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https://github.com/djdprogramming/adfa2

# 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
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URL: https://github.com/djdprogramming/adfa2
Owner: djdprogramming
Created: 2021-01-08T04:25:25.000Z (about 4 years ago)
Default Branch: main
Last Pushed: 2021-01-08T04:25:27.000Z (about 4 years ago)
Last Synced: 2023-10-25T18:29:31.781Z (about 1 year ago)
Size: 0 Bytes
Stars: 11
Watchers: 2
Forks: 0
Open Issues: 0

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