Hari Thapliyaal

Hari Thapliyal is a vivid blogger on various subjects including Data Science, Project Management, and Advait-Vedanta Philosophy. He has Master’s degrees in Computers, Business Management, Data Science, and Economics. He is pursuing his Doctorate in AI (NLP) from the Swiss School of Business Management (Geneva, Switzerland). Hari has around 3 decades of experience in management & leadership, training, consulting, and coaching in technology. 16+ years in all phases of software product development. 10+ years in course design, training, coaching, and consulting on Project Management. 3+ years experience in course development, exploring, experimenting, training, coaching, and mentoring hundreds of professionals in Data Science, AI, CV, NLP, complex machine learning algorithms, statistical modeling, identifying patterns, and extracting valuable insights. He has diverse experience in planning & executing multiple projects and liaising with key stakeholders to identify & resolve the business problem and deliver excellent results. In his spare time, he loves to Meditate in some secluded place or in nature.

Data Science, AI, ML, eBooks, PDF Books

DS, AI, ML, Books Available

All these books are available in pdf format at this link. I update this page less frequently but keep adding books in the repo. The list below may not contain the name you are looking for. Therefore, it is suggested to visit the link mentioned earlier. This link contains excellent presentations Book, (PPT), Handbook, Report, Articles (ARTC), Book, Booklet, eBook, Notebook, Notes, PAPER, GUIDE, TOC, Syllabus, LINKS, Handbook, Chapter, Tool, BROC – Broacher on Machine Learning, Deep Learning, NLP, Statistics, Reinforcement Learning, GAN. 560+ pdf files, 400+ books.

If you want to search all the books on above link then in the search box at above link you can enter “Book”. Similarly if you looking for research paper you can type Paper”. This repository also has some important AI/ML research paper.

  1. 10 Keys to AI Success in 2021-by-DataRobot-PPT.pdf
  2. 10 Useful Way To Visualize Your Data-GUIDE.pdf
  3. 100 Numpy Exercises-NOTEBOOK.pdf
  4. 100 Pandas Exercises-NOTEBOOK.pdf
  5. 13 Data Science Questions for DS Project-PPT.pdf
  6. 19 Slides on AI, AI-Ethics, and XAI – Murat Durmus-PPT.pdf
  7. 2021 Executive Guide to Data Science and AI-GUIDE.pdf
  8. 2022-AI-Index-Report_Master-REPORT.pdf
  9. 33-Ways-to-Tableau-by-Ryan-Sleeper-PPT.pdf
  10. 38 Free Courses on Coursera for Data Science-PPT.pdf
  11. 50 YearsDataScience-REPORT.pdf
  12. 50-Ways-Impact-Business-AI-RapidMiner-REPORT.pdf
  13. 83+Datasets from Google-PPT.pdf
  14. 9 Best practices every Data Science leader should follow-PPT.pdf
  15. A brief guide for Data Cleaning-NOTE.pdf
  16. A Collection of 10 free ‘Springer’ Books-LINKS.pdf
  17. A Comprehensive Guide to Machine Learning-BOOK.pdf
  18. A Comprehensive Survey on Graph Neural Networks-PAPER.pdf
  19. A Concrete Introduction to Higher Algebra-BOOK.pdf
  20. A course in Time Series Analysis-eBook.pdf
  21. A Framework For The Ethical Use Of Advanced Data Science in Humanitarian Sector-GUIDE.pdf
  22. A Guide for Ethical Data Science-GUIDE.pdf
  23. A guide to using AI in the public sector-GUIDE.pdf
  24. A list with over 150 Biases (Cognitive, Social, & Memory)-REPORT.pdf
  25. A practical guide for testing for DEVOPS.pdf
  26. A Practical Guide to Getting Started with DevOps-REPORT.pdf
  27. A Programmers Guide to Data Mining-BOOK.pdf
  28. A Selective Overview of Deep Learning-PAPER.pdf
  29. A Short introduction to boosting-PAPER.pdf
  30. A Survey of the State of Explainable AI for NLP-PAPER.pdf
  31. A Survey on Explainable AI (XAI) towards Medical XAI-PAPER.pdf
  32. A Visual Introduction to Deep Learning-GUIDE.pdf
  33. A-Brief-Introduction-NeuralNetwork-by-DavidKriesel-BOOK.pdf
  34. A-Gentle-Introduction-to-Optimization-eBook.pdf
  35. Advance Introduction to Machine Learning CMU-PPT.pdf
  36. Advanced Analytics with Spark-BOOK.pdf
  37. Advanced Data Analysis from an Elementary Point of View-BOOK.pdf
  38. Advanced Deep Learning with TensorFlow2-BOOK.pdf
  39. Advanced Python Tips-BOOK.pdf
  40. Advancing mathematics by guiding human intuition with AI-ARTC.pdf
  41. Advantages & Disadvantages of k-Means and Hierarchical clustering-PPT.pdf
  42. Agile-AI-REPORT.pdf
  43. AI An Introduction to the Legal, Policy & Ethical Issues-REPORT.pdf
  44. AI and Education-REPORT.pdf
  45. AI and Ethics – Operationalising Responsible AI-PAPER.pdf
  46. AI for Data Science-eBOOK.pdf
  47. Algorithmic Aspects of Machine Learning-BOOK.pdf
  48. Algorithms for Decision Making-BOOK.pdf
  49. Algorithms for Decision Making-eBOOK.pdf
  50. Algorithms-Notes-For-Professionals-NOTES.pdf
  51. All-Of-Statistics-A-Concise-Course-in-Statistical-Inference_by_Larry-BOOK.pdf
  52. Amazon Machine Learning-Developer Guide-TOOL.pdf
  53. An Introduction to Cloud Databases-REPORT.pdf
  54. An Introduction to Data – Everything You Need to Know about AI and Bigdata-eBOOK.pdf
  55. An Introduction to Statistical Learning with Application in R-BOOK.pdf
  56. An-Introduction-To-Machine-Learning-byMiroslav-BOOK.pdf
  57. Analyzing The Expressive Power of Graph Neural Network in a Spectral Perspective-PAPER.pdf
  58. Analytics in a Big Data World The Essential Guide to Data Science and its Applications-BOOK.pdf
  59. An_Introduction_to_Genetic_Algorithms-BOOK.pdf
  60. Approaching (Almost) Any Machine Learning Problem-eBOOK.pdf
  61. Artificial Intelligence for a Better Future- An Ecosystem Perspective on the Ethics of AI-BOOK.pdf
  62. Automated Machine Learning Methods, Systems, Challenges-BOOK.pdf
  63. Awesome Visualization Techniques for every dataset-NOTEBOOK.pdf
  64. AWS Admin HANDBOOK.pdf
  65. AWS lens – Machine Learning Lens-TOOL.pdf
  66. AWS Short Notes-TOOL.pdf
  67. Azure ML-NOTEBOOK.pdf
  68. Azure Storage Services-TOOL.pdf
  69. Azure-ComputerVision_Jun2021-eBOOK.pdf
  70. A_Comprehensive_Guide_for_Visualization-PPT.pdf
  71. A_Course_in_Machine_Learning_byHalDaume-BOOK.pdf
  72. Basics of cloud computing-PPT.pdf
  73. Bayes theorem_A_Visual_Introduction_for_Beginner-eBOOK.pdf
  74. Bayesian Reasoning and Machine Learning-BOOK.pdf
  75. BAYESUVIUS-A_Visual_Dictionary_of_Bayesian _Networks_and_Causal_Inference-BOOK.pdf
  76. Baysian Reasoning & Machine Learning-BOOK.pdf
  77. BD-Planning-for-big-data-CIO-HANDBOOK.pdf
  78. Beginners Guide to Analytics-GUIDE.pdf
  79. Beginners_guide_to_data_science-_a_twics_guide-PPT.pdf
  80. Best Practices for ML Engineering-REPORT.pdf
  81. Big Data Analytics Overview-REPORT.pdf
  82. Big Data-Casestudy Collection-REPORT.pdf
  83. BigBook-Experimentation-Casestudies-PPT.pdf
  84. Bigdata-Hadoop and Spark-Performance-for-the-enterprise-REPORT.pdf
  85. Box Plot and Identifying Outliers-PPT.pdf
  86. Bright Data’s Solution Diagram-GUIDE.pdf
  87. Building Data Science Teams-REPORT.pdf
  88. Building Intelligent Apps with Cognitive APIs-BOOK.pdf
  89. Building_effective_dashboards-ARTC.pdf
  90. Business Analyst HANDBOOK.pdf
  91. C++ Tutorial-NOTES.pdf
  92. Cambridge_International_AS__A_Level_Mathematics_Probability_and_Statistics_1-BOOK.pdf
  93. Career guide for product managers-GUIDE.pdf
  94. Challenges in Deploying Machine Learning-PAPER.pdf
  95. Chapter1-KocovicRadenkovic-doi-BOOK.pdf
  96. Chatbot HANDBOOK.pdf
  97. Clinical AI quality improvement-PAPER.pdf
  98. Cloud Application Architecture-PPT.pdf
  99. Cloud Choices for Microsoft Project 2013 and 2010-F-3.2013-ARTC.pdf
  100. Comet-Standardizing-ML-Experiment-REPORT.pdf
  101. Communication Skills-REPORT.pdf
  102. Communications Skills for Managers-GUIDE.pdf
  103. Complete-Data Structures and Algorithms Roadmap-REPORT.pdf
  104. Complete_Machine_learning_Notes_from_Andrew_Ng-NOTEBOOK.pdf
  105. Comprehensive & Practical Inferential Statistics Guide-GUIDE.pdf
  106. COMPREHENSIVE GUIDE TO INTERVIEWS FOR DATA SCIENCE.pdf
  107. Computer Age Statistical Inference-BOOK.pdf
  108. Computer Vision Algo & Applications-by-Szeliski-BOOK.pdf
  109. Computer Vision Model, Learning and Inference-BOOK.pdf
  110. Computer Vision Projects-PPT.pdf
  111. Computing Neural Network Gradients-REPORT.pdf
  112. Core Principle of Data Visualization-GUIDE.pdf
  113. Cost of Data Breach Study 2020 byIBMSecurity-REPORT.pdf
  114. CS725 – Foundations of Machine learning – Lecture Notes.pdf
  115. Dashboards in Python-TOOL.pdf
  116. Data Analytics Pipeline-PPT.pdf
  117. Data Cleaning Resource-GUIDE.pdf
  118. Data Driven Creating a Data Culture-ARTC.pdf
  119. Data Engineering Fundamentals-NOTES.pdf
  120. Data Governance Clinic-REPORT.pdf
  121. Data Integration-Introduction-PPT.pdf
  122. Data Mesh Book-TOC.pdf
  123. Data Mining Process, Techniques, Tools-ARTC.pdf
  124. Data Modeling for Data Engineers-PPT.pdf
  125. Data Science Applications & use Cases-PPT.pdf
  126. Data Science Course for beginners-eBOOK.pdf
  127. Data Science for Busines_by_FosterProvost_TomFawcett-BOOK.pdf
  128. Data Science from Scratch-BOOK.pdf
  129. Data Science in Marketing Analytics BOOK.pdf
  130. Data Science Open Toolkit-PPT.pdf
  131. Data science skills-PPT.pdf
  132. Data Science Tools-GUIDE.pdf
  133. Data Strategy- NOTES.pdf
  134. Data Structures and Algorithms Roadmap-REPORT.pdf
  135. Data Structures And Algorithms With Python-BOOK.pdf
  136. Data Visualization 101-How_to_Design_Charts_and_Graphs-PPT.pdf
  137. Data Visualization in R-PPT.pdf
  138. Data Visualization in Society-BOOK.pdf
  139. Data Visualization with R-PPT.pdf
  140. Data Visualization-storytelling-REPORT.pdf
  141. Data Visualization1- How to Pick the Right Chart Type-REPORT.pdf
  142. Data Visualization2- How to Pick the Right Chart Type-REPORT.pdf
  143. Data Visualization_Grossberg_lecture1-PPT.pdf
  144. Data Visualisation-Advanced_Plotting-PPT.pdf
  145. Data Warehouse Concepts-ARTC.pdf
  146. Data-Career-Skills-Checklist.pdf
  147. Data-visualization-made-simple-SosulskiKristen-BOOK.pdf
  148. DataGovernance-A conceptual framework, structured review-PAPER.pdf
  149. DataScienceBooks-NOTES.jpg
  150. Data_Analytics-Practical_Guide-BOOK.pdf
  151. Data_preparation_for_data_mining-NOTES.pdf
  152. Data_Science_from_Scratch_by_GoelGrus_BOOK.pdf
  153. Decision Trees-NOTES.pdf
  154. Deep Adversarial Learning for NLP-PPT.pdf
  155. Deep Fake-PPT.pdf
  156. Deep Learning Cookbook-BOOK.pdf
  157. Deep Learning for Dummies-BOOK.pdf
  158. Deep Learning for NLP.pdf
  159. Deep Learning for Understanding Consumer Histories-NOTE.pdf
  160. Deep Learning in Medical Image Analysis-PPT.pdf
  161. Deep Learning on Graphs for NLP-PPT.pdf
  162. Deep learning theory lecture notes – Advanced Topics-NOTES.pdf
  163. Deep Learning with Azure-BOOK.pdf
  164. Deep Learning with Python-BOOK.pdf
  165. Deep Learning-by-JohnKelleher-MIT-Press-BOOK.pdf
  166. Deep Learning-From-Basics-to-Practice-by-AndrewGlassner-CHAPTER.pdf
  167. Deep Learning-NOTES.pdf
  168. Deep Reinforcement Learning-BOOK.pdf
  169. Deep-Learning-BOOK.pdf
  170. Deep-Learning-Interviews-Problem & Solutions-BOOK.pdf
  171. Deep-Learning-with-PyTorch-BOOK.pdf
  172. Deep_Learning-by-IanGoodfellow-TOC.pdf
  173. Deep_Learning_Methods_and_Applications-BOOK.pdf
  174. Deep_learning_with_python_mini_course-SYLLABUS.pdf
  175. Designing The SIEM Monitoring Environment-PPT.pdf
  176. Detailed human avatar-PAPER.pdf
  177. Detecting Damaged And Undamaged During Hurricane-NOTEBOOK.pdf
  178. Developmental Neurobiology-BOOK.pdf
  179. DevOps Complete Package .pdf
  180. DevOps Training Content-TOC.pdf
  181. Digital Entrepreneurship Impact on Business and Society-BOOK.pdf
  182. Digital Image Processing Introduction-GUIDE.pdf
  183. Discrete Mathematics-Book.pdf
  184. Dive into Deep Learning-BOOK.pdf
  185. Docker & Python-PPT.pdf
  186. DRIFT_26_CameraReadySubmission_NeurIPS_DRL-PAPER.pdf
  187. DS Portfolio 101-PPT.pdf
  188. DSA Preparation-BinaryTree-NOTES.pdf
  189. Dynamic Routing Between Capsules-PAPER.pdf
  190. Dynamical Systems with Applications using Python-BOOK.pdf
  191. Easy and Quick guide to Statistics of Machine Learning-GUIDE.pdf
  192. EDA the right way-BOOK.pdf
  193. Efficient Learning Machines-BOOK.pdf
  194. Email Sentences and Phrases in Different Situations-GUIDE.pdf
  195. Emotions Detection From Text Using LSTM NLP network-NOTEBOOK.pdf
  196. Ensemble Learning-PPT.pdf
  197. Essentials-of-Business-Analytics-BOOK.pdf
  198. Ethical Considerations and Statistical Analysis of Industry Involvement in ML Research-REPORT.pdf
  199. Ethics & Governance of Artificial Intelligence for Health by WHO-GUIDE.pdf
  200. Evaluating ML Model-BOOK.pdf
  201. Excel-Advanced_Excel_Charts_byTP-BOOK.pdf
  202. Excel-This-isnt-excel-it-is-magic-BOOK.pdf
  203. Excel2010_byTP-BOOK.pdf
  204. Excel_charts_TP-BOOK.pdf
  205. Excel_Data_Analysis_TP-BOOK.pdf
  206. Excel_macros_byTutorialPoint-BOOK.pdf
  207. Excel_power_pivot_byTP-BOOK.pdf
  208. Executive Data Science-GUIDE.pdf
  209. Expert’s Way To Search On Google-NOTES.pdf
  210. Explaining decisions made with AI-BOOK.pdf
  211. Exploratory Data Analysis-NOTEBOOK.pdf
  212. Exploring Data Science-BOOK.pdf
  213. Fairness in Machine Learning A Survey-PAPER.pdf
  214. Feature Engineering Made Easy-BOOK.pdf
  215. Feature Engineering-PPT.pdf
  216. Fighting depression and mental illness with AI-NOTES.pdf
  217. Flask API-PPT.pdf
  218. Flask_Web_Development_Developing-BOOK.pdf
  219. fluent-python-BOOK.pdf
  220. Forecast Time Series Data Using Deep Learning-NOTES.pdf
  221. Forecasting COVID-19 Confirmed Cases-PAPER.pdf
  222. Foundations of Data Science-REPORT.pdf
  223. Foundations of Reinforcement Learning with Applications in Finance_by_AswinRao-BOOK.pdf
  224. FourthBrain MLE-Jan2022-SYLLABUS.pdf
  225. Free ML AI resources by DeepMind-LINKS.pdf
  226. Fundamentals of Deep Learning-byNikhil-BOOK.pdf
  227. fundamentals-of-mlops-REPORT.pdf
  228. GAN-PPT.gslides
  229. GDPR_Developer_Guide-PPT.pdf
  230. Generalization Beyond Overfitting On Small Datasets-PAPER.pdf
  231. Getting Analytics Right-REPORT.pdf
  232. Getting Started With Tensorflow-BOOK.pdf
  233. Getting Your First Job in Data Science-REPORT.pdf
  234. Getting_Started-with-Artificial_Intelligence-REPORT.pdf
  235. Git Notes For Professionals-REPORT.pdf
  236. Git Tutorial Book.pdf
  237. GNN-based-Fraud Detection-PPT.pdf
  238. Going Pro in Data Science-eBOOK.pdf
  239. Graph Database for Beginners-GUIDE.pdf
  240. Graph Representation Learning-BOOK.pdf
  241. Graph Theory with Applications by BondyMurty-BOOK.pdf
  242. GraphDatabase_ML-PPT.pdf
  243. GraphMachine Learning NeurIPS 2020-PAPER.pdf
  244. Graph_Algorithms_Spark & Neo4j-BOOK.pdf
  245. Grokking Algorithms-BOOK.pdf
  246. Grouped Convolutional Neural Networks for Multivariate Time Series -PAPER.pdf
  247. Grouped functional time series forecasting An application to age-specific mortality rates-PAPER.pdf
  248. Guide to Machine Learning Pipelines-GUIDE.pdf
  249. Hand Gesture Recognition Algorithm-NOTEBOOK.pdf
  250. Handbook_of_Formulae_and_Constants-GUIDE.pdf
  251. Hands On Machine Learning with Scikit Learn and TensorFlow-BOOK.pdf
  252. Hands on Machine Learning with Scikit-Learn, Keras & TensorFlow-BOOK.pdf
  253. Hands-On Python Tutorial-BOOK.pdf
  254. Hands-on-machine_Gueron-BOOK.pdf
  255. Hidden Markov Model-CHAPTER.pdf
  256. Hidden Technical Debt in Machine Learning Systems-PAPER.pdf
  257. Hierarchical Clustering Dendrograms-REPORT.pdf
  258. How to avoid machine learning pitfalls-PAPER.pdf
  259. How to forecast Time Series Data using Deep Learning-ARTC.pdf
  260. How to Read a Paper-ARTC.pdf
  261. How to Read the abstract-GUIDE.pdf
  262. How to write a professional emails in English-eBOOK.pdf
  263. How-data-science-is-transforming-health-care-ARTC.pdf
  264. How-to-Create-an-AI-Center-of-Excellence-for-Enterprise-ARTC.pdf
  265. Identifying machine learning techniques for classification of target advertising-PAPER.pdf
  266. Image Classification Using Keras and Tensorflow -NOTEBOOK.pdf
  267. Improving Deep Neural Networks-NOTES.pdf
  268. Improving ICS Cybersecurity with Defense-In-Depth Strategies-REPORT.pdf
  269. Information Theory, Inference, and Learning Algorithms-BOOK.pdf
  270. Intro to Probability for Data Science-BOOK.pdf
  271. Introducing Microsoft Power BI-BOOK.pdf
  272. Introducing MLOps by Orelly-eBOOK.pdf
  273. Introducing MLOPS-How to Scale Machine Learning in the Enterprise-BOOK.pdf
  274. Introduction to algorithm by MIT Press-BOOK.pdf
  275. INTRODUCTION TO DATA SCIENCE.pdf
  276. Introduction to Docker and Docker Swarm-PPT.pdf
  277. Introduction to DS-PPT.pdf
  278. Introduction to Machine Learning with Python-BOOK.pdf
  279. Introduction to Machine Learning-BOOK.pdf
  280. Introduction to Probability and Statistics for Engineers and Scientists-BOOK.pdf
  281. Introduction to Probability for Data Science-BOOK.pdf
  282. Introduction to Programming in Python-TOOL.pdf
  283. Introduction to Scientific Programming with Python-BOOK.pdf
  284. Introduction to Statistical Learning TOC.pdf
  285. Introduction_to_algorithms-3rd Edition-BOOK.pdf
  286. Introduction_to_Applied_Linear_Algebra-BOOK.pdf
  287. Introductory-Business-Statistics-with-Interactive-Spreadsheets-BOOK.pdf
  288. Jumpstart Tableau-BOOK.pdf
  289. Kaggle State of Machine Learning & Data Science 2021-PPT.pdf
  290. Kanban vs Scrum-NOTES.pdf
  291. Keyboard Shortcuts for Data Scientists-GUIDE.pdf
  292. Knowledge_Graphs_Data_in_Context_Responsive-REPORT.pdf
  293. Kubernetes Introduction-PPT.pdf
  294. Large Object Storage in a Database or a Filesystem-PAPER.pdf
  295. Learn Keras for Deep Neural Networks-BOOK.pdf
  296. Learn Python in One Day and Learn It Well byJamieChan-BOOK.pdf
  297. Learning Graph Heuristic Search-PAPER.pdf
  298. Learning latent causal graphs via mixture oracles-PAPER.pdf
  299. Learning OpenCV 3 Computer Vision with Python-BOOK.pdf
  300. Learning Pandas-BOOK.pdf
  301. Learning Predictive Analytics with Python-BOOK.pdf
  302. Learning PySpark-BOOK.pdf
  303. Learning SQL book from Stakeoverlow-eBOOK.pdf
  304. Learning the parts of Objects by Non-negative Matrix Factorization-ARTC.pdf
  305. Learning to Love Data Science-BOOK.pdf
  306. Lecture Notes in_Machine Learning-byKrishnaChandra-BOOK.pdf
  307. Lecture Notes on Probability and Statistics-NOTES.pdf
  308. LightGBM A Highly Efficient Gradient Boosting-PAPER.pdf
  309. Linear Algebra-BOOK.pdf
  310. Linear Algebra- a Powerful Tool in Data Science-ARTC.pdf
  311. Linear Algebra-PPT.pdf
  312. Logging and Debugging-NOTEBOOK.pdf
  313. Logistic Regression- A Self-learning Text, Third Edition-BOOK.pdf
  314. Loss Functions and Metrics-PPT.pdf
  315. Machine Learning and data mining Lecture-NOTES.pdf
  316. Machine Learning and Security_ Protecting Systems with Data and Algorithms-BOOK.pdf
  317. Machine Learning Cheat Sheet-GUIDE.pdf
  318. Machine Learning for Everyone-GUIDE.pdf
  319. Machine Learning for Humans-BOOK.pdf
  320. Machine Learning From Scratch-REPORT.pdf
  321. Machine Learning God byStefanStavrev-BOOK.pdf
  322. Machine Learning in Python Essential Techniques-BOOK.pdf
  323. Machine Learning Observability Checklist-REPORT.pdf
  324. Machine Learning plus Intelligent Optimization-full-BOOK.pdf
  325. Machine learning with limited data-GUIDE.pdf
  326. Machine Learning with Limited Data-REPORT.pdf
  327. Machine Learning with Python for Everyone-GUIDE.pdf
  328. Machine Learning within a Graph Database- A Case Study on Link Prediction for Scholarly Data-PAPER.pdf
  329. Machine+Learning+Dev+Ops+Engineer+Nanodegree+Program-TOC.pdf
  330. Machine-learning-cheat-sheet-GUIDE.pdf
  331. Machine-Learning-Yearning-by-AndrewNG-BOOK.pdf
  332. Machine_Learning_1s_Course_for_Engineers_and_Scientists-BOOK.pdf
  333. Machtematics for Machine Learning-REPORT.pdf
  334. Make-AI-BI-Work-at-Scale-by-15Leaders-BOOK.pdf
  335. Management Consulting-Case Interview Workbook-REPORT.pdf
  336. Manning Deep Reinforcement Learning in Action-BOOK.pdf
  337. Manual For Data Science Projects-NOTES.pdf
  338. Mastering Machine Learning with Python in Six Steps-BOOK.pdf
  339. Mastering Machine Learning with scikit-learn-BOOK.pdf
  340. Mastering Matplotlib-TOOL.pdf
  341. Mastering Pandas for Finance-BOOK.pdf
  342. Mastering Pandas-BOOK.pdf
  343. Mastering Python For Data Science-BOOK.pdf
  344. Mastering Python Regular Expressions-BOOK.pdf
  345. Math of Deep Learning Neural Networks-REPORT.pdf
  346. Mathematical Foundations of Data Sciences-BOOK.pdf
  347. Mathematics_for_Machine_Learning-BOOK.pdf
  348. Mathematics for Machine Learning-BOOK.pdf
  349. Matplotlib for Python Developers-TOOL.pdf
  350. Maximum Likelihood Estimation-CHAPTER.pdf
  351. Merlion- A Machine Learning Library for Time Series-PAPER.pdf
  352. Microsoft .NET Framework 4.5 Quickstart Cookbook-BOOK.pdf
  353. Microsoft Azure Handbook-GUIDE.pdf
  354. Microsoft Security Best Practices-GUIDE.pdf
  355. Microsoft System Center – Deploying HyperV with Software Defined Storage and Networking-BOOK.pdf
  356. MicrosoftPress-Azure Machine Learning-eBook.pdf
  357. MicrosoftPress-Data Science with Microsoft SQL Server 2016.-eBook.pdf
  358. MicrosoftPress-Developing Business Intelligence Apps for SharePoint-BOOK.pdf
  359. MicrosoftPress_Azure_Automation-eBOOK.PDF
  360. MicrosoftPress_Azure_Web_Apps_for_Developers-eBook.pdf
  361. MicrosoftPress_Building_Cloud_Apps_with Microsoft_Azure-eBook.pdf
  362. MicrosoftPress_CreatingMobileAppswithXamarinForms-eBook.pdf
  363. MicrosoftPress_Introducing_Azure-ebook.pdf
  364. MicrosoftPress_Introducing_HDInsight-eBook.pdf
  365. MicrosoftPress_Introducing_Microsoft_SQL_Server_2014-ebook.pdf
  366. MicrosoftPress_Introducing_Power_BI-ebook.pdf
  367. MicrosoftPress_Introducing_Windows_10-eBook.pdf
  368. MicrosoftPress_Introducing_Windows_ITPro-ebook.pdf
  369. MicrosoftPress_Managing_Agile_Software_Projects-eBook.pdf
  370. MicrosoftPress_Microsoft_Platform_and_Tools_for_Mobile_App_Development-eBook.pdf
  371. MicrosoftPress_NET_Technology_Guide_for_Business_Applications-eBook.pdf
  372. MicrosoftPress_Programming_Windows_8_Apps_HTML_CSS_JavaScript_2E-eBook.pdf
  373. Migrating BigData Analytics into the Cloud-REPORT.pdf
  374. Mining_Of_Massive_Datasets-BOOK.pdf
  375. ML applications in marketing-PPT.pdf
  376. ML applications in retail-PPT.pdf
  377. ML for Absolute Beginners-REPORT.pdf
  378. MLOps and data versioning in ML Projects-ARTC.pdf
  379. MLOps Guide from Google-GUIDE.pdf
  380. Models of Learning and Optimization for Data Scientists-NOTEBOOK.pdf
  381. MongoDB with Python-NOTEBOOK.pdf
  382. Move your data science to production-Saturn Cloud-REPORT.pdf
  383. MySQL Notes For Professionals-BOOK.pdf
  384. National AI Strategies and Human Rights- A Review-REPORT.pdf
  385. Natural Language Processing Recipes-BOOK.pdf
  386. Natural_Language_Processing_with_Python-BOOK.pdf
  387. Neo4j_Graph_Algorithms-BOOK.pdf
  388. Neural Networks And Deep Learning-BOOK.pdf
  389. Neural Networks-NOTE.pdf
  390. NeuralProphet-Explainable Forecasting at Scale-PAPER.pdf
  391. Nine Emerging Python Libraries for Data Science-NOTES.pdf
  392. NLI-Tutorial-PPT.pdf
  393. NLP-Panini-Perspective-BOOK.pdf
  394. Numerical Python a Practical Techniques Approach for Industry-BOOK.pdf
  395. OLAP vs OLTP-PPT.pdf
  396. OLTP vs OLAP-ARTC.pdf
  397. Open Data Structures-BOOK.pdf
  398. Open Data Structures_ An Introduction-BOOK.pdf
  399. Optimization for Machine Learning-NOTES.pdf
  400. Outlier Detection-PPT.pdf
  401. Over 100 Data and Analytics Predictions Through 2025-ARTC.pdf
  402. Pandas – Powerful Python data analysis-NOTEBOOK.pdf
  403. Pandas at one place-NOTEBOOK.pdf
  404. Pandas-10min-NOTEBOOK.pdf
  405. Patterns, Predictions, And Action – A story about Machine Learning-BOOK.pdf
  406. PAVE-Lazy-MDP based Ensemble to Improve Recall of Product Attribute Extraction Models-PAPER.pdf
  407. PC Architecture-BOOK.pdf
  408. Phynance-TOC.pdf
  409. Plotly-NOTEBOOK.pdf
  410. Policy Guide on AI for Children-GUIDE.pdf
  411. Power BI – Step by step training -GUIDE.pdf
  412. Power BI for Beginners-NOTES.pdf
  413. Power BI Tutorial-PPT.pdf
  414. PowerBI for Intermediates-GUIDE.pdf
  415. Practical SQL A Beginner’s Guide to Storytelling with Data-BOOK.pdf
  416. Practical Statistics for Data Scientists-BOOK.pdf
  417. Practical Statistics_Visualization With Python _ Plotly-ARTC.pdf
  418. Practical Time Series Analysis Prediction with Statistics and Machine Learning-BOOK.pdf
  419. Practitioners Guide for MLOPS – Google Cloud-GUIDE.pdf
  420. Practitioners Guide to MLOPS by GoogleCloud-GUIDE.pdf
  421. Presenting_Data_People_Cannot_Ignore-REPORT.pdf
  422. Presenting_Data_People_Cant_Ignore_PPT.pdf
  423. Principles of Data Analysis By Prasenjit Saha-REPORT.pdf
  424. Principles of Data Wrangling-BOOK.pdf
  425. Pro Python System Administration-BOOK.pdf
  426. Probability & Statistics with Applications to Computing-BOOK.pdf
  427. Probability and Statistics for Data Science-BOOK.pdf
  428. Probability and Statistics for Engineers and Scientists_bySheldonRoss-BOOK.pdf
  429. Probability-GUIDE.pdf
  430. Production Machine Learning For Dummies-BOOK.pdf
  431. Programmers Guide to Data Mining The Art of the Numerati-BOOK.pdf
  432. Programming Hive-BOOK.pdf
  433. Proper Clustering Problem using K-means and Hierarchical-NOTEBOOK.pdf
  434. Python – Intermediate-BOOK.pdf
  435. Python 3 for Absolute Beginners-BOOK.pdf
  436. Python AI Projects for Beginners-8 smart and exciting AI applications-BOOK.pdf
  437. Python Data Science Essentials-BOOK.pdf
  438. Python Data Science Handbook-BOOK.pdf
  439. Python for Data Analysis- Wrangling with Pandas, Numpy and IPython-BOOK.pdf
  440. Python For Everybody-BOOK.pdf
  441. Python For Kids-BOOK.pdf
  442. Python for NLP-PPT.pdf
  443. Python HANDBOOK.pdf
  444. Python Machine Learning and Deep Learning with scikit-learn, and TensorFlow-BOOK.pdf
  445. Python Machine Learning Cookbook_Practical Solutions from Preprocessing to Deep Learning-BOOK.pdf
  446. Python Machine Learning Projects-BOOK.pdf
  447. Python Notes For Professionals-BOOK.pdf
  448. Python Programming for Beginner-by Michael Daswon-BOOK.pdf
  449. Python programming for everyone-BOOK.pdf
  450. Python Tricks-BOOK.pdf
  451. Python_Cheat Sheet-NOTEBOOK.pdf
  452. Pytorch-TOC.pdf
  453. QC Paulinesia – Quantum Computing-GUIDE.pdf
  454. Quality Measuring Tools-REPORT.pdf
  455. Quantum computing Emerging ecosystem & Industry use cases-REPORT.pdf
  456. Quantum Computing-BOOK.pdf
  457. Quantum Computing_Progress and Prospects-BOOK.pdf
  458. Quantum Mechanics-Fall2021 Lecture Notes by BarakShoshany-BOOK.pdf
  459. Query_Optimization_Techniques-PPT.pdf
  460. Query_Optimization_Techniques1-PPT.pdf
  461. R Programming for ANY Team-REPORT.pdf
  462. Randomforest-PAPER.pdf
  463. README.md
  464. Real Time BigData Analytics Emerging Architecture-REPORT.pdf
  465. Receptive Fields of Single Neurones in the Cats Striate Cortex-PAPER.pdf
  466. Reference for choosing right data visualization-GUIDE.pdf
  467. Regular Expressions Cookbook-BOOK.pdf
  468. Regulate to Innovate-REPORT.pdf
  469. Reinforcement Learning.pdf
  470. Reinforcement Learning2020-BOOK.pdf
  471. Reinforcement-Learning-An-Introduction-BOOK.pdf
  472. Relevance and impact of AI for company law-REPORT.pdf
  473. Research-Methods-in-Psychology-BOOK.pdf
  474. Resources for Datascientists-TOC.pdf
  475. Resources to Learn AI-LINKS.pdf
  476. Resumes and Cover Letters-GUIDE.pdf
  477. Review of Probability Theory-REPORT.pdf
  478. Robotics, AI, and Humanity Science-PAPERS.pdf
  479. Robustness and Explainability of Artificial Intelligence-REPORT.pdf
  480. ROC Graphs- Notes and Practical Considerations for Researchers-GUIDE.pdf
  481. Scalable Linear Algebra on a Relational Database System-PAPER.pdf
  482. Scaling Large Production Clusters-PAPER.pdf
  483. Semantic Layer for Data Analytics-REPORT.pdf
  484. Seven Databases in Seven Weeks-NOTES.pdf
  485. SGBoost-PPT.pdf
  486. Simplifying Machine Learning with PyCaret-TOOL.pdf
  487. Skewness-PPT.pdf
  488. Skillful writing of an awful research paper-GUIDE.pdf
  489. Speech and Language Processing-BOOK.pdf
  490. Springer Ebooks-LINKS.pdf
  491. Springer Ebooks-NOTES.pdf
  492. Springer-LINKS.pdf
  493. SQL API with Python-NOTEBOOK.pdf
  494. SQL Complete-NOTES.pdf
  495. SQL for Data Analysis by CathyTanimura-BOOK.pdf
  496. SQL for Data Science by AntoniaBadia-BOOK.pdf
  497. SQL Free eBOOK.pdf
  498. SQL Nulls-PPT.pdf
  499. SQL Tutorial-NOTES.pdf
  500. Standard Normal Probabilities-GUIDE.pdf
  501. Starting a Career in Data Science-REPORT.pdf
  502. Statistical foundations of machine learning-BOOK.pdf
  503. Statistical Modeling- The Two Cultures-REPORT.pdf
  504. Statistical Patterns Recognition_PPT.pdf
  505. Statistics & Machine Learning in Python-BOOK.pdf
  506. Statistics and Machine Learning in Python-BOOK.pdf
  507. Statistics for Machine Learning-BOOK.pdf
  508. Statistics Machine Learning in Python-BOOK.pdf
  509. Statistics Notes-Day1-NOTE.pdf
  510. Statistics_Cartoon_Guide-BOOK.pdf
  511. Statistics_With_Julia_Fundamentals for Data Science, Machine Learning and AI-BOOK.pdf
  512. Statistics Demystified-BOOK.pdf
  513. STREAMING WITH Publish Subscribe Messaging with Kafka-PPT.pdf
  514. Super-study-guide-data-science-tools-REPORT.pdf
  515. Supervised Learning-GUIDE.pdf
  516. Supervised Machine Learning – Lecture Notes for Statistical Machine Learning-BOOK.pdf
  517. Supervised Machine Learning-BOOK.pdf
  518. Supplementary Material for _Probabilistic Machine Learning-Advanced Topics-BOOK.pdf
  519. Support Vector Machine-PPT.pdf
  520. System Design Basics Handbook-NOTES.pdf
  521. Tableau Tips-PPT.pdf
  522. Talend_DefinitiveGuide_DataGovernance-GUIDE.pdf
  523. Theory of Quantum Information-NOTES.pdf
  524. TGN-Temporal Graph Networks for Dynamic Graphs-REPORT.pdf
  525. The 5 Feature Selection Algorithms every Data Scientist should know-ARTC.pdf
  526. The AI revolution in scientific research-REPORT.pdf
  527. The AI Thought Book by Murat Durmus-PPT.pdf
  528. The Analytics Setup Guidebook-BOOK.pdf
  529. The Art of Effective Visualization of Multi-dimensional Data-ARTC.pdf
  530. The Art of Machine Learning-Algorithms+Data+R-BOOK.pdf
  531. The Complete Guide to Data Storytelling-GUIDE.pdf
  532. The Comprehensive Guide to DAX-BOOK.pdf
  533. The Data Revolution_Big Data Open Data Infrastructures and Their Consequences-BOOK.pdf
  534. The Data Science Design Manual-BOOK.pdf
  535. The Data Science Handbook-BOOK.pdf
  536. The Definitive Guide to DAX-NOTEBOOK.pdf
  537. The Elements of Statistical Learning Data Mining, Inference, and Prediction-BOOK.pdf
  538. The Elements of Statistical Learning-BOOK.pdf
  539. The Entity Relationship (ER) Model-PPT.pdf
  540. The Future of Analytics-BOOK.pdf
  541. The LION Way – Machine Learning plus Intelligent Optimization-Part3-BOOK.pdf
  542. The Matrix Cookbook-GUIDE.pdf
  543. The Modern Mathematics of Deep Learning-PAPER.pdf
  544. The Next Level of Data Visualization in Python-ARTC.pdf
  545. The Product Manager Handbook-BOOK.pdf
  546. The React Beginner’s Handbook-HANDBOOK.pdf
  547. The state of DevOps Tools-REPORT.pdf
  548. The Ultimate Guide to Effective Data Collection-REPORT.pdf
  549. The Unicode® Standard Version 12.0-Core Specification-GUIDE.pdf
  550. The-365-Data-Science-Booklet-PPT.pdf
  551. The-Data-Science-Career-Guide-by365DataScience-PPT.pdf
  552. The-Official-Kanban-GUIDE.pdf
  553. The-Python-Apprentice-by_RoberSmallshire-BOOK.pdf
  554. The-Python-Journeyman-byAustin-BOOK.pdf
  555. The-Python-Master-by-RobertSmallshire-BOOK.pdf
  556. The.StatQuest.Illustrated.Guide.to.Machine.Learning-PPT.pdf
  557. TheHundredPage_MachineLearning-BOOK.pdf
  558. The_5_most_influential_visuals-REPORT.pdf
  559. The_Data_Science-Booklet.pdf
  560. Think Complexity-BOOK.pdf
  561. Think Python How to Think Like a Computer Scientist-BOOK.pdf
  562. Think Stats-BOOK.pdf
  563. Think_Stats_Probality_and_Stats_for_Programmers-BOOK.pdf
  564. Thriver Ashish’s Complete DSA Sheet.pdf
  565. Time Series using Deep Learning-CHAPTER.pdf
  566. TimeSeries Forecasting Using fbprophet-NOTEBOOK.pdf
  567. TMLC-BROC.pdf
  568. Tokenization of Infrastructure A blockchain-REPORT.pdf
  569. Tokenization of Infrastructure-REPORT.pdf
  570. Top 10 for Data Science-PPT.pdf
  571. Top 12 AI Tools & Frameworks You Need to Know-REPORT.pdf
  572. Towards Demystifying Serverless Machine Learning Training-PAPER.pdf
  573. Towards_Responsible_Data_Practices_for_Machine_Learning_in_India-Health_and_Agriculture-REPORT.pdf
  574. Transfer Learning – VGG19 Model-NOTEBOOK.pdf
  575. Tree Based ML Models-PPT.pdf
  576. Triple-View Feature Learning for Medical Image Segmentation-PAPER.pdf
  577. TwoPhaseCommitIsEvil-PPT.pdf
  578. Uber Tagged LeetCode Problems-LINKS.pdf
  579. Ultimate Guide to Data Cleaning-ARTC.pdf
  580. Undergraduate Texts in Mathematics-BOOK.pdf
  581. Understanding bias in facial recognition technologies-REPORT.pdf
  582. Understanding Diffusion Models- A Unified Perspective-PAPER.pdf
  583. Understanding Machine Learning From Theory to Algorithms-BOOK.pdf
  584. Understanding Transformers, the Data Science Way-NOTE.pdf
  585. Use of Sanskrit for Natural Language Processing-REPORT.pdf
  586. Value-creation-in-the-metaverse-REPORT.pdf
  587. Vectors-HANDBOOK.pdf
  588. Very Deep Convolutional Networks for Large Scale Image Recognition-PAPER.pdf
  589. VIS-Building_effective_dashboards-REPORT.pdf
  590. VIS-ggplot2-NOTEBOOK.pdf
  591. VIS-Introducing Plotly Express – Plotly-ARTC.pdf
  592. VIS-Visualization in plotly with R-NOTEBOOK.pdf
  593. Visualize And Modeling Joint Behaviors of Categorical Variables-BOOK.pdf
  594. Web3 Glossary-PPT.pdf
  595. What is a Data Warehouse-PPT.pdf
  596. What Is Data Warehousing-ARTC.pdf
  597. What is Dimensional Model-ARTC.pdf
  598. What is Exploratory Data Analysis-ARTC.pdf
  599. What is Meta Data-PPT.pdf
  600. What is OLAP-ARTC.pdf
  601. Word Embedding LSA, Word2Vec, Glove, ELMo-PPT.pdf
  602. Writing for computer science 3rd edition-BOOK.pdf
  603. XAI – David Gunning (DARPA)-PPT.pdf
  604. XGB Regressor With Hyperparameter Tuning-TOOL.pdf
  605. XGBoost A Scalable Tree Boosting System-PAPER.pdf
  606. XGBoost Reliable Large-scale Tree Boosting System-PAPER.pdf

Free Online Books

  1. Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville (05/07/2015)
  2. Neural Networks and Deep Learning by Michael Nielsen (Dec 2014)
  3. Deep Learning by Microsoft Research (2013)
  4. Deep Learning Tutorial by LISA lab, University of Montreal (Jan 6 2015)
  5. neuraltalk numpy-based RNN/LSTM implementation by Andrej Karpathy
  6. Artificial Intelligence: A Modern Approach
  7. Deep Learning in Neural Networks: An Overview
  8. Artificial intelligence and machine learning: Topic wise explanation 
  9. Grokking Deep Learning for Computer Vision
  10. Dive into Deep Learning – numpy based interactive Deep Learning book