├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2025 erencice 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 🚀 AI & Data Science Roadmap 2 | 3 | Welcome to the **AI & Data Science Roadmap**! This guide offers a structured, step-by-step learning path designed to build your knowledge progressively — starting from foundational mathematics and statistics, moving through programming and data manipulation, and advancing toward sophisticated machine learning, deep learning, and production-level AI systems. 4 | 5 | Each section includes carefully selected must-have and nice-to-have resources such as courses, books, articles, and practical projects to ensure both theoretical understanding and hands-on experience. 6 | 7 | Follow this roadmap to learn effectively, connect concepts across disciplines, and develop real-world skills that will empower your journey in AI, data science, and beyond. Let’s dive in! 📘📊🤖 8 | 9 | --- 10 | 11 | ## 🧭 For Those Who Prefer a Practical Approach 12 | 13 | For those who want to dive quickly into practice and consolidate the theoretical foundational concepts later, this is an ideal starting point. This pathway allows you to jump straight into real-world projects and gain hands-on experience using data science tools. If you prefer, you can skip this step and start directly from the theoretical and basic math/statistics sections. 14 | 15 | * [Google Advanced Data Analytics Certificate (Coursera)](https://www.coursera.org/professional-certificates/google-advanced-data-analytics) - Focuses on hands-on analytics, data tools, and practical workflows. 16 | 17 | --- 18 | 19 | ## 1. 📐 Mathematics for Machine Learning 20 | 21 | ### Learning Sequence and Resources 22 | 23 | 1. **Basic Concepts and Quick Introduction** 24 | 25 | * [Mathematics for Machine Learning (YouTube)](https://www.youtube.com/watch?v=LwCRRUa8yTU) – Visual and intuitive introduction to fundamental math concepts. 26 | * [Expressway to Data Science: Essential Math (Coursera)](https://www.coursera.org/specializations/expressway-to-data-science-essential-math) – Core math topics tailored for data science. 27 | 28 | 2. **In-Depth Mathematics Training** 29 | 30 | * [Mathematics for Machine Learning Specialization (Coursera)](https://www.coursera.org/specializations/mathematics-machine-learning) – Comprehensive courses on linear algebra, calculus, probability, and statistics. 31 | 32 | 3. **Detailed Reference and Support Resource** 33 | 34 | * [Everything You Always Wanted to Know About Mathematics (PDF)](https://www.math.cmu.edu/~jmackey/151_128/bws_book.pdf) – Use as a detailed reference for clarifying concepts when needed. 35 | 36 | ### Resource Priority 37 | 38 | | Resource | Priority | 39 | | :----------------------------------------------- | :---------- | 40 | | Mathematics for Machine Learning (YouTube) | Must-have | 41 | | Expressway to Data Science: Essential Math | Must-have | 42 | | Mathematics for Machine Learning Specialization | Must-have | 43 | | Everything You Always Wanted to Know About Mathematics (PDF) | Nice-to-have | 44 | 45 | ### Brief Notes and Recommendations 46 | 47 | * The first two resources are great for quickly and effectively grasping fundamental concepts. 48 | * The Coursera Mathematics for Machine Learning Specialization is ideal for solidifying your math foundation, especially if you need deeper understanding. 49 | * The PDF resource is useful as a reference for deep dives but not recommended as a primary starting point. 50 | * Completing this math foundation will make progressing to machine learning and data science much easier and daha efficient. 51 | 52 | --- 53 | 54 | ## 2. 📊 Statistics 55 | 56 | ### Learning Sequence and Resources 57 | 58 | 1. **Fundamental Statistics Concepts and Theory Introduction** 59 | 60 | * [Stanford Statistics (Coursera)](https://www.coursera.org/learn/stanford-statistics) – Comprehensive and solid foundation course in statistics. 61 | * [Probability & Statistics (Coursera)](https://www.coursera.org/learn/probability-statistics) – Basics of probability and statistics combining theory and practice. 62 | 63 | 2. **Applied Statistics and Hypothesis Testing** 64 | 65 | * [Statistical Analysis & Hypothesis Testing with SAS](https://www.coursera.org/learn/statistical-analysis-hypothesis-testing-sas) – Performing statistical tests and experiment design using SAS. 66 | * [A/B Testing Guide (Medium)](https://vkteam.medium.com/practitioners-guide-to-statistical-tests-ed2d580ef04f#1e3b) – Practical explanation of statistical tests in A/B testing. 67 | * [Planning A/B Tests Step-by-Step](https://towardsdatascience.com/step-by-step-for-planning-an-a-b-test-ef3c93143c0b) – Stepwise guide for designing A/B tests properly. 68 | 69 | 3. **Statistics with Python and Advanced Topics** 70 | 71 | * [Think Stats (PDF)](https://greenteapress.com/thinkstats/thinkstats.pdf) – Introduction to statistics using Python. 72 | * [Basics of Statistics – SOGA-PY (FU Berlin)](https://www.geo.fu-berlin.de/en/v/soga-py/Basics-of-statistics/index.html) – Beginner-level online tutorials with Python applications. 73 | * [Advanced Statistics – SOGA-PY (FU Berlin)](https://www.geo.fu-berlin.de/en/v/soga-py/Advanced-statistics/index.html) – Intermediate to advanced statistics concepts. 74 | 75 | ### Resource Priority 76 | 77 | | Resource | Priority | 78 | | :----------------------------------------------- | :---------- | 79 | | Stanford Statistics (Coursera) | Must-have | 80 | | Probability & Statistics (Coursera) | Must-have | 81 | | Statistical Analysis & Hypothesis Testing with SAS | Must-have | 82 | | A/B Testing Guide (Medium) | Must-have | 83 | | Planning A/B Tests Step-by-Step | Must-have | 84 | | Think Stats (PDF) | Nice-to-have | 85 | | Basics of Statistics – SOGA-PY (FU Berlin) | Nice-to-have | 86 | | Advanced Statistics – SOGA-PY (FU Berlin) | Nice-to-have | 87 | 88 | ### Brief Notes and Recommendations 89 | 90 | * Taking both the Stanford and Probability & Statistics courses together is effective to build a solid understanding of statistics and probability fundamentals. 91 | * The SAS course is valuable for gaining practical skills in statistical analysis and hypothesis testing. 92 | * A/B testing guides provide practical context for experiment design and interpreting results. 93 | * Think Stats is a good intro for Python users, best started after grasping fundamentals. 94 | * SOGA-PY resources are well-prepared and supportive for those aiming to learn intermediate and advanced topics. 95 | 96 | --- 97 | 98 | ## 3. 📖 Econometrics 99 | 100 | ### Learning Sequence and Resources 101 | 102 | 1. **Foundations and Theoretical Background** 103 | 104 | * [Econometric Theorems (Book)](https://bookdown.org/ts_robinson1994/10EconometricTheorems/) – Core theoretical principles behind econometric models. 105 | * [Gujarati’s Basic Econometrics (PDF)](https://www.cbpbu.ac.in/userfiles/file/2020/STUDY_MAT/ECO/1.pdf) – Classic, widely used textbook on regression and inference. 106 | 107 | 2. **Practical Econometrics and Time Series** 108 | 109 | * [Regressions, Time Series, Fitting Distributions (Coursera)](https://www.coursera.org/learn/erasmus-econometrics) – Practical course covering regressions and time series methods. 110 | * [Forecasting Principles & Practice with R](https://otexts.com/fpp3/) – Modern forecasting techniques in R. 111 | * [Forecasting: Principles and Practice, the Pythonic Way (E-Book)](https://otexts.com/fpppy/) – Python equivalent for time series forecasting. 112 | 113 | 3. **Applied Time Series & Advanced Topics** 114 | 115 | * [ARIMA for Time Series Forecasting](https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/) – Tutorial for ARIMA modeling with Python. 116 | * [Kaggle: Time Series Basics](https://www.kaggle.com/learn/time-series) – Hands-on projects for time series analysis. 117 | * [Advanced Econometrics (Econometrics II) (E-Book)](https://vladislav-morozov.github.io/econometrics-2/) – Covers advanced estimation and inference methods. 118 | * [Econometrics with Unobserved Heterogeneity (E-Book)](https://vladislav-morozov.github.io/econometrics-heterogeneity/) – Specialized panel data techniques with heterogeneity. 119 | 120 | 4. **Supporting Tools and Notes** 121 | 122 | * [Using R for Introductory Econometrics (Website)](https://pyoflife.com/using-r-for-introductory-econometrics/) – Guide for implementing econometrics in R. 123 | * [Econometrics Notes by F. Diebold (PDF)](https://www.sas.upenn.edu/~fdiebold/Teaching104/Econometrics.pdf) – Comprehensive lecture notes from a leading econometrics course. 124 | 125 | ### Resource Priority 126 | 127 | | Resource | Priority | 128 | | :----------------------------------------------- | :---------- | 129 | | Econometric Theorems (Book) | Must-have | 130 | | Gujarati’s Basic Econometrics (PDF) | Must-have | 131 | | Regressions, Time Series, Fitting Distributions (Coursera) | Must-have | 132 | | Forecasting Principles & Practice with R | Must-have | 133 | | ARIMA for Time Series Forecasting | Must-have | 134 | | Kaggle: Time Series Basics | Nice-to-have | 135 | | Forecasting: Principles and Practice, the Pythonic Way | Nice-to-have | 136 | | Advanced Econometrics (Econometrics II) | Nice-to-have | 137 | | Econometrics with Unobserved Heterogeneity | Nice-to-have | 138 | | Using R for Introductory Econometrics | Nice-to-have | 139 | | Econometrics Notes by F. Diebold | Nice-to-have | 140 | 141 | ### Brief Notes and Recommendations 142 | 143 | * Start with foundational theoretical texts (Econometric Theorems, Gujarati) to build strong conceptual understanding. 144 | * Note that Gujarati’s Basic Econometrics and other textbooks are comprehensive and long-term reads; it is perfectly fine to study them gradually over time while concurrently progressing through other practical and course-based resources. 145 | * The Coursera course is ideal to bridge theory with practical regression and time series modeling. 146 | * Forecasting with R is a key practical skill; complement it with ARIMA tutorials and Kaggle projects for hands-on experience. 147 | * Advanced and specialized topics can be tackled after mastering the basics and practice workflows. 148 | * Supplement your learning with R-focused guides and lecture notes for implementation details and broader perspective. 149 | 150 | --- 151 | 152 | ## 4. 🐍📊 Programming Languages & Data Tools 153 | 154 | ### Learning Sequence and Resources 155 | 156 | 1. **Python Basics and Fundamentals** 157 | 158 | * [Python Crash Course (YouTube)](https://www.youtube.com/watch?v=rfscVS0vtbw) – Beginner-friendly Python tutorial. 159 | * [Python Introduction Notes by Kevin Sheppard (PDF)](https://www.kevinsheppard.com/files/teaching/python/notes/python_introduction_2021.pdf) – Well-structured starter notes for Python learners. 160 | * [Introduction to Python – SOGA-PY (FU Berlin)](https://www.geo.fu-berlin.de/en/v/soga-py/Introduction-to-Python/index.html) – Interactive lessons to reinforce basics. 161 | 162 | 2. **Applied Python for Data Science** 163 | 164 | * [Python for Applied Data Science (Coursera)](https://www.coursera.org/learn/python-for-applied-data-science-ai) – Learn Python through real data science projects. 165 | * [Python for Data Analysis by Wes McKinney (Website)](https://wesmckinney.com/book/) – Key reference for data manipulation with Pandas. 166 | * [Statsmodels](https://www.statsmodels.org/dev/stats.html) - Statistical modeling and econometrics in Python. 167 | 168 | 3. **Algorithmic Thinking and Data Structures** 169 | 170 | * [Data Structures & Algorithms (Coursera)](https://www.coursera.org/specializations/algorithms) – Build algorithmic problem-solving skills. 171 | * [LeetCode Study Plan](https://leetcode.com/studyplan/) – Structured practice for algorithms and coding challenges. 172 | * [LeetCode Explore](https://leetcode.com/explore/learn/) – Interactive coding challenges on DSA topics. 173 | 174 | 4. **R Programming for Data Analysis & Visualization** 175 | 176 | * [R for Data Science (2e) (Website)](https://r4ds.hadley.nz) – Learn tidyverse-based R programming for data science. 177 | * [Efficient R Programming](https://csgillespie.github.io/efficientR/) – Techniques for writing efficient R code. 178 | * [R Graphics Cookbook](https://r-graphics.org) – Practical guide to creating various plots in R. 179 | * [Tidy Text Mining](https://www.tidytextmining.com) – Learn text mining using tidy principles in R. 180 | * [Applied Generalized Linear Models and Multilevel Models in R](https://bookdown.org/roback/bookdown-BeyondMLR/) – Covers advanced statistical modeling in R. 181 | 182 | ### Resource Priority 183 | 184 | | Resource | Priority | 185 | | :----------------------------------------------- | :---------- | 186 | | Python Crash Course (YouTube) | Must-have | 187 | | Python for Applied Data Science (Coursera) | Must-have | 188 | | Data Structures & Algorithms (Coursera) | Must-have | 189 | | Python Introduction Notes by Kevin Sheppard (PDF) | Must-have | 190 | | Python for Data Analysis by Wes McKinney | Must-have | 191 | | Statsmodels | Must-have | 192 | | R for Data Science (2e) | Must-have | 193 | | Efficient R Programming | Must-have | 194 | | R Graphics Cookbook | Nice-to-have | 195 | | Tidy Text Mining | Nice-to-have | 196 | | Applied Generalized Linear Models and Multilevel Models in R | Nice-to-have | 197 | | Introduction to Python – SOGA-PY (FU Berlin) | Nice-to-have | 198 | | LeetCode Study Plan | Nice-to-have | 199 | | LeetCode Explore | Nice-to-have | 200 | 201 | ### Brief Notes and Recommendations 202 | 203 | * Start with Python basics using video tutorials and structured notes for a smooth introduction. 204 | * Parallelly build applied skills with data science projects and Pandas-focused references. 205 | * `Statsmodels` is essential for advanced statistical modeling with Python. 206 | * Strengthen problem-solving by learning algorithms and data structures, practicing regularly on platforms like LeetCode. 207 | * For R, focus on `R for Data Science` to grasp data manipulation and visualization, and use `Efficient R Programming` for better coding practices. 208 | * Interactive lessons and additional notes help reinforce understanding but can be used flexibly according to your pace. 209 | * Combining both Python and R skills offers versatility in data science roles. 210 | 211 | --- 212 | 213 | ## 5. 🔍 Exploratory Data Analysis (EDA) 214 | 215 | ### Learning Sequence and Resources 216 | 217 | 1. **Foundations of EDA** 218 | 219 | * [EDA with Python & Pandas (Coursera)](https://www.coursera.org/projects/exploratory-data-analysis-python-pandas) – Learn data summarization and visualization basics. 220 | * [IBM: EDA for Machine Learning](https://www.coursera.org/learn/ibm-exploratory-data-analysis-for-machine-learning) – EDA concepts tailored for ML workflows. 221 | 222 | 2. **Advanced Visualization Techniques** 223 | 224 | * [EDA with Seaborn (Coursera)](https://www.coursera.org/projects/exploratory-data-analysis-seaborn) – Deep dive into visual data exploration with Seaborn. 225 | 226 | 3. **Tools and Automation** 227 | 228 | * [Dataprep.ai (Website)](https://dataprep.ai) – Automate and simplify data cleaning and EDA processes. 229 | 230 | ### Resource Priority 231 | 232 | | Resource | Priority | 233 | | :------------------------------- | :---------- | 234 | | EDA with Python & Pandas (Coursera) | Must-have | 235 | | IBM: EDA for Machine Learning | Must-have | 236 | | EDA with Seaborn (Coursera) | Nice-to-have | 237 | | Dataprep.ai (Website) | Nice-to-have | 238 | 239 | ### Brief Notes and Recommendations 240 | 241 | * Begin with basic EDA principles using Pandas and Python to understand data distribution and patterns. 242 | * Follow up with ML-focused EDA to contextualize analysis in predictive modeling. 243 | * Advanced visualizations with Seaborn help uncover deeper insights, while tools like Dataprep.ai speed up routine data prep tasks. 244 | * Combining manual and automated approaches ensures a thorough and efficient EDA workflow. 245 | 246 | --- 247 | 248 | ## 6. 🧮 SQL Introduction 249 | 250 | ### Learning Sequence and Resources 251 | 252 | 1. **SQL Fundamentals** 253 | 254 | * [SQL Tutorial](https://www.sqltutorial.org/) – Comprehensive step-by-step tutorials suitable for all levels. 255 | * [SQL Roadmap (roadmap.sh)](https://roadmap.sh/sql) – Visual roadmap guiding the topic progression. 256 | 257 | ### Resource Priority 258 | 259 | | Resource | Priority | 260 | | :----------------------- | :---------- | 261 | | SQL Tutorial | Must-have | 262 | | SQL Roadmap (roadmap.sh) | Nice-to-have | 263 | 264 | ### Brief Notes and Recommendations 265 | 266 | * Start with the SQL Tutorial to build a strong foundation in querying, filtering, joining, and manipulating data. 267 | * Use the roadmap for a visual overview of concepts and to plan learning progression. 268 | * SQL skills are essential for data retrieval from databases, so prioritize mastering the basics before moving on to integration with other tools. 269 | 270 | --- 271 | 272 | ## 7. 🌐 Web Scraping with Python 273 | 274 | ### Learning Sequence and Resources 275 | 276 | 1. **Core Web Scraping Tools** 277 | 278 | * [Selenium Documentation](https://selenium-python.readthedocs.io/index.html) – Automate browsers, useful for dynamic sites. 279 | * [Beautiful Soup Docs](https://tedboy.github.io/bs4_doc/index.html) – Parse HTML and extract information easily. 280 | 281 | 2. **Hands-On Practice** 282 | 283 | * [Practice Web Scraping](https://www.scrapingcourse.com/ecommerce/) – Practical exercises scraping mock e-commerce websites. 284 | 285 | ### Resource Priority 286 | 287 | | Resource | Priority | 288 | | :----------------------- | :---------- | 289 | | Selenium Documentation | Must-have | 290 | | Beautiful Soup Docs | Must-have | 291 | | Practice Web Scraping | Nice-to-have | 292 | 293 | ### Brief Notes and Recommendations 294 | 295 | * Learn both Beautiful Soup and Selenium as they complement each other: BS for static pages and Selenium for dynamic content. 296 | * Hands-on practice is crucial—try the practice course to consolidate your skills on real-like projects. 297 | * Web scraping is valuable for gathering raw data that isn't easily accessible via APIs or databases. 298 | 299 | --- 300 | 301 | ## 8. 🎓 Advanced Theoretical ML 302 | 303 | ### Learning Sequence and Resources 304 | 305 | 1. **Introductory Theory and Classic Texts** 306 | 307 | * [mlcourse.ai](https://mlcourse.ai/book/index.html) – Theory-rich book/course combining fundamentals and practice. 308 | * [Introduction to Statistical Learning (Book)](https://www.statlearning.com/) – Classic text on ML theory using R, great for foundational concepts. 309 | * [Intro to ML Specialization (Coursera)](https://www.coursera.org/specializations/machine-learning-introduction) – Balanced theory and application. 310 | * [Machine Learning – SOGA-PY (FU Berlin)](https://www.geo.fu-berlin.de/en/v/soga-py/Machine-learning/index.html) – Blends theoretical concepts with Python practice. 311 | 312 | 2. **Deep Theoretical Foundations** 313 | 314 | * [Pattern Recognition and Machine Learning – Bishop (PDF)](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf) – Core probabilistic ML textbook, advanced level. 315 | * [Think Complexity (2nd Edition) – Allen B. Downey (PDF)](https://greenteapress.com/wp/think-complexity/) – Covers computational and complexity models in Python. 316 | * [ML Refined (Website)](https://www.mlrefined.com/) – Elegant theoretical explanations for many ML algorithms. 317 | 318 | 3. **Practical Libraries** 319 | 320 | * [Scikit-learn Official Documentation](https://scikit-learn.org/stable/index.html) – Essential for implementing classical ML algorithms in Python. 321 | 322 | ### Resource Priority 323 | 324 | | Resource | Priority | 325 | | :----------------------------------------------- | :---------- | 326 | | mlcourse.ai | Must-have | 327 | | Introduction to Statistical Learning | Must-have | 328 | | Pattern Recognition and Machine Learning | Must-have | 329 | | Scikit-learn Official Documentation | Must-have | 330 | | Intro to ML Specialization (Coursera) | Nice-to-have | 331 | | Machine Learning – SOGA-PY (FU Berlin) | Nice-to-have | 332 | | Think Complexity | Nice-to-have | 333 | | ML Refined | Nice-to-have | 334 | 335 | ### Brief Notes and Recommendations 336 | 337 | * Begin with mlcourse.ai and ISL for a solid theoretical base combined with practical examples. 338 | * Progress to Bishop’s PRML book for deep theoretical understanding; this can be a long-term study alongside other topics. 339 | * Use scikit-learn docs to connect theory to practice by implementing algorithms. 340 | * Supplement learning with online courses and explanatory websites for diverse perspectives and reinforcement. 341 | 342 | --- 343 | 344 | ## 9. ☁️ Big Data 345 | 346 | ### Learning Sequence and Resources 347 | 348 | 1. **Big Data Fundamentals and Tools** 349 | 350 | * [Big Data Specialization (Coursera)](https://www.coursera.org/specializations/big-data) – Comprehensive specialization covering Hadoop, Spark, and distributed data processing. 351 | 352 | ### Resource Priority 353 | 354 | | Resource | Priority | 355 | | :------------------------ | :---------- | 356 | | Big Data Specialization | Must-have | 357 | 358 | ### Brief Notes and Recommendations 359 | 360 | * This specialization is essential to understand large-scale data processing and distributed computing frameworks. 361 | * Recommended after building a solid foundation in Python, SQL, and data handling concepts. 362 | * Useful for roles involving data engineering or handling massive datasets. 363 | 364 | --- 365 | 366 | ## 10. 🔬 Deep Learning 367 | 368 | ### Learning Sequence and Resources 369 | 370 | 1. **Core Deep Learning Courses and Books** 371 | 372 | * [Deep Learning Specialization (Coursera)](https://www.coursera.org/specializations/deep-learning) – Andrew Ng’s in-depth course series covering foundational DL concepts and architectures. 373 | * [Dive into Deep Learning](https://d2l.ai) – Hands-on, interactive resource with code examples in Python. 374 | * [Illustrated Transformers (Jalammar)](https://jalammar.github.io/illustrated-transformer/) – Visual and intuitive explanation of transformer models. 375 | 376 | 2. **Advanced and Supplementary Materials** 377 | 378 | * [Deep Learning Book (Goodfellow et al.)](https://www.deeplearningbook.org/) – Authoritative textbook, more theoretical and advanced. 379 | * [Applied ML Practices (GitHub)](https://github.com/eugeneyan/applied-ml) – Real-world applications and pipelines for ML and DL. 380 | 381 | ### Resource Priority 382 | 383 | | Resource | Priority | 384 | | :----------------------------------- | :---------- | 385 | | Deep Learning Specialization | Must-have | 386 | | Dive into Deep Learning | Must-have | 387 | | Illustrated Transformers | Must-have | 388 | | Deep Learning Book (Goodfellow et al.) | Nice-to-have | 389 | | Applied ML Practices (GitHub) | Nice-to-have | 390 | 391 | ### Brief Notes and Recommendations 392 | 393 | * Start with Andrew Ng’s Deep Learning Specialization for a clear and structured introduction. 394 | * Complement with “Dive into Deep Learning” for practical coding and interactive learning. 395 | * The Illustrated Transformers article is great for understanding state-of-the-art transformer architectures. 396 | * The Goodfellow book is highly recommended for deeper theoretical insights but can be studied alongside or after completing the specialization. 397 | * Use Applied ML Practices to see how deep learning fits into real-world workflows and pipelines. 398 | 399 | --- 400 | 401 | ## 11. ⚙️ MLOps (Machine Learning Operations) 402 | 403 | ### Learning Sequence and Resources 404 | 405 | 1. **Foundations and Practical MLOps** 406 | 407 | * [MLOps Zoomcamp (GitHub)](https://github.com/DataTalksClub/mlops-zoomcamp) – Hands-on MLOps practical training. 408 | * [MLOps for ML Engineering (Coursera)](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops) – Covers CI/CD, pipelines, deployment best practices. 409 | 410 | 2. **Advanced Concepts and Architecture** 411 | 412 | * [Machine Learning Systems (Book)](https://mlsysbook.ai) – In-depth guide to designing scalable ML pipelines and systems. 413 | 414 | ### Resource Priority 415 | 416 | | Resource | Priority | 417 | | :---------------------------- | :---------- | 418 | | MLOps Zoomcamp | Must-have | 419 | | MLOps for ML Engineering | Must-have | 420 | | Machine Learning Systems (Book) | Nice-to-have | 421 | 422 | ### Brief Notes and Recommendations 423 | 424 | * Essential for transitioning ML models from experiments to production systems. 425 | * Start with practical courses to grasp pipeline and deployment fundamentals. 426 | * The book is great for architectural depth and long-term reference. 427 | 428 | --- 429 | 430 | ## 12. 🛠️ Data Engineering 431 | 432 | ### Learning Sequence and Resources 433 | 434 | 1. **Core Data Engineering Skills** 435 | 436 | * [Data Engineering Zoomcamp (GitHub)](https://github.com/DataTalksClub/data-engineering-zoomcamp) – Learn tools like Airflow, Kafka, Spark, and data pipeline construction. 437 | 438 | ### Resource Priority 439 | 440 | | Resource | Priority | 441 | | :------------------------ | :---------- | 442 | | Data Engineering Zoomcamp | Must-have | 443 | 444 | ### Brief Notes and Recommendations 445 | 446 | * Crucial for managing data flow, transformations, and storage in scalable systems. 447 | * Best taken after foundational knowledge of Python, SQL, and Big Data concepts. 448 | 449 | --- 450 | 451 | ## 13. 🧠 Large Language Models (LLMs) & Open-Source AI (Optional) 452 | 453 | ### Learning Sequence and Resources 454 | 455 | 1. **Transformer Models and LLMs** 456 | 457 | * [Hugging Face Course](https://huggingface.co/course/chapter1) – Hands-on training on transformers and language models. 458 | * [Hugging Face AI Agents Course](https://huggingface.co/learn/agents-course/unit0/introduction) – Building autonomous AI agents using open-source tools. 459 | 460 | ### Resource Priority 461 | 462 | | Resource | Priority | 463 | | :---------------------------- | :---------- | 464 | | Hugging Face Course | Must-have | 465 | | Hugging Face AI Agents Course | Nice-to-have | 466 | 467 | ### Brief Notes and Recommendations 468 | 469 | * Ideal for users interested in cutting-edge NLP and AI agent development. 470 | * Can be explored after mastering core ML and deep learning topics. 471 | * Optional for general ML/data science learners, but highly valuable for NLP-focused roles. 472 | 473 | --- 474 | 475 | ## 📚 Additional Resources 476 | 477 | > Bonus reads and repositories for deep learners. 478 | 479 | * [AI Engineering Reading List (Latent.Space)](https://www.latent.space/p/2025-papers) – Must-read AI papers for 2025. 480 | * [Think Like a Data Scientist (SSRN Paper)](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3475303) – Understand the mindset of a successful data scientist. 481 | * [Think Like a Data Scientist (PDF)](https://lmsspada.kemdiktisaintek.go.id/pluginfile.php/752025/mod_resource/content/2/Think%20Like%20a%20Data%20Scientist.pdf) – Full-text version of the data science thought process. 482 | * [Python for Algorithmic Trading Cookbook (GitHub)](https://github.com/PacktPublishing/Python-for-Algorithmic-Trading-Cookbook) – Code recipes for finance-focused data science with Python. 483 | * [AI for Medicine Specialization (Coursera)](https://www.coursera.org/specializations/ai-for-medicine) – Real-world AI applications in the medical field. 484 | * [Statistical Modeling, Causal Inference, and Social Science](https://online.stat.psu.edu/stat462/node/77/) – Course materials covering statistical modeling and causal inference. 485 | * [Computer Vision Roadmap 2024 (PClub)](https://pclub.in/roadmap/2024/08/17/cv-roadmap/) – A detailed guide to learning computer vision from basics to advanced research. 486 | 487 | --- 488 | 489 | **🎓 Enjoy the journey! Learn with curiosity. Build with purpose.** 🚀 490 | --------------------------------------------------------------------------------