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  • benamin / idea_activate

    ultimate-python, JetBrains全家桶激活 idea激活 Activator idea全家桶激活 JetBrains收费所有收费插件激活 idea收费所有收费插件激活 JetBrains 2017-2022所有版本激活 idea激活 phpstorm激活 pycharm激活 webstorm激活 AppCode激活 Goland激活 Rubymine激活 clion激活 datagrip激活 ride激活 IntelliJ IDEA PhpStorm AppCode DataGrip RubyMine WebStorm Rider CLion PyCharm GoLand DataSpell dotCover dotTrace dotMemory Symfony Support Wolfram Language Shopware Hybris Integration Material Theme UI JetForcer | The Smartest Force.com IDE AEM IDE React Native Console ANSI Highlighter Premium AEM Support SystemVerilog CodeMR JavaDoc Clean Read AutoCode for Java Database Tools and SQL for WebStorm Extra Icons Java Antidecompiler Scipio ERP Integration ZenUML support JFormDesigner (Marketplace Edition) OrchidE - Ansible Language Support Iedis 2 CMake Plus OpenAPI Editor Iceberg Atom One Dark By Mayke Generate Document OfficeFloor Android WiFiADB Laravel Idea Odoo Bitbucket Pull Requests Merge Request Integration EE - Code Review for GitLab Salesforce B2C Commerce (SFCC) MinBatis POJO to JSON Schema RDF and SPARQL BashSupport Pro MyBatis Log Cypress Support Pro Smart Jump Auto Java Code Suggestions Auto GOLang Code Suggestions Auto Ruby Code Suggestions Commit Template Auto Javascript Code Suggestions Auto PHP Code Suggestions SystemVerilog Studio SPARQL Toolset QML Editor Stryker Elasticsearch VisualGC Auto Python Code Suggestions Flutter Storm AWS Lambda Deployer PlantUML Studio Code With Me Premium JPA Buddy Firebase Rules Syntax Highlighter JPA SQL Execution God Recorder Ledger CLI Requirements Pro Regex Tool Android Package Helper Gitlab CI Circle CI Heroku Console Helper Redis Manager Zerocode Scenario Helper StormSections Sentry Integration Redis Find In Files (Configurations) Bitrise CI Qt Style Sheets Editor Database Helper Php Inspections (EA Ultimate) leetcode editor pro PHP Data Object Generator Material Theme UI High Contrast MQTT Client Notes Magnolia YAML Assistant Cap-Elasticsearch AStock Cap-Redis Maestro Simple Object Copy ElasticSearch-Admin Typed Django Template QueryFlag Nginx Configuration Pro Zookeeper-Admin API Runner NEON Nette Support Mybatis Smart Code Help Pro Code Refactor AI XSD Visualizer Spring Boot Machinet AI Unit Tests ExcelEditor Friendly Terminal WireMocha DynamoDB FastShell Jsonnet Templating Language Support PHP Houdini XSD to JSON Schema Quarkus Assistant codeCreator CIclone Database Tool Karate Material Theme UI Extras Material Theme UI Custom Theme Material Theme UI Language Additions Material Theme UI Project Frame Rancher Redis Client ScreenCodePro CodeKits Redis Operator Restful Fast Request MyBatisCodeHelperPro (Marketplace Edition) Redis

    From user benamin

  • djdprogramming / adfa2

    ultimate-python, # David's Personal Roadmap to Learning Data Science #### Based on the article [Learn Data Science for free in 2021](https://www.kdnuggets.com/2021/01/learn-data-science-free-2021.html) from KDnuggets. Some additions have been made. ###### I'm new to data science and programming. Some areas of study in this roadmap may be researched to a point of redundancy while materials for other topics could be seriously lacking. As I progress through this learning path, I'll be able to gauge which areas need more (or less) focus and will add and remove resources as needed. ## Schoolwork ##### Required readings for my Data Science classes. - [ ] [Doing Data Science: Straight Talk from the Frontline](https://www.amazon.com/Doing-Data-Science-Straight-Frontline/dp/1449358659) by Cathy O'Neil & Rachel Schutt - [ ] 1. Introduction: What is Data Science? - [ ] 2. Statistical Inference, Exploratory Data Analysis, and the Data Science Process - [ ] 3. Algorithms - [ ] 4. Spam Filters, Naive Bayes, and Wrangling - [ ] 5. Logistic Regression - [ ] 6. Time Stamps and Financial Modeling - [ ] 7. Extracting Meaning from Data - [ ] 8. Recommendation Engines: Building a User-Facing Data Product at Scale - [ ] 9. Data Visualization and Fraud Detection - [ ] 10. Social Networks and Data Journalism - [ ] 11. Causality - [ ] 12. Epidemiology - [ ] 13. Lessons Learned from Data Competitions: Data Leakage and Model Evaluation - [ ] 14. Data Engineering: MapReduce, Pregel, and Hadoop - [ ] 15. The Students Speak - [ ] 16. Next-Generation Data Scientists, Hubris, and Ethics - [ ] [Practical Statistics for Data Scientists: 50 Essential Concepts](https://www.amazon.com/Practical-Statistics-Data-Scientists-Essential/dp/149207294X/ref=sr_1_1?dchild=1&keywords=Practical+Statistics+for+Data+Scientists&qid=1609991269&s=books&sr=1-1) by Peter Bruce, Andrew Bruce & Peter Gedeck - [ ] 1. Exploratory Data Analysis - [ ] 2. Data and Sampling Distributions - [ ] 3. Statistical Experiments and Significance Testing - [ ] 4. Regression and Prediction - [ ] 5. Classification - [ ] 6. Statistical Machine Learning - [ ] 7. Unsupervised Learning ## Programming Skills ##### Learn programming basics. - [ ] [Python 3 Basics Tutorial Series](https://www.youtube.com/playlist?list=PLQVvvaa0QuDe8XSftW-RAxdo6OmaeL85M) by sentdex - [ ] 1. Python 3 Programming Tutorial: Why Python 3? Python 2 vs Python 3 `7:36` - [ ] 2. Python 3 Programming Tutorial: Installing Python 3 - How to Install Both Python 2 and Python 3 `15:47` - [ ] 3. Python 3 Programming Tutorial: Print Function and Strings `9:31` - [ ] 4. Python 3 Programming Tutorial: Math `4:49` - [ ] 5. Python 3 Programming Tutorial: Variables `4:26` - [ ] 6. Python 3 Programming Tutorial: While Loop `5:55` - [ ] 7. Python 3 Programming Tutorial: For Loop `9:05` - [ ] 8. Python 3 Programming Tutorial: If Statement `4:54` - [ ] 9. Python 3 Programming Tutorial: If Else `3:20` - [ ] 10. Python 3 Programming Tutorial: If Elif Else `4:19` - [ ] 11. Python 3 Programming Tutorial: Functions `3:05` - [ ] 12. Python 3 Programming Tutorial: Function Parameters `4:00` - [ ] 13. Python 3 Programming Tutorial: Function Parameter Defaults `6:06` - [ ] 14. Python 3 Programming Tutorial: Global and Local Variables `6:31` - [ ] 15. Python 3 Programming Tutorial: Installing Modules `7:44` - [ ] 16. Python 3 Programming Tutorial: How to Download and Install Python Packages and Modules with Pip `8:32` - [ ] 17. Python 3 Programming Tutorial: Common Errors `4:49` - [ ] 18. Python 3 Programming Tutorial: Writing to File `3:35` - [ ] 19. Python 3 Programming Tutorial: Appending Files `2:42` - [ ] 20. Python 3 Programming Tutorial: Read from a File `1:49` - [ ] 21. Python 3 Programming Tutorial: Classes `4:56` - [ ] 22. Python 3 Programming Tutorial: Frequently Asked Questions `5:33` - [ ] 23. Python 3 Programming Tutorial: Getting User Input `1:43` - [ ] 24. Python 3 Programming Tutorial: Statistics (Mean, Standard Deviation) `2:36` - [ ] 25. Python 3 Programming Tutorial: Module Import Syntax `5:31` - [ ] 26. Python 3 Programming Tutorial: Making Modules `4:58` - [ ] 27. Python 3 Programming Tutorial: Lists and Tuples `5:51` - [ ] 28. Python 3 Programming Tutorial: List Manipulation `9:35` - [ ] 29. Python 3 Programming Tutorial: Multi-Dimensional List `5:45` - [ ] 30. Python 3 Programming Tutorial: Reading from a CSV Spreadsheet `9:24` - [ ] 31. Python 3 Programming Tutorial: Try and Except Error Handlings `7:04` - [ ] 32. Python 3 Programming Tutorial: Multi-Line Print `3:19` - [ ] 33. Python 3 Programming Tutorial: Dictionaries `7:11` - [ ] 34. Python 3 Programming Tutorial: Built-in Functions `10:58` - [ ] 35. Python 3 Programming Tutorial: OS Module `5:01` - [ ] 36. Python 3 Programming Tutorial: Sys Module `11:00` - [ ] 37. Python 3 Programming Tutorial: urllib Module `24:04` - [ ] 38. Python 3 Programming Tutorial: Regular Expressions/Regex with re `19:58` - [ ] 39. Python 3 Programming Tutorial: Parsing Websites with re and urllib `7:29` - [ ] 40. Python 3 Programming Tutorial: Tkinter Module Making Windows `8:03` - [ ] 41. Python 3 Programming Tutorial: Tkinter Adding Buttons `6:29` - [ ] 42. Python 3 Programming Tutorial: Tkinter Event Handling `5:40` - [ ] 43. Python 3 Programming Tutorial: Tkinter Menu Bar `10:25` - [ ] 44. Python 3 Programming Tutorial: Tkinter Adding Images and Text `11:59` - [ ] 45. Python 3 Programming Tutorial: Threading Module `18:43` - [ ] 46. Python 3 Programming Tutorial: cx_freeze Python to .exe `12:08` - [ ] 47. Python 3 Programming Tutorial: Subprocess Module `13:17` - [ ] 48. Python 3 Programming Tutorial: Matplotlib Graphing Intro `10:25` - [ ] 49. Python 3 Programming Tutorial: Matplotlib Labels and Titles `5:03` - [ ] 50. Python 3 Programming Tutorial: Matplotlib Styles `10:38` - [ ] 51. Python 3 Programming Tutorial: Matplotlib Legends `4:07` - [ ] 52. Python 3 Programming Tutorial: Scatter Plots and Bar Charts `6:38` - [ ] 53. Python 3 Programming Tutorial: Matplotlib Plotting from a CSV `7:21` - [ ] 54. Python 3 Programming Tutorial: ftplib FTP Transfers Python `8:47` - [ ] 55. Python 3 Programming Tutorial: Sockets Intro `10:48` - [ ] 56. Python 3 Programming Tutorial: Sockets Simple Port Scanner `5:08` - [ ] 57. Python 3 Programming Tutorial: Threaded Port Scanner `9:36` - [ ] 58. Python 3 Programming Tutorial: Sockets Binding and Listening `5:53` - [ ] 59. Python 3 Programming Tutorial: Sockets Client Server System `10:27` - [ ] [Intermediate Python Programming](https://www.youtube.com/playlist?list=PLQVvvaa0QuDfju7ADVp5W1GF9jVhjbX-_) by sentdex - [ ] 1. Intermediate Python Programming: Introduction `7:48` - [ ] 2. Intermediate Python Programming: String Concatenation and Formatting `13:40` - [ ] 3. Intermediate Python Programming: Argparse for CLI `10:49` - [ ] 4. Intermediate Python Programming: List Comprehension and Generator Expressions `6:52` - [ ] 5. Intermediate Python Programming: More on List Comp and Generators `15:28` - [ ] 6. Intermediate Python Programming: Timeit Module `11:28` - [ ] 7. Intermediate Python Programming: Enumerate `4:48` - [ ] 8. Intermediate Python Programming: Zip `7:23` - [ ] 9. Intermediate Python Programming: Writing Our Own Generator `11:08` - [ ] 10. Intermediate Python Programming: Multiprocessing `11:30` - [ ] 11. Intermediate Python Programming: Getting Returned Values from Processes `4:22` - [ ] 12. Intermediate Python Programming: Multiprocessing Spider Example `24:18` - [ ] 13. Intermediate Python Programming: Object Oriented Programming Introductions `11:35` - [ ] 14. Intermediate Python Programming: Creating an Environment for Our Project `11:49` - [ ] 15. Intermediate Python Programming: Many Blob Objects `8:30` - [ ] 16. Intermediate Python Programming: Object Modularity Thoughts `16:41` - [ ] 17. Intermediate Python Programming: OOP Inheritance `10:17` - [ ] 18. Intermediate Python Programming: Decorators `8:50` - [ ] 19. Intermediate Python Programming: Operator Overloading `10:19` - [ ] 20. Intermediate Python Programming: Detecting Collisions `15:20` - [ ] 21. Intermediate Python Programming: Special Methods, OOP, Iteration `13:30` - [ ] 22. Intermediate Python Programming: Logging `15:00` - [ ] 23. Intermediate Python Programming: Error Handling `6:11` - [ ] 24. Intermediate Python Programming: --str-- and --repr-- `11:32` - [ ] 25. Intermediate Python Programming: Args and Kwargs `11:58` - [ ] 26. Intermediate Python Programming: Asyncio - Asynchronous Programming with Coroutines `28:37` - [ ] [2021 Complete Python Bootcamp From Zero to Hero in Python](https://www.udemy.com/course/complete-python-bootcamp/) by Jose Portilla - [ ] 1. Course Overview - [ ] 2. Python Setup - [ ] 3. Python Object and Data Structure Basics - [ ] 4. Python Comparison Operators - [ ] 5. Python Statements - [ ] 6. Methods and Functions - [ ] 7. Milestone Project 1 - [ ] 8. Object Oriented Programming - [ ] 9. Modules and Packages - [ ] 10. Errors and Exceptions Handlings - [ ] 11. Milestone Project 2 - [ ] 12. Python Decorators - [ ] 13. Python Generators - [ ] 14. Advanced Python Modules - [ ] 15. Web Scraping with Python - [ ] 16. Working with Images with Python - [ ] 17. Working with PDFs and Spreadsheet CSV Files - [ ] 18. Emails with Python - [ ] 19. Final Capstone Python Project - [ ] 20. Advanced Python Objects and Data Structures - [ ] 21. Bonus Material - Introduction to GUIs - [ ] Build the 5 projects listed in the [5 Intermediate Python Projects](https://www.youtube.com/watch?v=o5sb8ehRSYA&ab_channel=TechWithTim) video by Tech With Tim - [ ] 1. Build a Website with Django/Flask - [ ] 2. Use a WebScraper - [ ] 3. Create a Game with PyGame - [ ] 4. Build a GUI with Tkinter/PyQt5 - [ ] 5. Robotics/Raspberry Pi Project ## Data Analysis and Visualization ##### Learn NumPy, Pandas and Matplotlib. - [ ] [Python NumPy Tutorial for Beginners](https://www.youtube.com/watch?v=QUT1VHiLmmI&ab_channel=freeCodeCamp.org) by freeCodeCamp.org `58:09` - [ ] Read the [Introduction to NumPy](https://jakevdp.github.io/PythonDataScienceHandbook/02.00-introduction-to-numpy.html) chapter from the Python Data Science Handbook by Jake VanderPlas - [ ] 1. Introduction to NumPy - [ ] 2. Understanding Data Types in Python - [ ] 3. The Basics of NumPy Arrays - [ ] 4. Computation on NumPy Arrays: Universal Functions - [ ] 5. Aggregations: Min, Max, and Everything in Between - [ ] 6. Computation on Arrays: Broadcasting - [ ] 7. Comparisons, Masks, and Boolean Logic - [ ] 8. Fancy Indexing - [ ] 9. Sorting Arrays - [ ] 10. Structured Data: NumPy's Structured Arrays - [ ] [Pandas Tutorials](https://www.youtube.com/playlist?list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS) by Corey Schafer - [ ] 1. Python Pandas Tutorial: Getting Started with Data Analysis - Installation and Loading Data `23:01` - [ ] 2. Python Pandas Tutorial: DataFrame and Series Basics - Selecting Rows and Columns `33:35` - [ ] 3. Python Pandas Tutorial: Indexes - How to Set, Reset, and Use Indexes `17:27` - [ ] 4. Python Pandas Tutorial: Filtering - Using Conditionals to Filter Rows and Columns `23:04` - [ ] 5. Python Pandas Tutorial: Updating Rows and Columns - Modifying Data within DataFrames `40:03` - [ ] 6. Python Pandas Tutorial: Add/Remove Rows and Columns from DataFrames `16:55` - [ ] 7. Python Pandas Tutorial: Sorting Data `15:40` - [ ] 8. Python Pandas Tutorial: Grouping and Aggregating - Analyzing and Exploring Your Data `49:06` - [ ] 9. Python Pandas Tutorial: Cleaning Data - Casting Data Types and Handling Missing Values `31:54` - [ ] 10. Python Pandas Tutorial: Working with Dates and Time Series Data `35:41` - [ ] 11. Python Pandas Tutorial: Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc. `32:45` - [ ] Read the [Data Manipulation with Pandas](https://jakevdp.github.io/PythonDataScienceHandbook/03.00-introduction-to-pandas.html) chapter from the Python Data Science Handbook by Jake VanderPlas - [ ] 1. Data Manipulation with Pandas - [ ] 2. Introducing Pandas Objects - [ ] 3. Data Indexing and Selection - [ ] 4. Operating on Data in Pandas - [ ] 5. Handling Missing Data - [ ] 6. Hierarchical Indexing - [ ] 7. Combining Datasets: Concat and Append - [ ] 8. Combining Datasets: Merge and Join - [ ] 9. Aggregation and Grouping - [ ] 10. Pivot Tables - [ ] 11. Vectorized String Operations - [ ] 12. Working with Time Series - [ ] 13. High-Performance Pandas: eval() and query() - [ ] [Matplotlib Tutorials](https://www.youtube.com/playlist?list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB_) by Corey Schafer - [ ] 1. Matplotlib Tutorial: Creating and Customizing Our First Plots `35:01` - [ ] 2. Matplotlib Tutorial: Bar Charts and Analyzing Data from CSVs `34:26` - [ ] 3. Matplotlib Tutorial: Pie Charts `17:02` - [ ] 4. Matplotlib Tutorial: Stack Plots `14:49` - [ ] 5. Matplotlib Tutorial: Filling Area on Line Plots `15:18` - [ ] 6. Matplotlib Tutorial: Histograms `16:36` - [ ] 7. Matplotlib Tutorial: Scatter Plots `21:24` - [ ] 8. Matplotlib Tutorial: Plotting Time Series Data `17:09` - [ ] 9. Matplotlib Tutorial: Plotting Live Data in Real-Time `20:34` - [ ] 10. Matplotlib Tutorial: Subplots `21:22` - [ ] Read the [Visualization with Matplotlib](https://jakevdp.github.io/PythonDataScienceHandbook/04.00-introduction-to-matplotlib.html) chapter from the Python Data Science Handbook by Jake VanderPlas - [ ] 1. Visualization with Matplotlib - [ ] 2. Simple Line Plots - [ ] 3. Simple Scatter Plots - [ ] 4. Visualizing Errors - [ ] 5. Density and Contour Plots - [ ] 6. Histograms, Binnings, and Density - [ ] 7. Customizing Plot Legends - [ ] 8. Customizing Colorbars - [ ] 9. Multiple Subplots - [ ] 10. Text and Annotation - [ ] 11. Customizing Ticks - [ ] 12. Customizing Matplotlib: Configurations and Stylesheets - [ ] 13. Three-Dimensional Plotting in Matplotlib - [ ] 14. Geographic Data with Basemap - [ ] 15. Visualization with Seaborn - [ ] [Python for Data Science - Course for Beginners (Learn Python, Pandas, NumPy, Matplotlib)](https://www.youtube.com/watch?v=LHBE6Q9XlzI&t=2s&ab_channel=freeCodeCamp.org) by freeCodeCamp.org `12:19:51` ## Data Preprocessing ##### Learn the basics of data preprocessing. - [ ] [Data Cleaning](https://www.kaggle.com/learn/data-cleaning) by Kaggle - [ ] 1. Handling Missing Values - [ ] 2. Scaling and Normalization - [ ] 3. Parsing Dates - [ ] 4. Character Encodings - [ ] 5. Inconsistent Data Entry - [ ] Do the [Titanic - Machine Learning from Disaster](https://www.kaggle.com/c/titanic) competition by Kaggle - [ ] Do the [Housing Prices](https://www.kaggle.com/c/home-data-for-ml-course) competition by Kaggle - [ ] [Feature Engineering](https://www.kaggle.com/learn/feature-engineering) by Kaggle - [ ] 1. Baseline Model - [ ] 2. Categorical Encodings - [ ] 3. Feature Generation - [ ] 4. Feature Selection ## Databases ##### Learn about databases. - [ ] [Intro to SQL](https://www.kaggle.com/learn/intro-to-sql) by Kaggle - [ ] 1. Getting Started with SQL and BigQuery - [ ] 2. Select, From & Where - [ ] 3. Group By, Having & Count - [ ] 4. Order By - [ ] 5. As & With - [ ] 6. Joining Data - [ ] [Advanced SQL](https://www.kaggle.com/learn/advanced-sql) by Kaggle - [ ] 1. JOINs and UNIONs - [ ] 2. Analytic Functions - [ ] 3. Nested and Repeated Data - [ ] 4. Writing Efficient Queries - [ ] [MongoDB with Python Crash Course - Tutorial for Beginners](https://www.youtube.com/watch?v=E-1xI85Zog8&ab_channel=freeCodeCamp.org) by freeCodeCamp.org `1:57:33` ## Machine Learning ##### Taking our first steps into the world of ML. - [ ] [Machine Learning](https://www.coursera.org/learn/machine-learning#syllabus) by Andrew Ng (skipping the MATLAB section) - [ ] 1. Introduction - [ ] 2. Linear Regression with One Variable - [ ] 3. Linear Algebra Review - [ ] 4. Linear Regression with Multiple Variables - [ ] 5. Logistic Regression - [ ] 6. Regularization - [ ] 7. Neural Networks: Representation - [ ] 8. Neural Networks: Learning - [ ] 9: Advice for Applying Machine Learning - [ ] 10. Machine Learning System Design - [ ] 11. Support Vector Machines - [ ] 12. Unsupervised Learning - [ ] 13. Dimensionality Reduction - [ ] 14. Anomaly Detection - [ ] 15. Recommender Systems - [ ] 16. Large Scale Machine Learning - [ ] 17. Application Example: Photo OCR - [ ] [Coursera Machine Learning MOOC by Andrew Ng Python Programming Assignments](https://github.com/dibgerge/ml-coursera-python-assignments) - [ ] Exercise 1 - [ ] Exercise 2 - [ ] Exercise 3 - [ ] Exercise 4 - [ ] Exercise 5 - [ ] Exercise 6 - [ ] Exercise 7 - [ ] Exercise 8 - [ ] Do any [Kaggle](https://www.kaggle.com/) competition - [ ] [Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning) by Kaggle - [ ] 1. Introduction - [ ] 2. Missing Values - [ ] 3. Categorical Variables - [ ] 4. Pipelines - [ ] 5. Cross-Validation - [ ] 6. XGBoost - [ ] 7. Data Leakage ## Linear Algebra and Statistics ##### Learn linear algebra and statistics. - [ ] [Linear Algebra](https://www.khanacademy.org/math/linear-algebra) on Khan Academy - [ ] 1. Vectors and Spaces - [ ] 2. Matrix Transformations - [ ] 3. Alternate Coordinate Systems (Bases) - [ ] [Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/index.htm) on MIT OpenCourseWare - [ ] Problem Set 1 - [ ] Problem Set 2 - [ ] Problem Set 3 - [ ] Problem Set 4 - [ ] Problem Set 5 - [ ] Problem Set 6 - [ ] Problem Set 7 - [ ] Problem Set 8 - [ ] Problem Set 9 - [ ] Problem Set 10 - [ ] Exam 1 - [ ] Exam 2 - [ ] Exam 3 - [ ] Final Exam - [ ] [Statistics and Probability](https://www.khanacademy.org/math/statistics-probability) on Khan Academy - [ ] 1. Analyzing Categorical Data - [ ] 2. Displaying and Comparing Quantitative Data - [ ] 3. Summarizing Quantitative Data - [ ] 4. Modeling Data Distributions - [ ] 5. Exploring Bivariate Numerical Data - [ ] 6. Study Design - [ ] 7. Probability - [ ] 8. Counting, Permutations, and Combinations - [ ] 9. Random Variables - [ ] 10. Sampling Distributions - [ ] 11. Confidence Intervals - [ ] 12. Significance Tests (Hypothesis Testing) - [ ] 13. Two-Sample Inference for the Difference Between Groups - [ ] 14. Inference for Categorical Data (Chi-Square Tests) - [ ] 15. Advanced Regression (Inference and Transforming) - [ ] 16. Analysis of Variance (ANOVA) - [ ] [Introduction to Probability and Statistics](https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/index.htm) on MIT OpenCourseWare - [ ] Problem Set 1 - [ ] Problem Set 2 - [ ] Problem Set 3 - [ ] Problem Set 4 - [ ] Problem Set 5 - [ ] Problem Set 6 - [ ] Problem Set 7 - [ ] Problem Set 8 - [ ] Problem Set 9 - [ ] Exam 1 Practice Questions I - [ ] Exam 1 Practice Questions II - [ ] Exam 1 Practice Questions: Long List - [ ] Exam 1 - [ ] Exam 2 Practice Questions - [ ] Exam 2 - [ ] Final Exam Practice Questions - [ ] Final Exam - [ ] [Deep Learning Book](https://www.deeplearningbook.org/) by Ian Goodfellow, Yoshua Bengio & Aaron Courville - [ ] 1. Introduction - [ ] 2. Linear Algebra - [ ] 3. Probability and Information Theory - [ ] 4. Numerical Computation - [ ] 5. Machine Learning Basics - [ ] 6. Deep Feedforward Networks - [ ] 7. Regularization for Deep Learning - [ ] 8. Optimization for Training Deep Models - [ ] 9. Convolutional Networks - [ ] 10. Sequence Modeling: Recurrent and Recursive Nets - [ ] 11. Practical Methodology - [ ] 12. Applications - [ ] 13. Linear Factor Models - [ ] 14. Autoencoders - [ ] 15. Representation Learning - [ ] 16. Structured Probabilistic Models for Deep Learning - [ ] 17. Monte Carlo Methods - [ ] 18. Confronting the Partition Function - [ ] 19. Approximate Inference - [ ] 20. Deep Generative Models ## Deep Learning ##### Learning about deep learning. - [ ] [Practical Deep Learning for Coders](https://course.fast.ai/) by fast.ai - [ ] Lesson 1 - [ ] Lesson 2 - [ ] Lesson 3 - [ ] Lesson 4 - [ ] Lesson 5 - [ ] Lesson 6 - [ ] Lesson 7 - [ ] Lesson 8 - [ ] [Part 2: Deep Learning from the Foundations](https://course19.fast.ai/part2) by fast.ai - [ ] Lesson 1 - [ ] Lesson 2 - [ ] Lesson 3 - [ ] Lesson 4 - [ ] Lesson 5 - [ ] Lesson 6 - [ ] Lesson 7 - [ ] Lesson 8 - [ ] Lesson 9 - [ ] Lesson 10 - [ ] Lesson 11 - [ ] Lesson 12 - [ ] Lesson 13 - [ ] Lesson 14 - [ ] [Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning) by Andrew Ng - [ ] Course 1: Neural Networks and Deep Learning - [ ] 1. Introduction to Deep Learning - [ ] 2. Neural Network Basics - [ ] 3. Shallow Neural Networks - [ ] 4. Deep Neural Networks - [ ] Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - [ ] 1. Practical Aspects of Deep Learning - [ ] 2. Optimization Algorithms - [ ] 3. Hyperparameter tuning, Batch Normalization and Programming Frameworks - [ ] Course 3: Structuring Machine Learning Projects - [ ] 1. ML Strategy (1) - [ ] 2. ML Strategy (2) - [ ] Course 4: Convolutional Neural Networks - [ ] 1. Foundations of Convolutional Neural Networks - [ ] 2. Deep Convolutional Models: Case Studies - [ ] 3. Object Detection - [ ] 4. Special applications: Face recognition & Neural style transfer - [ ] Course 5: Sequence Models - [ ] 1. Recurrent Neural Networks - [ ] 2. Natural Language Processing & Word Embeddings - [ ] 3. Sequence Models & Attention Mechanism - [ ] [DeepLearning.AI TensorFlow Developer Professional Certificate](https://www.coursera.org/professional-certificates/tensorflow-in-practice?) by Laurence Moroney - [ ] Course 1: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning - [ ] 1. A New Programming Paradigm - [ ] 2. Introduction to Computer Vision - [ ] 3. Enhancing Vision with Convolutional Neural Networks - [ ] 4. Using Real-World Images - [ ] Course 2: Convolutional Neural Networks in TensorFlow - [ ] 1. Exploring a Larger Dataset - [ ] 2. Augmentation: A Technique to Avoid Overfitting - [ ] 3. Transfer Learning - [ ] 4. Multiclass Classifications - [ ] Course 3: Natural Language Processing in TensorFlow - [ ] 1. Sentiment in Text - [ ] 2. Word Embeddings - [ ] 3. Sequence Models - [ ] 4. Sequence Models and Literature - [ ] Course 4: Sequences, Time Series and Prediction - [ ] 1. Sequences and Prediction - [ ] 2. Deep Neural Networks for Time Series - [ ] 3. Recurrent Neural Networks for Time Series - [ ] 4. Real-World Time Series Data ## Cloud for Model Deployment ##### Learn how to build, train, test, and deploy a machine learning model on AWS. - [ ] [AWS Machine Learning Specialty](https://www.youtube.com/playlist?list=PLEF5xKHm-3ZNDvdJpMCLu9xa1oDNvAmMr) by Amazon - [ ] 1. AWS Training and Certification: Machine Learning `1:31` - [ ] 2. Build, Train and Deploy Machine Learning Models on AWS with Amazon SageMaker - AWS Online `35:51` - [ ] 3. AWS re:Invent 2018: Leadership Session: Machine Learning (AIM202-L) `58:01` - [ ] 4. Machine Learning Models with TensorFlow Using Amazon SageMaker - AWS Online Tech Talks `40:16` - [ ] 5. AWS re:Invent 2018: Build & Deploy ML Models Quickly & Easily with Amazon SageMaker `57:53` - [ ] 6. AWS re:Invent 2018: CI/CD for Your Machine Learning Pipeline with Amazon SageMaker `57:13` - [ ] 7. AWS Berlin Summit 2018 - Building and Running Your First ML Application on Amazon SageMaker `52:54` - [ ] 8. Predictive Analytics with Amazon SageMaker `1:03:29` - [ ] 9. AWS re:Invent 2018: AI/ML with Data Lakes: Counterintuitive Consumer Insights in Retail `1:00:10` - [ ] 10. AWS re:Invent 2018: Industrialize Machine Learning Using CI/CD Techniques (FSV304-i) `45:34` - [ ] 11. AWS re:Invent 2018: Driving Machine Learning and Analytics Use Cases with AWS Storage (STG302) `40:16` - [ ] 12. AWS re:Invent 2018: Deep Learning Applications Using TensorFlow (AIM401-R) `1:02:29` - [ ] 13. AWS re:Invent 2017: Machine Learning State of the Union (MCL210) `1:00:55` - [ ] 14. AWS re:Invent 2017: Containerized Machine Learning on AWS (CON309) `1:03:21` - [ ] 15. AWS re:Invent 2017: Introduction to Deep Learning (MCL205) `46:17` - [ ] 16. Continuous Delivery with AWS CodePipeline and Amazon SageMaker `25:24` - [ ] 17. AWS re:Invent 2017: Best Practices for Distributed Machine Learning and Predictive A (ABD403) `1:16:16` - [ ] 18. AWS re:Invent 2017: GPS: Enhancing Customer Security Using AI/ML on AWS (GPSTEC311) `50:21` - [ ] 19. How to Wrangle Data for Machine Learning on AWS `59:24` - [ ] 20. Extract Data from Images and Videos with Amazon Rekognition (Level 300) `26:52` - [ ] 21. Exploring the Business Use Cases for Amazon Machine Learning - 2017 AWS Online Tech Talks `30:35` - [ ] 22. AWS re:Invent 2017: Orchestrating Machine Learning Training for Netflix Recommendation (MCL317) `54:21` - [ ] 23. AWS re:Invent 2017: Reinforcement Learning - The Ultimate AI (ARC320) `1:00:00` - [ ] 24. Amazon Machine Learning: Empowering Developers to Build Smart Applications `55:09` - [ ] 25. Amazon SageMaker's Built-in Algorithm Webinar Series: DeepAR Forecasting `53:41` - [ ] 26. Amazon SageMaker's Built-in Algorithm Webinar Series: Linear Learner `58:55` - [ ] 27. Amazon SageMaker's Built-in Algorithm Webinar Series: Clustering with K Means `58:52` - [ ] 28. Amazon SageMaker's Built-in Algorithm Webinar Series: Latent Dirichlet Allocation (LDA) `57:25` - [ ] 29. Amazon SageMaker's Built-in Algorithm Webinar Series: XGBoost `1:01:02` - [ ] 30. Amazon SageMaker's Built-in Algorithm Webinar Series: ResNet `55:56` - [ ] 31. Amazon SageMaker-s Built-in Algorithm Webinar Series: Blazing Text `1:14:37` - [ ] 32. AWS re:Invent 2017: NEW LAUNCH! Introducing Amazon SageMaker (MCL365) `1:02:08` - [ ] 33. Fully Managed Notebook Instances with Amazon SageMaker - a Deep Dive `16:45` - [ ] 34. Built-in Machine Learning Algorithms with Amazon SageMaker - a Deep Dive `15:38` - [ ] [Machine Learning with TensorFlow on Google Cloud Platform Specialization](https://www.coursera.org/specializations/machine-learning-tensorflow-gcp) by Google Cloud Training - [ ] Course 1: How Google does Machine Learning - [ ] 1. Introduction to Course - [ ] 2. What It Means to Be AI First - [ ] 3. How Google Does ML - [ ] 4. Inclusive ML - [ ] 5. Python Notebooks in the Cloud - [ ] 6. Summary - [ ] Course 2: Launching into Machine Learning - [ ] 1. Introduction to Course - [ ] 2. Improve Data Quality and Exploratory Data Analysis - [ ] 3. Practical ML - [ ] 4. Optimization - [ ] 5. Generalization and Sampling - [ ] 6. Summary - [ ] Course 3: Introduction to TensorFlow - [ ] 1. Introduction to Course - [ ] 2. Introduction to TensorFlow - [ ] 3. Design and Build a TensorFlow Input Data Pipeline - [ ] 4. Training Neural Networks with TensorFlow 2 and the Keras Sequential API - [ ] 5. Training Neural Networks with TensorFlow 2 and the Keras Functional API - [ ] 6. Summary - [ ] Course 4: Feature Engineering - [ ] 1. Introduction to Course - [ ] 2. Raw Data to Features - [ ] 3. Preprocessing and Feature Creation - [ ] 4. Feature Crosses - [ ] 5. TensorFlow Transform - [ ] 6. Summary - [ ] Course 5: Art and Science of Machine Learning - [ ] 1. Introduction - [ ] 2. The Art of ML - [ ] 3. Hyperparameter Tuning - [ ] 4. A Pinch of Science - [ ] 5. The Science of Neural Networks - [ ] 6. Embeddings - [ ] 7. Summary

    From user djdprogramming

  • farmersrice / saltzero

    ultimate-python, Machine learning bot for ultimate tic-tac-toe based on DeepMind's AlphaGo Zero paper. C++ and Python.

    From user farmersrice

  • larose / utt

    ultimate-python, Ultimate Time Tracker - A simple command-line time tracker written in Python

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  • lemonit66 / idea_activate

    ultimate-python, JetBrains全家桶激活 idea激活 Activator idea全家桶激活 JetBrains收费所有收费插件激活 idea收费所有收费插件激活 JetBrains 2017-2022所有版本激活 idea激活 phpstorm激活 pycharm激活 webstorm激活 AppCode激活 Goland激活 Rubymine激活 clion激活 datagrip激活 ride激活 IntelliJ IDEA PhpStorm AppCode DataGrip RubyMine WebStorm Rider CLion PyCharm GoLand DataSpell dotCover dotTrace dotMemory Symfony Support Wolfram Language Shopware Hybris Integration Material Theme UI JetForcer | The Smartest Force.com IDE AEM IDE React Native Console ANSI Highlighter Premium AEM Support SystemVerilog CodeMR JavaDoc Clean Read AutoCode for Java Database Tools and SQL for WebStorm Extra Icons Java Antidecompiler Scipio ERP Integration ZenUML support JFormDesigner (Marketplace Edition) OrchidE - Ansible Language Support Iedis 2 CMake Plus OpenAPI Editor Iceberg Atom One Dark By Mayke Generate Document OfficeFloor Android WiFiADB Laravel Idea Odoo Bitbucket Pull Requests Merge Request Integration EE - Code Review for GitLab Salesforce B2C Commerce (SFCC) MinBatis POJO to JSON Schema RDF and SPARQL BashSupport Pro MyBatis Log Cypress Support Pro Smart Jump Auto Java Code Suggestions Auto GOLang Code Suggestions Auto Ruby Code Suggestions Commit Template Auto Javascript Code Suggestions Auto PHP Code Suggestions SystemVerilog Studio SPARQL Toolset QML Editor Stryker Elasticsearch VisualGC Auto Python Code Suggestions Flutter Storm AWS Lambda Deployer PlantUML Studio Code With Me Premium JPA Buddy Firebase Rules Syntax Highlighter JPA SQL Execution God Recorder Ledger CLI Requirements Pro Regex Tool Android Package Helper Gitlab CI Circle CI Heroku Console Helper Redis Manager Zerocode Scenario Helper StormSections Sentry Integration Redis Find In Files (Configurations) Bitrise CI Qt Style Sheets Editor Database Helper Php Inspections (EA Ultimate) leetcode editor pro PHP Data Object Generator Material Theme UI High Contrast MQTT Client Notes Magnolia YAML Assistant Cap-Elasticsearch AStock Cap-Redis Maestro Simple Object Copy ElasticSearch-Admin Typed Django Template QueryFlag Nginx Configuration Pro Zookeeper-Admin API Runner NEON Nette Support Mybatis Smart Code Help Pro Code Refactor AI XSD Visualizer Spring Boot Machinet AI Unit Tests ExcelEditor Friendly Terminal WireMocha DynamoDB FastShell Jsonnet Templating Language Support PHP Houdini XSD to JSON Schema Quarkus Assistant codeCreator CIclone Database Tool Karate Material Theme UI Extras Material Theme UI Custom Theme Material Theme UI Language Additions Material Theme UI Project Frame Rancher Redis Client ScreenCodePro CodeKits Redis Operator Restful Fast Request MyBatisCodeHelperPro (Marketplace Edition) Redis

    From user lemonit66

  • mljar / plotai

    ultimate-python, PlotAI - Your Ultimate Plotting Assistant! 📊🤖 Use ChatGPT-3.5 to create plots in Python and Matplotlib directly in your Python script or notebook.

    From organization mljar

  • swati1024 / torrents

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http://www.seedpeer.me/details/6185755/TutsPlus---The-MVC-Mindser-Jeffery-Way---ICARUS.html http://www.seedpeer.me/details/5024493/TutsPlus---Venture-Into-Vim.html http://www.seedpeer.me/details/6286416/Tutsplus---Vim-for-Advanced-Users.html http://www.seedpeer.me/details/6585031/Tutsplus---WordPress-Hackers-Guide-to-the-Galaxy.html http://www.seedpeer.me/details/4848477/TutsPlus---Writing-Modular-JavaScript.html @giansalex Owner Author giansalex commented on 26 Feb 2018 • SOLID http://www.allitebooks.com/beginning-solid-principles-and-design-patterns-for-asp-net-developers/ @giansalex Owner Author giansalex commented on 7 Mar 2018 Udemy: AWS Arquitecto de Soluciones Certificado Asociado https://mega.co.nz/#!ZzhGWSAL!wuthFca0SdJBjmaP5lFX0QF6PeMsrdclKFXlZL1Rsi4 Pass: gratismas.org @giansalex Owner Author giansalex commented on 7 Mar 2018 Go lang Complete https://www.freetutorials.us/wp-content/uploads/2017/11/FreeTutorials.Us-Udemy-go-the-complete-developers-guide.torrent @GCPBigData GCPBigData commented on 15 Jul 2018 go books https://drive.google.com/open?id=1d6OsFAn8kpHCXtw0bcoYuyHqrAdGZva0 @freisrael freisrael commented on 14 Aug 2018 giansalex thanks for sharing. I am looking for learning phython with Joe Marini. It would be great if you post it. @FirstBoy1 FirstBoy1 commented on 25 May 2019 Can anyone provide this book "Getting started with Spring Framework: covers Spring 5" by " J Sharma (Author), Ashish Sarin ". Thanks in advance @okreka okreka commented on 31 May 2019 Can anyone provide "Windows Presentation Foundation Masterclass" course from Udemy. Thanks in advance @singhaltanvi singhaltanvi commented on 8 Aug 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. @kumarsreenivas051 kumarsreenivas051 commented on 9 Sep 2019 Can anyone provide "Programming languages A,B and C" course from Coursera. Thanks in advance. @BrunoMoreno BrunoMoreno commented on 11 Sep 2019 The link for the torrents in piratebay, now is .org to the correct url. @sany2k8 sany2k8 commented on 24 Sep 2019 Can anyone add this The Complete Hands-On Course to Master Apache Airflow @pharaoh1 pharaoh1 commented on 30 Sep 2019 can you pls add this course to your list https://www.udemy.com/course/advanced-python3/ @SushantDhote936 SushantDhote936 commented on 1 Oct 2019 Can you add Plural Sight CISSP @allayGerald allayGerald commented on 1 Oct 2019 open directive for lynda courses: https://drive.google.com/drive/folders/1zQan1cq1ZnqXmueRF5IqKoOtpFxl6Y4G @ezekielskottarathil ezekielskottarathil commented on 3 Oct 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. "wrong place boy" @pulkitd2699 pulkitd2699 commented on 8 Oct 2019 Does anyone has a link for 'Cyber security: Python and web applications' course? Thanks @mohanrajrc mohanrajrc commented on 19 Oct 2019 • Can anyone provide torrent file for Mastering React By Mosh Hamedani. Thanks https://codewithmosh.com/p/mastering-react @evilprince2009 evilprince2009 commented on 27 Oct 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 @nunusandio nunusandio commented on 30 Oct 2019 Can anyone post torrent file for ASP.NET Authentication: The Big Picture https://app.pluralsight.com/library/courses/aspdotnet-authentication-big-picture/table-of-contents @EslamElmadny EslamElmadny commented on 9 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 hay brother! do you have html5 game development with javascript course ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 This link is not working brother! http://www.seedpeer.me/details/4657790/Lynda.com-Building-Facebook-Applications-with-HTML-and-JavaScript.html @smithtuka smithtuka commented on 20 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? @AbdOoSaed AbdOoSaed commented on 22 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff @EslamElmadny EslamElmadny commented on 23 Dec 2019 • Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj @jedi2610 jedi2610 commented on 27 Dec 2019 Can anyone provide me with Code with Mosh's Ultimate Java Mastery Series link? plis @InnocentZaib InnocentZaib commented on 31 Dec 2019 Please provide the link of codewithmosh The ultimate data structures and algorithms Bundle the link is given below. Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms @edward-teixeira edward-teixeira commented on 1 Jan 2020 Please provide the link of codewithmosh The ultimate data structures and algorithms Bundle the link is given below. Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms Yea i'm looking for it too @kaneyxx kaneyxx commented on 1 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj could you please share the part-1 & part-3? @edward-teixeira edward-teixeira commented on 2 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? @ravisharmaa ravisharmaa commented on 7 Jan Please add this . https://www.letsbuildthatapp.com/course/AppStore-JSON-APIs @WaleedAlrashed WaleedAlrashed commented on 13 Jan This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ @Sopheakmorm Sopheakmorm commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 @EslamElmadny EslamElmadny commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 +1 @EslamElmadny EslamElmadny commented on 20 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses @mohanrajrc mohanrajrc commented on 22 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. @shihab122 shihab122 commented on 22 Jan Please give me the torrnet file or link to download The Ultimate Design Patterns @EslamElmadny EslamElmadny commented on 22 Jan • Please give me the torrnet file or link to download The Ultimate Design Patterns Waiting for it also :D @K-wachira K-wachira commented on 23 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. You are a saviour .. Altho i feel bad i cant really buy the course... its really good @msdyn95 msdyn95 commented 25 days ago • Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ @K-wachira K-wachira commented 23 days ago This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ Hey did you find this one? @edward-teixeira edward-teixeira commented 22 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? @msdyn95 msdyn95 commented 21 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. @edward-teixeira edward-teixeira commented 20 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. Found it ! https://vminhsang.name.vn/category/it-courses/codewithmosh/ @ZainA14 ZainA14 commented 16 days ago • Can someone please link me to this mosh course for torrent or direct download link https://codewithmosh.com/p/the-ultimate-full-stack-net-developer-bundle @khushiigupta khushiigupta commented 9 days ago Can any one please provide me link for jenkins so that I can learn as al as possible to join this conversation on GitHub. Already have an account? Sign in to comment © 2020 GitHub, Inc. Terms Privacy Security Status Help Contact GitHub Pricing API Training Blog About

    From user swati1024

  • yodiaditya / vim-pydjango

    ultimate-python, Ultimate VIM for Python & Django development. Get the most productivity with autocomplete, linter, pep8 checker and many features to support your Django development

    From user yodiaditya

    Home Page: https://yodiw.com

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