├── .DS_Store
├── .idea
├── .gitignore
├── Machine-Learning-Roadmap.iml
├── inspectionProfiles
│ ├── Project_Default.xml
│ └── profiles_settings.xml
├── misc.xml
├── modules.xml
└── vcs.xml
├── LICENSE
├── README.md
└── images
├── Download_Roadmap_PDF.png
├── Machine_Learning_Roadmap.png
├── Machine_Learning_Roadmap_Newsletter.png
└── Machine_Learning_Roadmap_Subscribe.png
/.DS_Store:
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/README.md:
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1 | # Machine Learning Roadmap 2025 (FREE, Step-by-Step)
2 |
3 | # 🚀 Goal of the Roadmap
4 | The goal of the roadmap is to **provide a list of FREE resources that are enough to become a Middle/Senior Data Scientist starting from ZERO**.
5 | - ✅ Each topic has links to free courses, YouTube videos, articles, or book chapters.
6 | - ✅ The entire roadmap can be completed with 0$ spent.
7 | - ✅ By following the roadmap, you'll be ready for middle-level daily data science work.
8 | - ✅ You'll also be prepared for senior-level data science interviews from the theory perspective.
9 |
10 | Most of the resources were personally tested by [me,](https://www.linkedin.com/in/timurbikmukhametov/) when I was learning Machine Learning and Data Science from scratch, upgrading my skills or helping my team members doing so.
11 |
12 | ---
13 |
14 | 
15 |
16 | ---
17 |
18 |
19 | # 🎯 Who is this roadmap for?
20 | - ✅ Data Science beginners who are looking for a practical step-by-step guide.
21 | - ✅ Data Scientists who aim to level up skills for a job change or promotion.
22 | - ✅ Data Scientists who are looking to refresh their knowledge and prepare for interviews.
23 | - ✅ Data Scientists who want to level up skills in a specific domain, e.g. Optimization.
24 |
25 | # 📚 Download Machine Learning Roadmap as a PDF.
26 | The PDF version includes comments on each course, making the roadmap easier to follow and convenient to have on hand.
27 |
28 |
180 |
181 | ---
182 |
183 | ## 2. Data Science / ML Introduction
184 |
185 | ### 🙏 Please, support the repo with a STAR ⭐
186 | To help people learn ML Foundations for free, please, support this project with a GitHub star ⭐, so more people can learn ML for free.
187 |
188 | ---
189 |
190 | Now we are getting to the "most interesting" part.
191 |
192 | A solid understanding of **the basics** is crucial to being a great Data Scientist. This doesn’t mean you have to be a math genius, but **understanding core principles** will help both in your work and in interviews.
193 |
194 | This roadmap focuses on the most **widely used algorithms**—ones you **must** understand deeply. Once you master these, you’ll be able to explore other algorithms confidently.
195 |
196 | ---
197 |
198 | ### 2.1 Introduction
199 | Machine Learning is about finding patterns in data and making predictions.
200 |
201 | 💡 **Your goal?** Understand the fundamental concepts of ML, classification, and regression before moving forward.
202 |
203 | - 🎓 [Andrew Ng’s ML Course (Coursera)](https://www.coursera.org/learn/machine-learning?specialization=machine-learning-introduction)
204 | Perfect **introductory course** covering key ML concepts. Instead of browsing multiple intro courses, **start with this one.**
205 |
206 | 💡 **Note:** Coursera offers **financial aid**, so if needed, apply for it (I did it as a student, and it worked!).
207 |
208 | ---
209 |
210 | ### 2.2 Basic Probability, Statistics, and Linear Algebra
211 | To build good ML models, you **must** understand basic math concepts. You don’t need to be an expert, but knowing the fundamentals is essential.
212 |
213 | #### Linear Algebra
214 | - 🎥 [3Blue1Brown’s Linear Algebra Series](https://www.3blue1brown.com/topics/linear-algebra?ref=mrdbourke.com)
215 | **Mind-blowing visuals** that make linear algebra intuitive.
216 | - 📚 [Python Linear Algebra Tutorial - Pablo Caceres](https://pabloinsente.github.io/intro-linear-algebra)
217 | **Apply linear algebra concepts in Python** (essential for real-world ML applications).
218 |
219 | #### Probability and Statistics
220 | - 🎥 [Statistics Crash Course - Adriene Hill](https://www.youtube.com/playlist?list=PL8dPuuaLjXtNM_Y-bUAhblSAdWRnmBUcr)
221 | **Easiest explanations** for complex probability & stats concepts.
222 | - 📚 [Learn Statistics with Python - Ethan Weed](https://ethanweed.github.io/pythonbook/landingpage.html)
223 | **Hands-on Python exercises** for better understanding.
224 |
225 | ---
226 |
227 | ### 2.3 Supervised Learning
228 | Supervised learning is the foundation of ML. Most real-world applications involve some form of **classification** or **regression** problems.
229 |
230 | 💡 **Your goal?** Master these fundamental algorithms before moving to more complex techniques.
231 |
232 | #### Linear Regression
233 | ##### Intro theory:
234 | - 🎥 [Nando de Freitas UBC, Lecture 1](https://www.youtube.com/watch?v=fd6kQQEbq2Q&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6&index=3)
235 | - 🎥 [Nando de Freitas UBC, Lecture 2](https://www.youtube.com/watch?v=voN8omBe2r4&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6&index=4)
236 | ##### Python Implementation
237 | - 📚 [Linear Regression Closed-form](https://aunnnn.github.io/ml-tutorial/html/blog_content/linear_regression/linear_regression_tutorial.html)
238 | - 📚 [Linear Regression Gradient Descent](https://dmitrijskass.netlify.app/2021/04/03/gradient-descent-with-linear-regression-from-scratch/)
239 | ##### Regularization
240 | - 🎥 [Nando de Freitas UBC, Lecture 1](https://www.youtube.com/watch?v=hrIad1RVFV0&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6&index=5)
241 | - 🎥 [Nando de Freitas UBC, Lecture 2](https://www.youtube.com/watch?v=PvuN23m7hhY&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6&index=6)
242 | - 📊 [Visual explanation with code 1](https://aunnnn.github.io/ml-tutorial/html/blog_content/linear_regression/linear_regression_regularized.html#sphx-glr-download-blog-content-linear-regression-linear-regression-regularized-py)
243 | - 📊 [Visual explanation with code 2](https://mmuratarat.github.io/2019-09-01/regularized-linear-models)
244 | - 📚 [Sklearn tutorial with Lasso model](https://www.kirenz.com/blog/posts/2019-08-12-python-lasso-regression-auto/#lasso-regression-in-python)
245 |
246 | #### Logistic Regression
247 | - 📚 [MLCourse.ai - Logistic Regression](https://mlcourse.ai/book/topic05/topic05_intro.html)
248 | - 🔎 [Odds Ratio & Weights Interpretation](https://mmuratarat.github.io/2019-09-05/odds-ratio-logistic-regression)
249 |
250 | #### Gradient Boosting
251 | ##### Introduction
252 | - 📚 [MLCourse.ai - Gradient Boosting](https://mlcourse.ai/book/topic10/topic10_gradient_boosting.html)
253 | ##### Gradient Boosting, deeper dive
254 | - 📚 [XGBoost Paper](https://arxiv.org/pdf/1603.02754.pdf)
255 | - 📚 [Tutorial by Alexey Natekin](https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2013.00021/full)
256 | ##### Demo playground
257 | - 🎥 [Gradient Boosting Interactive Playground 1](https://arogozhnikov.github.io/2016/06/24/gradient_boosting_explained.html)
258 | - 🎥 [Gradient Boosting Interactive Playground 2](https://arogozhnikov.github.io/2016/07/05/gradient_boosting_playground.html)
259 |
260 | #### Random Forest
261 | ##### Intro lectures
262 | - 🎥 [Nando de Freitas UBC, Lecture 1](https://www.youtube.com/watch?v=-dCtJjlEEgM&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6&index=12)
263 | - 🎥 [Nando de Freitas UBC, Lecture 2](https://www.youtube.com/watch?v=3kYujfDgmNk&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6&index=13)
264 | - 🎥 [Nando de Freitas UBC, Lecture 3](https://www.youtube.com/watch?v=zFGPjRPwyFw&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6&index=14)
265 | ##### Bagging and Random Forest, Deeper Dive
266 | - 📚 [MLCourse.ai - Bagging & Random Forest](https://mlcourse.ai/book/topic05/topic05_intro.html)
267 |
268 | #### k-Nearest Neighbors (k-NN)
269 | - 📚 [Understanding k-NN](https://mmuratarat.github.io/2019-07-12/k-nn-from-scratch)
270 |
271 | ---
272 |
273 | ### 2.4 Unsupervised Learning
274 | Unsupervised learning helps **discover hidden structures** in data when labels are **not available.**
275 |
276 | #### Clustering
277 | - 📚 [k-Means Clustering](https://mmuratarat.github.io/2019-07-23/kmeans_from_scratch)
278 | - 📚 [DBScan Clustering](https://github.com/christianversloot/machine-learning-articles/blob/main/performing-dbscan-clustering-with-python-and-scikit-learn.md)
279 |
280 | #### Dimensionality Reduction
281 | - 📚 [PCA - Step-by-step Guide](https://sebastianraschka.com/Articles/2014_pca_step_by_step.html)
282 | **Fundamental technique for reducing data dimensions.**
283 | - 🎥 [t-SNE, Resource 1](https://www.analyticsvidhya.com/blog/2017/01/t-sne-implementation-r-python/)
284 | - 🎥 [t-SNE, Resource 2](https://distill.pub/2016/misread-tsne/)
285 | - 📚 [UMAP - Understanding & Applications](https://pair-code.github.io/understanding-umap/)
286 | ---
287 |
288 |
420 |
421 | ---
422 | ## 4. MLOps for Data Scientists
423 |
424 | ### 🙏 Please, support the repo with a STAR ⭐
425 | To help people learn ML Foundations for free, please, support this project with a GitHub star ⭐, so more people can learn ML for free.
426 |
427 | ---
428 |
429 | MLOps (Machine Learning Operations) is **essential** for deploying, managing, and scaling ML models in production. Many Data Scientists debate whether they need MLOps skills, but having a **solid understanding** will make you a stronger professional who can build and deploy end-to-end solutions.
430 |
431 | ---
432 |
433 | ### 4.1 Introduction
434 | Alexey Grigoriev and his team have created an **excellent MLOps course** covering key concepts. Another great resource is **Neptune AI's blog**, which provides **practical guides** on MLOps topics.
435 |
436 | - 🎓 [MLOps Zoomcamp - DataTalksClub](https://github.com/DataTalksClub/mlops-zoomcamp/tree/main/01-intro)
437 |
438 | ---
439 |
440 | ### 4.2 Model Registry and Experiment Tracking
441 | Model registry and experiment tracking are **critical** for managing models effectively, especially in a team setting.
442 |
443 | - 📚 [Model Registry - Neptune AI](https://neptune.ai/blog/ml-model-registry)
444 | - 📚 [Experiment Tracking - Neptune AI](https://neptune.ai/blog/ml-experiment-tracking)
445 | - 🛠️ [Hands-on Example - DataTalksClub](https://github.com/DataTalksClub/mlops-zoomcamp/tree/main/02-experiment-tracking)
446 |
447 | ---
448 |
449 | ### 4.3 ML Pipelines
450 | Well-structured **ML pipelines** streamline the model development and deployment process. Avoid hardcoded workflows—use proper pipeline tools!
451 |
452 | - 📚 [Building End-to-End ML Pipelines - Neptune AI](https://neptune.ai/blog/building-end-to-end-ml-pipeline)
453 | - 📚 [Best ML Workflow and Pipeline Orchestration Tools - Neptune AI](https://neptune.ai/blog/best-workflow-and-pipeline-orchestration-tools)
454 | - 🛠️ [ML Pipelines with Mage/Prefect - DataTalksClub](https://github.com/DataTalksClub/mlops-zoomcamp/tree/main/03-orchestration)
455 |
456 | 💡 **Your goal?** Try implementing a small pipeline **from scratch** using one of the recommended tools.
457 |
458 | ---
459 |
460 | ### 4.4 Model Monitoring
461 | Monitoring models **post-deployment** is **crucial** to ensure their performance **doesn’t degrade** over time.
462 |
463 | - 📚 [MLOps Monitoring Guides - Evidently AI](https://www.evidentlyai.com/mlops-guides)
464 | - 🎓 [MLOps Zoomcamp - Model Monitoring](https://github.com/DataTalksClub/mlops-zoomcamp/tree/main/05-monitoring)
465 |
466 | ---
467 |
468 | ### 4.5 Docker Basics
469 | Docker allows you to **containerize** ML models for **consistent deployment across different environments**. Though intimidating at first, it’s **a must-know tool** for any Data Scientist.
470 |
471 | - 🎥 [Docker Crash Course - Nana](https://www.youtube.com/watch?v=3c-iBn73dDE)
472 |
473 | ---
474 |
475 | ### 4.6 Additional Resources
476 | If you want to **go deeper into MLOps**, check out this roadmap. But **be cautious**—MLOps is vast, so focus on the fundamentals first!
477 |
478 | - 📚 [MLOps Roadmap 2024 - Marvelous MLOps](https://marvelousmlops.substack.com/p/mlops-roadmap-2024)
479 |
480 | ---
481 |
31 |
32 | 
](https://www.maistermind.ai/join)
37 |
38 | 
41 |
42 |