Learning Systems](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/)
- [Deep Learning](https://www.deeplearningbook.org/)
## 🛠️ Tools & Frameworks
- [PyTorch](https://www.youtube.com/watch?v=V_xro1bcAuA)
- [TensorFlow](https://www.youtube.com/watch?v=tPYj3fFJGjk)
- [Scikit-Learn](https://scikit-learn.org/stable/getting_started.html)
- [XGBoost](https://xgboost.readthedocs.io/en/latest/)
- [Keras](https://keras.io/getting_started/)
- [Perplexity](https://www.perplexity.ai/)
- [CursorAI](https://www.cursor.com/)
- [Whisper](https://github.com/openai/whisper)
## AI/ML Research Blogs
- [OpenAI Blog](https://openai.com/news/)
- [Google DeepMind](https://deepmind.google/discover/blog/)
- [Google Research](https://research.google/blog/)
- [Apple ML Research](https://machinelearning.apple.com/)
- [Amazon Science](https://www.amazon.science/blog?f0=0000016e-2fb1-d205-a5ef-afb9d52c0000&f0=0000016e-2ff0-da81-a5ef-3ff057f10000&f0=0000016e-2ff1-d205-a5ef-aff9651e0000)
- [Microsoft AI](https://www.microsoft.com/en-us/ai/blog/)
- [Meta AI Blog](https://ai.meta.com/blog/?page=1)
## AI/ML Applied Blogs
- [AWS Machine Learning Blog](https://aws.amazon.com/blogs/machine-learning/)
- [NVIDIA - Deep Learning Blog](https://blogs.nvidia.com/blog/category/deep-learning/)
- [AirBnB Engineering, AI & ML](https://medium.com/airbnb-engineering/ai/home)
- [Spotify Engineering](https://engineering.atspotify.com/)
- [Uber Engineering](https://eng.uber.com/category/articles/ai/)
- [Netflix Blog](https://netflixtechblog.com/)
- [Google AI](https://blog.google/technology/ai/)
## AI/ML Problems
### Easy
- [Matrix times Vector](https://www.deep-ml.com/problems/1)
- [Titanic: Machine Learning from Disaster](https://www.kaggle.com/c/titanic)
- [Predicting House Prices Using Linear Regression](https://www.kaggle.com/competitions/home-data-for-ml-course)
### Medium
- [Single Neuron](https://www.deep-ml.com/problems/24)
- [K-Means Clustering](https://www.deep-ml.com/problems/17)
- [Predicting Loan Default Risk](https://www.kaggle.com/c/home-credit-default-risk)
- [Sentiment Analysis on Movie Reviews](https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews)
### Hard
- [Decision Tree Learning](https://www.deep-ml.com/problems/20)
- [Implement a Simple RNN with Backpropagation](https://www.deep-ml.com/problems/62)
- [Generative Adversarial Networks (GANs) for Image Synthesis](https://www.kaggle.com/c/generative-dog-images)
## ⚡️ AI/ML Communities
- [r/LearnMachineLearning](https://www.reddit.com/r/learnmachinelearning/)
- [Chip Huyen MLOps Discord](https://discord.com/invite/dzh728c5t3)
- [Hugging Face Discord](https://discord.com/invite/hugging-face-879548962464493619)
## 📺 Youtube Channels
- [Stanford Online](https://www.youtube.com/watch?v=jGwO_UgTS7I&list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU)
- [Andrej Karpathy](https://www.youtube.com/watch?v=VMj-3S1tku0&list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ)
- [FreeCodeCamp](https://www.youtube.com/watch?v=i_LwzRVP7bg)
- [3Blue1Brown](https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)
- [Sentdex](https://www.youtube.com/watch?v=OGxgnH8y2NM&list=PLQVvvaa0QuDfKTOs3Keq_kaG2P55YRn5v)
## 📩 Newsletters
- [The AI Engineer](https://aimlengineer.io)
## 📃 Must Read Papers
- [Attention Is All You Need (Google)](https://arxiv.org/pdf/1706.03762)
- [DeepSeek R1: Incentivizing Reasoning Capability in LLMs](https://arxiv.org/pdf/2501.12948)
- [Monolith: Real Time Recommendation System (TikTok/ByteDance)](https://arxiv.org/pdf/2209.07663)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/pdf/1810.04805)
- [Understanding Deep Learning Requires Rethinking Generalization](https://arxiv.org/pdf/1611.03530)
- [Playing Atari with Deep Reinforcement Learning](https://arxiv.org/pdf/1312.5602)
- [Distilling the Knowledge in a Neural Network](https://arxiv.org/pdf/1503.02531)
- [Open AI Key Papers in Deep RL](https://spinningup.openai.com/en/latest/spinningup/keypapers.html)