ai-roadmap

KO | EN

gitlite — search

#javascript #hacktoberfest #react #ai #llm #linux #macos #awesome #kubernetes #awesome-list #pytorch #mcp #ios #open-source

(★ 9)

a roadmap to learn the ai

ai-roadmap 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# What to learn in order

## 1. Personalized Roadmap

Get personalized roadmap for learning Machine Learning, Deep Learning, Reinforcement Learning, Linear Algebra, Optimization, Calculus, Statistics, and Probability. This site shows how concepts from all of these courses relates to each other. You just set a goal for learning and it offers you a course to learn that thing in quickest way possible.
[click here](https://maps.joindeltaacademy.com/)

## 2. Giles McMullen-Klein's Roadmap

This is the order that he gave, but I think the learning order does not match perfectly. In my opinion, you should gain some intuition in mathematics and machine learning before diving into TensorFlow

**Giles McMullen-Klein**: [youtube channel](https://www.youtube.com/@gilesmcmullen)

| **Topic** | **Courses** |
|:---:|:---:|
| Python | [Python and Jupyter](https://opentechschool.github.io/python-data-intro/core/recap.html) |
| Experience | [Kaggle Learning](https://www.kaggle.com/learn) |
| TensorFlow | [Google Machine Learning Crash Course with TensorFlow APIs](https://developers.google.com/machine-learning/crash-course/) |
| TensorFlow | [TensorFlow Tutorials](https://www.tensorflow.org/tutorials) |
| ML | [Web](https://machinelearningmastery.com/start-here/) & [Book](https://machinelearningmastery.com/products/): Machine Learning Mastery, [dl books](https://libgen.rs/search.php?req=Jason+Brownlee&open=0&res=50&view=simple&phrase=1&column=author) |
| Math | Youtube Channel: [Prof Ghrist Math](https://www.youtube.com/@prof-g/playlists) |
| Math | Book: [Mathematics for Machine Learning](https://mml-book.github.io/) |
| Probability & Statistics | [web](https://www.probabilitycourse.com/chapter1/1_1_0_what_is_probability.php) & [book](https://download.library.lol/main/3306000/74216f6e871ea6caeb4f2411fb23ff5f/Hossein%20Pishro-Nik%20-%20Introduction%20to%20Probability%2C%20Statistics%2C%20and%20Random%20Processes-Kappa%20Research%2C%20LLC%20%282014%29.pdf): Intro to Prabability, Statistics, and Random Processes |
| Statistics | [web](https://www.statisticsdonewrong.com/) & [Book](https://download.library.lol/main/1327000/a859cbca829a4d35aa8f19d1146fc440/Alex%20Reinhart%20-%20Statistics%20Done%20Wrong_%20The%20Woefully%20Complete%20Guide-No%20Starch%20Press%20%282015%29.pdf): Statistics Done Wrong |
| ML | [github](https://github.com/ageron/handson-ml3) & [book](https://library.lol/main/D6756726683D9AACB09B8B71A2E76319): Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow |
| Paper | [Paper](https://courses.cs.duke.edu/spring20/compsci527/papers/Domingos.pdf): Pedro Domingos - A few useful things to know about machine learning |
| Q&A | Question & Answer StackExchange: [Data Science](https://datascience.stackexchange.com/), [Cross Validated](https://stats.stackexchange.com/?tags=machine-learning) |
| Pytorch | [PyTorch Tutorials](https://pytorch.org/tutorials/) |
| Scikit-learn | [Scikit-Learn Tutorials](https://scikit-learn.org/stable/tutorial/index.html) |

## 3. Kooshiar's Roadmap
**Kooshiar**: [youtube channel](https://www.youtube.com/@Kooshiar)
| **Topic** | **Courses** |
|:---:|:---:|
| Math | [Khan Academy Linear Algebra](https://www.khanacademy.org/math/linear-algebra), and [Deep Learning book by Ian Goodfellow](https://www.deeplearningbook.org/): chapters 2, 3, and 4 |
| Python | [beginner](https://www.youtube.com/watch?v=kqtD5dpn9C8), [syntax & practise](https://www.w3schools.com/python/python_exercises.asp), and [python for ml](https://www.youtube.com/watch?v=7eh4d6sabA0) |
| Computer Science | [SQL Tutorial](https://www.youtube.com/watch?v=HXV3zeQKqGY) and [Data Structures](https://www.youtube.com/watch?v=RBSGKlAvoiM&t=187s) & Algorithms |
| Engineering | Cloud Computing: [source 1](https://www.youtube.com/watch?v=r4YIdn2eTm4) and [source 2](https://www.youtube.com/watch?v=xcODUk0o6tU)
| Machine Learning | Coursera: [Machine Learning Specialization Andrew Ng Coursera](https://www.coursera.org/specializations/machine-learning-introduction) (3 Courses) |
| Artificial Intelligence | Book: [Artificial Intelligence by Stuart Russell and Peter Norvig](https://aima.cs.berkeley.edu/index.html) |
| Deep Learning | Books: [Deep Learning Neural Network by Ian Goodfellow](https://www.deeplearningbook.org/) and [Reinforcement Learning by Richard Sutton](http://www.incompleteideas.net/book/RLbook2020.pdf) |
| Modern ML Theory: ML Basics | [CH5 of DL Ian GoodFellow](https://www.deeplearningbook.org/) and [Mosh Hamedani: Python ML Tutorial (1h)](https://www.youtube.com/watch?v=7eh4d6sabA0) |
| Modern ML Theory: DL | [CH 6-12 of DL Ian GoodFellow](https://www.deeplearningbook.org/) |
| Modern ML Theory: NLP | [Stanford CS224N](https://youtube.com/playlist?list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4) |
| Modern ML Theory: RL | [DeepMind x UCL](https://www.youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm) |
| Modern ML Theory: Efficient Transformers | [paper](https://arxiv.org/pdf/2009.06732.pdf) |
| Modern ML Theory: Diffusion Models, LLMs | [paper](https://arxiv.org/pdf/2209.00796.pdf) |
| Tools Tutorials | [TensorFlow](https://www.tensorflow.org/tutorials), [PyTorch](https://pytorch.org/tutorials/), and [Google Cloud: ML on GCP](https://www.youtube.com/watch?v=f-Ly6qMETDY) |
| Useful Tools | [Kaggle Competitions](https://www.kaggle.com/competitions), [Google: Free Jupyter Notebook](https://colab.research.google.com/), and [Hugging Face: Model Repo](https://huggingface.co/models) |

## 4. ML Study Guide (from AssemblyAI)
| **Topic** | **Courses** |
|:---:|:---:|
| Math | Khan Academy: [Multivariable Calculus](https://www.khanacademy.org/math/multivariable-calculus), [Differential Equations](https://www.khanacademy.org/math/differential-equations), [Linear Algebra](https://www.khanacademy.org/math/linear-algebra), [Statistics Probability](https://www.khanacademy.org/math/statistics-probability), and 3Blue1Brown: [Essence of Linear Algebra](https://www.3blue1brown.com/essence-of-linear-algebra-page/) |
| Python | freeCodeCamp Crash Courses: [beginer](https://youtu.be/rfscVS0vtbw) and [intermediate](https://youtu.be/HGOBQPFzWKo) |
| Foundational Tools | Crash Courses: [NumPy](https://youtu.be/9JUAPgtkKpI), [Pandas](https://youtu.be/vmEHCJofslg), and [matPlotLib](https://youtu.be/3Xc3CA655Y4) |
| Machine Learning | [Machine Learning Specialization Andrew Ng Coursera](https://www.coursera.org/specializations/machine-learning-introduction) (3 Courses) and [Machine Learning From Scratch](https://youtube.com/playlist?list=PLqnslRFeH2Upcrywf-u2etjdxxkL8nl7E) |
| Hands-on Data Preparation | [Kaggle Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) and [Kaggle Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning) |
| Practise | Solve Challenges and build your own projects with datasets from [Kaggle.com](Kaggle.com). |
| Specialize & Create Blog | Specialize in one field (e.g. Computer Vision, NLP, etc.). Look at requirements in corresponding job descriptions and learn those skills. Tip: Create a blog and share tutorials and what you have learned! |
| Books | [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) and [Machine Learning with PyTorch and Scikit-Learn](https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312) |

## 5. Marcel DeSutter's Roadmap (a reddit user) + some of my recoms

you might thinkg some of these courses are old, but they worth it.

| **Steps** | **Course(s)** |
|:---------:|:----------------:|
| Math: Get general intuition | 3blue1brown: [Linear Algebra](https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab), [Calculus](https://www.youtube.com/watch?v=WUvTyaaNkzM&list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr), Khan Academy: [Multivariable Calculus](https://www.youtube.com/playlist?list=PLSQl0a2vh4HC5feHa6Rc5c0wbRTx56nF7) |
| Probability Theory | Choose one of these 3 courses: MIT 6-041sc (2013) Probabilistic Systems Analysis And Applied Probability: [youtube](https://www.youtube.com/playlist?list=PLUl4u3cNGP60A3XMwZ5sep719_nh95qOe), [course page](https://ocw.mit.edu/courses/6-041sc-probabilistic-systems-analysis-and-applied-probability-fall-2013/) - MIT 6-012 (2018) Introduction To Probability: [youtube](https://www.youtube.com/playlist?list=PLUl4u3cNGP60hI9ATjSFgLZpbNJ7myAg6), [course page](https://ocw.mit.edu/courses/res-6-012-introduction-to-probability-spring-2018/), Stanford CS109 (2022) Introduction to Probability for Computer Scientists: [youtube](https://www.youtube.com/playlist?list=PLoROMvodv4rOpr_A7B9SriE_iZmkanvUg), [course page](https://web.stanford.edu/class/archive/cs/cs109/cs109.1232/) |
| Linear Algebra | MIT 18.06 (2005) Linear Algebra: [youtube](https://www.youtube.com/playlist?list=PL221E2BBF13BECF6C), [course page](https://ocw.mit.edu/courses/18-06sc-linear-algebra-fall-2011/) |
| Calculus | [Calculus with Dr. Marchese: I, II, III](https://www.youtube.com/user/amarchese22/playlists) |
| Connect all the previous concepts together | 18.065 (2018) Matrix Methods In Data Analysis, Signal Processing, And Machine Learning: [youtube](https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k), [course page](https://ocw.mit.edu/courses/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/) |
| Machine learning basics | Andrew NG's courses on coursera: [Machine Learning Specialization](https://www.coursera.org/specializations/machine-learning-introduction), [Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning), [Tensorflow in Practice Specialization](https://www.coursera.org/professional-certificates/tensorflow-in-practice)
| Machine Learning | Stanford CS229 ML: [Youtube 2008 classic ml](https://www.youtube.com/playlist?list=PLA89DCFA6ADACE599), [Youtube 2018 updated with new topics](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU), and [course page](https://cs229.stanford.edu/syllabus-autumn2018.html) - CORNELL CS4780 ML for Intelligent Systems: [youtube](https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS), [course page](https://www.cs.cornell.edu/courses/cs4780/2024sp/) |
| Python | [Corey Schafer Youtube Playlists](https://www.youtube.com/user/schafer5/playlists) |
| Programming | Book 1: [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/) and Book 2: [ Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow (3rd edition)](https://github.com/ageron/handson-ml3) |

## Other Roadmaps
1. [roadmap.sh - AI and Data Scientist Roadmap (2024)](https://roadmap.sh/ai-data-scientist)
2. [rentry.org ML roadmap](https://rentry.org/machine-learning-roadmap)
3. [Daniel Bourke ML roadmap (2020)](https://whimsical.com/machine-learning-roadmap-2020-CA7f3ykvXpnJ9Az32vYXva)
4. [Marcel DeSutter's other roadmap (2021)](https://www.reddit.com/r/learnmachinelearning/comments/qlpcl8/comment/hj5mx9h/)

# Rankings & Reviewes

## Greg Hogg's Reviews
**Greg Hogg**: [youtube channel](https://www.youtube.com/@GregHogg)
### Ranking The Best Machine Learning Courses & Books
| **Rank** | **Courses** |
|:---:|:---:|
| Outstanding Content & Free | [The Hundred-Page ML Book](http://themlbook.com/wiki/doku.php), [Andrew Ng ML Coursera Specialization](https://bit.ly/3d1QATT), [An Intro to Statistical Learning: with application in Python/R](https://www.statlearning.com/), and [Deep Learning Coursera Specialization](https://bit.ly/3KPfint) |
| Great Content, but Expensive | [Hands-on ML w Sklearn, Keras & TF](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/), [Udemy Machine Learning A-Z Py & R](https://www.udemy.com/course/machinelearning/), [Udemy Python 4 Data Science & ML Bootcamp](https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/), and [Udemy ML, DS & GAI w Python](https://www.udemy.com/course/data-science-and-machine-learning-with-python-hands-on/) |
| Good Content, but Difficult | [The Elements of Statistical Learning](https://hastie.su.domains/ElemStatLearn/) and [Depp Learning by Ian GoodFellow](https://www.deeplearningbook.org/) |
| Decent, but there's better | IBM Machine Learning Certification - IBM Mathematics for ML - All edx courses - Fast.AI Practical Deep Learning for Coders - Google Machine Learning Crash Course - ML Univ of Washington Specialization - Advanced ML HSE University |

### Data Science Professional Certifications Ranked
| **Rank** | **Courses** |
|:---:|:---:|
| UNBEATABLE | [Andrew Ng ML Coursera Specialization](https://bit.ly/3d1QATT), [Deep Learning Coursera Specialization](https://bit.ly/3KPfint), and [IBM Data Science Coursera Professional Certification](https://bit.ly/3Rn00ZA) |
| GREAT AND IT'S IN PYTHON | [IBM Introduction to Data Science Specialization](https://bit.ly/3CWaFG8), [IBM Data Science Fundamentals with Python and SQL](https://bit.ly/3Qhi7ic), [University of Washington Machine Learning Specialization](https://bit.ly/3D04S2d), and [University of Michigan Applied Data Science with Python Specialization](https://bit.ly/3RlkFNk) |

ai-roadmap

ai-roadmap 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

ai-roadmap

ai-roadmap 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README