├── assets ├── README.md ├── ml-dl-ai.png ├── machine-learning.png ├── Modern-Data-Scientist.jpeg └── modern-data-scientist.jpeg ├── Recommendation Systems └── README.md ├── Reinforcement Learning └── README.md ├── Data Engineering ├── resources.md └── README.md ├── Beginner Roadmap └── README.md ├── NLP └── README.md ├── README.md ├── Textbooks └── README.md ├── Computer Vision └── README.md ├── Blogs └── README.md ├── Data Analyst └── README.md ├── Data Science └── README.md ├── Deep Learning └── README.md └── Machine Learning └── README.md /assets/README.md: -------------------------------------------------------------------------------- 1 | # Assets 2 | -------------------------------------------------------------------------------- /Recommendation Systems/README.md: -------------------------------------------------------------------------------- 1 | # Recommendation Systems 2 | 3 | Coming Soon! 🔔🔔🔔 4 | -------------------------------------------------------------------------------- /Reinforcement Learning/README.md: -------------------------------------------------------------------------------- 1 | # Reinforcement Learning 2 | 3 | Coming Soon! 🔔🔔🔔 4 | -------------------------------------------------------------------------------- /assets/ml-dl-ai.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/66daysofdata/Resources/HEAD/assets/ml-dl-ai.png -------------------------------------------------------------------------------- /assets/machine-learning.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/66daysofdata/Resources/HEAD/assets/machine-learning.png -------------------------------------------------------------------------------- /assets/Modern-Data-Scientist.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/66daysofdata/Resources/HEAD/assets/Modern-Data-Scientist.jpeg -------------------------------------------------------------------------------- /assets/modern-data-scientist.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/66daysofdata/Resources/HEAD/assets/modern-data-scientist.jpeg -------------------------------------------------------------------------------- /Data Engineering/resources.md: -------------------------------------------------------------------------------- 1 | # Resources for learning Data Engineering 2 | 3 | * [Data Engineering Cookbook](https://github.com/andkret/Cookbook) - Andreas Kretz 4 | -------------------------------------------------------------------------------- /Beginner Roadmap/README.md: -------------------------------------------------------------------------------- 1 | # Beginner Roadmap 2 | 3 | [![License](https://img.shields.io/badge/License-CC0%201.0%20Universal-brightgreen.svg?style=flat-square)](https://github.com/66daysofdata/License) 4 | [![Contribute](https://img.shields.io/badge/PRs-Contributions%20are%20Welcome-blue.svg?style=flat-square)](https://github.com/66daysofdata/Welcome-to-the-community) 5 | 6 | ## 1. Introduction to Programming 7 | 8 | | Index | Course Name | Link | Description | 9 | |-------|-----------------------------------------|---------------------------------------------------------------------|-----------------------------------------------------------------------------| 10 | | 1.0 | Introduction to Programming | [Watch on YouTube](https://www.youtube.com/watch?v=zOjov-2OZ0E&t=4s) | Learn the fundamentals of computer programming and science. These concepts apply to all programming languages, creating a strong foundation for future learning. | 11 | | 1.2 | R Programming Tutorial (YouTube) | [Watch on YouTube](https://www.youtube.com/watch?v=_V8eKsto3Ug&t=140s) | A beginner-friendly hands-on YouTube course to learn the Basics of Statistical Computing | 12 | | 1.3 | Understanding Data Science (DataCamp) | [Understanding Data Science](https://www.datacamp.com/courses/understanding-data-science) | An interactive course that introduces the core concepts of data science and its applications. | 13 | | 1.4 | Data Science with Python (YouTube) | [Watch on YouTube](https://www.youtube.com/watch?v=qBigTkBLU6g&list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9) | A comprehensive introduction to data science using Python, covering key concepts and practical applications. | 14 | -------------------------------------------------------------------------------- /NLP/README.md: -------------------------------------------------------------------------------- 1 | # Natural Language Processing 2 | 3 | [![License](https://img.shields.io/badge/License-CC0%201.0%20Universal-brightgreen.svg?style=flat-square)](https://github.com/66daysofdata/License) 4 | [![Contribute](https://img.shields.io/badge/PRs-Contributions%20are%20Welcome-blue.svg?style=flat-square)](https://github.com/66daysofdata/Welcome-to-the-community) 5 | 6 | 7 | 🗒️ 🖥️ 8 | 9 | What is Natural Language Processing? 10 | 11 | Natural language processing (NLP) refers to the branch of computer science—and more specifically, the branch of artificial intelligence or AI—concerned with giving computers the ability to understand text and spoken words in much the same way human beings can. Some applications used in NLP are spam detection, machine translation, virtual agents/chatbots, text summarization and social media sentiment analysis. [source](https://www.ibm.com/topics/natural-language-processing). 12 | 13 | 14 | ### 1. Introduction to Natural Language Processing 15 | 16 |
17 | 18 | | Index | Course Name | Link | Description | 19 | | ----- | ------------------- | ----| ------------ | 20 | | 1.0 | NLP with Deep Learning playlist |[course link](https://www.youtube.com/watch?v=rmVRLeJRkl4&list=PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ)| This playlist will introduce you to the foundations of the effective modern methods for Deep Learning applied to NLP. | 21 | 22 | ### 2. Intro to Natural Language Processing with fast.ai 23 | 24 | | Index | Course Name | Link | Description | 25 | | ----- | ------------------- | ----| ------------ | 26 | | 1.0 | Intro to Natural Language Processing with fast.ai 27 | |[course link](https://www.youtube.com/watch?v=cce8ntxP_XI&list=PLtmWHNX-gukKocXQOkQjuVxglSDYWsSh9)| An overview of the field of Natural Language Processing (NLP), including key areas, commonly used tools and Python libraries, debate within the field as more ML methods are adapted, and ethical issues by fast.ai Code. | 28 | 29 |
30 | 31 | 32 | 33 | 34 | -------------------------------------------------------------------------------- /Data Engineering/README.md: -------------------------------------------------------------------------------- 1 | # Data Engineer Roadmap by [Alexandra Abbas](https://github.com/alexandraabbas) 2 | 3 | ![Modern Data Engineer Roadmap 2020](https://raw.githubusercontent.com/datastacktv/data-engineer-roadmap/master/img/title.png) 4 | 5 | > Roadmap to becoming a data engineer in 2020 6 | 7 | [![Twitter](https://img.shields.io/badge/-Twitter-1DA1F2)](https://twitter.com/datastacktv) 8 | [![YouTube](https://img.shields.io/badge/-YouTube-FF0000)](https://www.youtube.com/channel/UCQSbqkMlvf_J949HDWxOt7Q) 9 | [![Website](https://img.shields.io/badge/-Website-565CD8)](https://datastack.tv/) 10 | 11 | This roadmap aims to give a **complete picture of the modern data engineering landscape** and serve as a **study guide** for aspiring data engineers. 12 | 13 | *** 14 | 15 |

Note to beginners

16 | 17 | > Beginners shouldn’t feel overwhelmed by the vast number of tools and frameworks listed here. A typical data engineer would master a subset of these tools throughout several years depending on his/her company and career choices. 18 | 19 | *** 20 | 21 | ![Data Engineer Roadmap](https://raw.githubusercontent.com/datastacktv/data-engineer-roadmap/master/img/roadmap.png) 22 | 23 | ## Nice to have 😎 24 | 25 | ![Data Engineer Roadmap Extras](https://raw.githubusercontent.com/datastacktv/data-engineer-roadmap/master/img/extras.png) 26 | 27 | ## Contributions are welcome 💜 28 | 29 | Please raise an issue to discuss your suggestions or open a Pull Request to request improvements. 30 | 31 | ## Reviewers 🔎 32 | 33 | Huge thank you to [@whydidithavetobebugs](https://github.com/whydidithavetobebugs), [@sawidis](https://github.com/sawidis), [@marclamberti](https://github.com/marclamberti) and [@mpyeager](https://github.com/mpyeager) for reviewing this roadmap. 34 | 35 | ## About us 👋🏼 36 | 37 | [datastack.tv](https://datastack.tv/) is the learning platform for the modern data stack. We create concise screencast video tutorials for data engineers. [**Browse our courses here!**](https://datastack.tv/courses.html) 38 | 39 | ## License 🗞 40 | 41 | > Copyright © 2020 Alexandra Abbas — 42 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Resources 2 | 3 | [![License](https://img.shields.io/badge/License-CC0%201.0%20Universal-brightgreen.svg?style=flat-square)](https://github.com/66daysofdata/License) 4 | [![Contribute](https://img.shields.io/badge/PRs-Contributions%20are%20Welcome-blue.svg?style=flat-square)](https://github.com/66daysofdata/Welcome-to-the-community) 5 | 6 | A comprehensive resource list covering various topics in Data Science, Machine Learning, and Deep Learning, from beginner to advanced levels. 7 | 8 | ## Beginner-Friendly Roadmap 9 | 10 | If you're new to the field, it is recommend starting with th **Beginner Roadmap** folder. This section guides you through the basics and fundamentals, preparing you for more advanced topics. 11 | 12 | ## Essential Tracks 13 | 14 | For those looking to follow a structured approach: 15 | 16 | 1. **Data Analyst**: Learn how to collect, analyze, and visualize data to gain insights. 17 | 2. **Data Science**: Discover the entire data science process, from problem formulation to model deployment. 18 | 3. **Machine Learning**: Master the art of training algorithms to make predictions or decisions based on data. 19 | 4. **Deep Learning**: Dive into the world of neural networks and learn how to build complex models. 20 | 21 | ## Specialized Topics 22 | 23 | Once you have a solid foundation, explore these specialized areas: 24 | 25 | 1. **Natural Language Processing (NLP)**: Learn how to process, understand, and generate human language. 26 | 2. **Computer Vision**: Discover image and video processing techniques to extract insights from visual data. 27 | 3. **Reinforcement Learning**: Understand how agents learn through trial and error to make decisions in complex environments. 28 | 4. **Recommendation Systems**: Develop models that suggest products or services based on user behavior and preferences. 29 | 5. **Data Engineering**: Learn how to design, build, and maintain large-scale data systems. 30 | 31 | ## Additional Resources 32 | 33 | * **Blogs**: Stay up-to-date with the latest news, trends, and insights from industry experts. 34 | * **Textbooks**: Explore comprehensive books on various topics in Machine Learning, Deep Learning, and Data Science. 35 | 36 | ## Contributing 37 | 38 | This project is open to contributions. If you'd like to add resources, correct mistakes, or suggest new topics, please feel free to submit a pull request. 39 | 40 | ## License 41 | 42 | This project is licensed under the CC0 1.0 Universal license. 43 | 44 | -------------------------------------------------------------------------------- /Textbooks/README.md: -------------------------------------------------------------------------------- 1 | # Texbook resources for all fields of Data Science 2 | 3 | 4 | **Python** 5 | 6 | 1. [Python for Everybody](http://do1.dr-chuck.com/pythonlearn/EN_us/pythonlearn.pdf) by Dr.Chuck Severance 7 | 2. [Automate The Boring Stuff](https://automatetheboringstuff.com/) by Al Sweigart 8 | 9 | **R** 10 | 11 | 1. [R for Data Science: Import, Tidy, Transform, Visualize, and Model Data](https://www.amazon.com/Data-Science-Transform-Visualize-Model/dp/1491910399)by Hadley Wickham 12 | 2. [ggplot2: Elegant Graphics for Data Analysis](https://ggplot2-book.org/) by Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen 13 | 14 | **Data Analysis** 15 | 16 | 1. [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662/ref=sr_1_3?dchild=1&keywords=Data+Analysis&qid=1602718388&sr=8-3) by Wes McKinney 17 | 18 | **Machine Learning** 19 | 20 | 1. [Hands On Machine Learning](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646) by Aurélien Géron 21 | 2. [The Hundred-Page Machine Learning Book](http://themlbook.com/wiki/doku.php) 22 | 23 | **NLP** 24 | 25 | 1. [NLP In Action](https://www.manning.com/books/natural-language-processing-in-action) by Hobson Lane, Cole Howard, & Hannes Hapke 26 | 27 | **Deep Learning** 28 | 29 | 1. [Dive into Deep Learning](https://d2l.ai/) 30 | 2. [Neural Networks for Pattern Recognition](https://www.amazon.com/Networks-Recognition-Advanced-Econometrics-Paperback/dp/0198538642) by Christopher Bishop 31 | 3. [Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks](https://www.amazon.com/Neural-Smithing-Supervised-Feedforward-Artificial/dp/0262527014) by R. Reed, R. Marksll 32 | 4. [Deep Learning (Adaptive Computation and Machine Learning series)](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) by Ian Goodfellow, Yoshua Bengio, & Aaron Courville 33 | 34 | **Data Visualization** 35 | 36 | 1. [Envisioning Information](https://www.amazon.com/Envisioning-Information-Edward-R-Tufte/dp/0961392118) by Edward Tufte 37 | 2. [Visual Explanations](https://www.amazon.com/Visual-Explanations-Quantities-Evidence-Narrative/dp/1930824157) by Edward Tufte 38 | 3. [The Visual Display of Quantitative Information](https://www.amazon.com/Visual-Display-Quantitative-Information/dp/1930824130) by Edward Tufte 39 | 4. [Beautiful Evidence](https://www.amazon.com/Beautiful-Evidence-Edward-R-Tufte/dp/1930824165) by Edward Tufte 40 | 5. [The Wall Street Journal Guide to Information Graphics](https://www.amazon.com/Street-Journal-Guide-Information-Graphics/dp/0393347281) by Dona M Wong 41 | 6. [Visualize This](https://www.amazon.com/Visualize-This-FlowingData-Visualization-Statistics/dp/0470944889) by Nathan Yau 42 | 7. [Fundamentals of Data Visualization](https://www.amazon.com/Fundamentals-Data-Visualization-Informative-Compelling/dp/1492031089) by Claus O Wilke 43 | 44 |
45 | 46 | More comming soon! 🔔🔔🔔 47 | -------------------------------------------------------------------------------- /Computer Vision/README.md: -------------------------------------------------------------------------------- 1 | # Computer Vision 2 | 3 | 4 | ## Computer Vision Roadmap 5 | 6 | [![License](https://img.shields.io/badge/License-CC0%201.0%20Universal-brightgreen.svg?style=flat-square)](https://github.com/66daysofdata/License) 7 | [![Contribute](https://img.shields.io/badge/PRs-Contributions%20are%20Welcome-blue.svg?style=flat-square)](https://github.com/66daysofdata/Welcome-to-the-community) 8 | 9 | 10 | 💻 👀 11 | 12 | What is Computer Vision? 13 | 14 | Computer Vision is a way for computers to detect objects. Computer Vision is used in text extraction, aaugmented reality, agriculture, autonomous vehicles, healthcare, sports, manufacturing, face recognition, spatial analysis and image segmentation to name the most common ones. 15 | 16 | 17 | ### 1. Introduction to Computer Vision 18 | 19 |
20 | 21 | | Index | Course Name | Link | Description | 22 | | ----- | ------------------- | ----| ------------ | 23 | | 1.0 | Intro To Computer Vision | [course link](https://www.udacity.com/course/introduction-to-computer-vision--ud810)| An introduction to Compter Vision offered by Georgia Tech. | 24 | 25 | ### 2. Computer Vision Playlist from Stanford 26 | 27 |
28 | 29 | | Index | Course Name | Link | Description | 30 | | ----- | ------------------- | ----| ------------ | 31 | | 1.0 | Complete Playlist for Computer Vision | [course link](https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PLf7L7Kg8_FNxHATtLwDceyh72QQL9pvpQ)| An introduction to Compter Vision playlist offered by Stanford University. | 32 | 33 |
34 | 35 | ### 3. Introduction to Computer Vision and Image Processing 36 | 37 | | Index | Course Name | Link | Description | 38 | | ----- | ------------------- | ----| ------------ | 39 | | 1.0 | Intro To Computer Vision and image Processing | [course link](https://www.coursera.org/learn/introduction-computer-vision-watson-opencv)| An Introduction to Computer Vision and Image Processing an online course offered by IBM on Coursera. | 40 | 41 |
42 | 43 | ### 4. Python with Computer Vision, Open CV and Deep Learning 44 | 45 | | Index | Course Name | Link | Description | 46 | | ----- | ------------------- | ----| ------------ | 47 | | 1.0 | Python with Computer Vsion Open CV and Deep Learning | [course link](https://www.udemy.com/course/python-for-computer-vision-with-opencv-and-deep-learning/)| An online course offered by Udemy using Python with Open CV for Deep Learning | 48 | 49 | ### 5. Advanced Computer Vision 50 | 51 | | Index | Course Name | Link | Description | 52 | | ----- | ------------------- | ----| ------------ | 53 | | 1.0 | Learn advanced computer vision using Python. You will learn state of the art computer vision techniques by building five projects using OpenCV and Mediapipe | [course link](https://www.youtube.com/watch?v=01sAkU_NvOY&t=216s)| Advanced Computer Vision with Python - Full Course from Free Code Camp | 54 | 55 | ### 6. Machine Vision Theory 56 | 57 | | Index | Course Name | Link | Description | 58 | | ----- | ------------------- | ----| ------------ | 59 | | 1.0 | Machine Vision course and covers the basics of machine vision theory | [course link](https://www.youtube.com/watch?v=tY2gczObpfU&list=PLUl4u3cNGP63pfpS1gV5P9tDxxL_e4W8O)| A full playlist for Machine Vision course offered by MIT. | 60 | 61 | 62 | -------------------------------------------------------------------------------- /Blogs/README.md: -------------------------------------------------------------------------------- 1 | # Blogs and Podcasts 2 | 3 | [![License](https://img.shields.io/badge/License-CC0%201.0%20Universal-brightgreen.svg?style=flat-square)](https://github.com/66daysofdata/License) 4 | [![Contribute](https://img.shields.io/badge/PRs-Contributions%20are%20Welcome-blue.svg?style=flat-square)](https://github.com/66daysofdata/Welcome-to-the-community) 5 | 6 | ### Here is a list of blogs or podcasts related to: 7 | 8 | - Data Science 9 | - Machine Learning 10 | - Deep Learning and 11 | - Artificial Intelligence 12 | 13 | | Index | Podcast Name | Main Topics | Host(s) / Network | Description | Web Link | 14 | |-------|----------------------------|----------------------------------|---------------------------------|----------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------| 15 | | 1.0 | Data Science At Home | Data Science, ML, AI | Dr. Francesco Gadaleta | Focuses on AI, machine learning, and algorithms, with industry insights and expert guests. | [datascienceathome.com](https://datascienceathome.com/) | 16 | | 2.0 | Data Skeptic | Data Science, ML, AI, Stats | Kyle Polich | Covers a wide range of data science topics, alternating between short and in-depth episodes. | [dataskeptic.com](https://dataskeptic.com/podcast) | 17 | | 3.0 | DataFramed | Data Science, Industry, Careers | Adel Nehme (DataCamp) | Interviews with data leaders on building data cultures and scaling data literacy. | [datacamp.com/podcast](https://www.datacamp.com/community/podcast) | 18 | | 4.0 | Gradient Dissent | Deep Learning, AI, ML | Lukas Biewald (Weights & Biases)| Industry leaders discuss deploying deep learning models in production. | [weightsandbiases.com/gradient-dissent](https://wandb.ai/site/resources/podcast/) | 19 | | 5.0 | Practical AI | AI, ML, LLMs, Industry Trends | Chris Benson, Daniel Whitenack | Weekly episodes on practical AI/ML, focusing on real-world implementations. | [practicalai.fm](https://practicalai.fm/) | 20 | | 6.0 | The TWIML AI Podcast | ML, AI, NLP, LLMs | Sam Charrington | Features top minds in ML/AI, with deep dives into research and industry trends. | [twimlai.com](https://twimlai.com/podcast/twimlai/) | 21 | | 7.0 | AI Engineering Podcast | Machine Learning, Business | Tobias Macey | This show is your guidebook to building scalable and maintainable AI systems. | [AI Engineering Podcast.com](https://www.aiengineeringpodcast.com/) | 22 | | 8.0 | SuperDataScience | Data Science, ML, Careers | Jon Krohn | Mixes technical know-how with career advice for aspiring data professionals. | [superdatascience.com](https://www.superdatascience.com/podcast) | 23 | | 9.0 | Data Engineering Podcast | Data Engineering, Data Science | Tobias Macey | Technical discussions on data management and engineering best practices. | [dataengineeringpodcast.com](https://www.dataengineeringpodcast.com/) | 24 | | 10.0 | O’Reilly Data Show | Data Science, Big Data, AI | O’Reilly Media | Explores big data, data science, and AI opportunities and techniques. | [oreilly data-show-podcast](https://www.oreilly.com/radar/topics/oreilly-data-show-podcast/) | 25 | | 11.0 | Adventures in Machine Learning| Machine Learning, AI | Charles M Wood | Weekly podcast covering ML breakthroughs and resources, explained in plain English. | [adventuresinmachinelearning.com](https://adventuresinmachinelearning.com/) | 26 | | 12.0 | Talking Machines | Machine Learning, AI | Neil Lawrence, Katherine Gorman | Expert interviews and industry news in machine learning. | [talkingmachines.fm](https://www.talkingmachines.fm/) | 27 | | 13.0 | Linear Digressions | ML, Data Science | Ben Jaffe, Katie Malone | Explains ML and data science concepts for a general audience. | [lineardigressions.com](https://lineardigressions.com/) | 28 | | 14.0 | NLP Highlights | NLP, Research Papers | Matt Gardner, Pradeep Dasigi | Short discussions and interviews on recent NLP research and papers. | [allenai.org/podcasts](https://allenai.org/podcasts) | 29 | | 15.0 | Chatbot Diaries | NLP, LLMs, AI | Chatbot Diaries | Focuses on language models, AI, and their impact on communication and education. | [chatbotdiaries.com](https://chatbotdiaries.com/) | 30 | | 16.0 | WordBirds | NLP, Content, Enterprise AI | Chris Willis (Axios HQ) | Explores the role of content and NLP in enterprise business and branding. | [axios.com/wordbirds](https://www.acrolinx.com/wordbirds-podcast/) | 31 | | 17.0 | NVIDIA AI Podcast | AI, ML, NLP, Deep Learning | Nvidia | The NVIDIA AI Podcast shines a light on the stories and solutions behind the most innovative changes, helping to inspire and educate listeners | [Nvidia AI Podcast](https://ai-podcast.nvidia.com/) | 32 | | 18.0 | Data Science Salon | Data Science, AI, Industry | Various | Thought leadership from data professionals at major companies. | [datascience.salon/podcast](https://www.datascience.salon/podcast/) | 33 | | 19.0 | Leaading With Data | ML, Data Science | Analytics Vidhya | Interviews with ML experts on recent developments in the field. | [Leading with Data](https://www.analyticsvidhya.com/events/leading-with-data/?ref=global_footer) | 34 | | 20.0 | Harvard Data Science Review Podcast | Data Science, Applications | Harvard Data Science Review | Case studies on how data influences the modern world. | [hdsr.mitpress.mit.edu/podcast](https://hdsr.mitpress.mit.edu/podcast) | 35 | | 21.0 | Data Stories | Data Visualization, Data Science | Enrico Bertini, Moritz Stefaner | Focuses on data visualization and storytelling in data science. | [datastori.es](https://datastori.es/) | 36 | | 22.0 | Future of Life Institute Podcast | AI Ethics, Safety, Alignment | Future of Life Institute | Explores challenges in aligning AI with human values and goals. | [Future of Life Podcast](https://futureoflife.org/project/future-of-life-institute-podcast/) | 37 | | 23.0 | Machine Learning Mastery Blog | ML, Tutorials, Careers | Jason Brownlee | Covers practical ML techniques, tutorials, and career strategies for data scientists. | [machinelearningmastery.com/blog](https://machinelearningmastery.com/blog/) | 38 | | 24.0 | The Data Exchange Podcast Presented by Gradient Flow | Deep Learning, Research | Ben Lorica | Focuses on converstaions with leaders about deep learning research, applications, and industry trends. | [The Data Exchange Podcast](https://gradientflow.com/podcast/) | 39 | | 25.0 | AI Today Podcast | AI News, Research, Trends | Kathleen Walch & Ronald Schmelzer | Discusses current AI news, breakthroughs, and industry developments. | [AI Today Podcast](https://www.cognilytica.com/aitoday/) | 40 | | 26.0 | AI in Business Podcast | AI Applications, Entrepreneurship| Daniel Fagella | Daniel Faggella interviews top AI executives from Fortune 500 firms and unicorn startups - to uncover trends, use-cases, and best practices for practical AI adoption. | [aiinbusiness.com/podcast](https://podcast.emerj.com/) | 41 | | 27.0 | Towards Data Science | Data Science, ML, AI Blog| Towards Data Science | A popular blog with articles on data science, machine learning, and AI. | [Towards Data Science Blog](https://towardsdatascience.com/) | | 42 | 43 | 44 | -------------------------------------------------------------------------------- /Data Analyst/README.md: -------------------------------------------------------------------------------- 1 | ## Data Analyst Roadmap 2 | 3 | [![License](https://img.shields.io/badge/License-CC0%201.0%20Universal-brightgreen.svg?style=flat-square)](https://github.com/66daysofdata/License) 4 | [![Contribute](https://img.shields.io/badge/PRs-Contributions%20are%20Welcome-blue.svg?style=flat-square)](https://github.com/66daysofdata/Welcome-to-the-community) 5 | 6 | 📈📉📊🧡💛💚 7 | 8 | What does a Data Analyst do? 9 | 10 | A data analyst is someone who collects, processes and performs statistical analyses of data. Data Analyst is the one who must turn the data into information and the information into insights. He or she can translate numbers and data into plain English in order to help organizations and companies understand how to make better business decisions. 11 | 12 | **Data Analyst Tasks involves:** 13 | 14 | - Data Gathering 15 | - Data Cleaning 16 | - Data Preprocessing 17 | - Data Manipulation 18 | - Data Visualization 19 | - Data Interpretation 20 | 21 | 22 | Some of the required skills to be a Data Analyst include: 23 | 24 | - Extract, Transform and Load (ETL) data 25 | - Data Mining techniques 26 | - Data Analysis 27 | - Foundations in SQL 28 | - Descriptive Statistics 29 | - Mean, Median, Mode, Range, etc. 30 | - ANOVA 31 | - Data Wrangling 32 | - Data Visualization 33 | - Data Storytelling 34 | - Creating Dashboards by using tools like: 35 | - Tableau 36 | - Power BI 37 | - Programming in Languages such as: 38 | - Python (Recommended) 39 | - R (great for statistics and visualizations) 40 | - Other popular tools include: 41 | - Apache Spark (Recommended) 42 | - RapidMiner 43 | - KNIME 44 | - Qlik 45 | - Splunk 46 | - Looker 47 | - SAS (heavily used in Health Care companies) 48 | - Excel (Recommended: accessible at most companies, great for starters!) 49 | 50 | Here below you will find some courses to get you started: 51 | 52 |
53 | 54 | --- 55 | 56 |
57 | 58 | 59 | ### 1. Introduction to Computer Science (OPTIONAL but highly Recommended) 60 | 61 |
62 | 63 | | Index | Course Name | Link | Description | 64 | | ----- | ------------------- | ----| ------------ | 65 | | 1.0 | Intro To CS by Harvard University | [course link](https://www.youtube.com/playlist?list=PLWKjhJtqVAbmGw5fN5BQlwuug-8bDmabi)| A playlist to get a good foundation of Computer Science. | 66 | 67 |
68 | 69 | ### 2. Introduction to Python 70 | 71 |
72 | 73 | | Index | Course Name | Link | Description | 74 | | ----- | ------------------- | ----| ------------ | 75 | | 1.0 | Python3 Bootcamp by Jose Portilla | [course link](https://www.udemy.com/course/complete-python-bootcamp/)| This course will get you up and running with python covering the basics, data structure, and OOP topics. | 76 | | 2.0 | Python for Data Science by DataPublishing | [course link](https://www.youtube.com/watch?v=LHBE6Q9XlzI&t=8275s) | This Python course will take you from knowing nothing to coding and analyzing data with Python using tools like Pandas, NumPy, and Matplotlib. | 77 | | 3.0 | Python Programming All-in-One Tutorial Series by Caleb Curry | [course link](https://www.youtube.com/watch?v=adNgan70iyU) | This is a 7 hour video to learn mostly everthing you need to know about Python. | 78 | 79 | 80 |
81 | 82 | ### 3. Introduction to SQL (Structured Query Language) 83 | 84 |
85 | 86 | | Index | Course Name | Link | Description | 87 | | ----- | ------------------- | ----| ------------ | 88 | | 1.0 | The Ultimate MySQL Bootcamp: Go from SQL Beginner to Expert by Colt Steel | [course link](https://www.udemy.com/course/the-ultimate-mysql-bootcamp-go-from-sql-beginner-to-expert/)| An introductory course in SQL that will take you From beginner to expert. | 89 | | 2.0 | SQL Tutorial - Full Database Course for Beginners by GiraffeAcademy | [course link](https://www.youtube.com/watch?v=HXV3zeQKqGY)| In this course you will learn database management basics and SQL using the MySQL RDBMS. The course is designed for beginners to SQL and database management systems, and will introduce common database management topics. | 90 | | 3.0 | Learn SQL Basics for Data Science Specialization | [course link](https://www.coursera.org/specializations/learn-sql-basics-data-science)| This Specialization is intended for a learner with no previous coding experience seeking to develop SQL query fluency. Through four progressively more difficult SQL projects with data science applications, you will cover topics such as SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, and more. These topics will prepare you to apply SQL creatively to analyze and explore data; demonstrate efficiency in writing queries; create data analysis datasets; conduct feature engineering, use SQL with other data analysis and machine learning toolsets; and use SQL with unstructured data sets(Financial Aid Available). | 91 | 92 |
93 | 94 | ### 4. Probabilities and Statistics 95 | 96 |
97 | 98 | | Index | Course Name | Link | Description | 99 | | ----- | ------------------- | ----| ------------ | 100 | | 1.0 | Statistics - A Full University Course by Monika Wahi | [course link](https://www.youtube.com/watch?v=xxpc-HPKN28) | This course introduces the various methods used to collect, organize, summarize, interpret and reach conclusions about data. An emphasis is placed on demonstrating that statistics is more than mathematical calculations. | 101 | | 2.0 | Probability for Data Science by Derek Banas | [course link](https://www.youtube.com/watch?v=sEte4hXEgJ8&t=1232s) | This is an hour course that will give you a great friendly introduction to the major concepts of Probability. | 102 | | 3.0 | Statistics for Data Science by Derek Banas | [course link](https://www.youtube.com/watch?v=tcusIOfI_GM&t=19s)| Another one hour video course that would give you a friendly introduction all the concepts of Statistics. | 103 | 104 | 105 | 106 | 107 |
108 | 109 | ### 5. Data Analysis 110 | 111 |
112 | 113 | | Index | Course Name | Link | Description | 114 | | ----- | ------------------- | ----| ------------ | 115 | | 1.0 | Data Analysis with Python - Full Course for Beginners by Santiago Basulto from RMOTR | [course link](https://www.youtube.com/watch?v=r-uOLxNrNk8&t=0s) | In this tutorial you'll learn the whole process of Data Analysis: reading data from multiple sources (CSVs, SQL, Excel, etc), processing them using NumPy and Pandas, visualize them using Matplotlib and Seaborn and clean and process it to create reports. | 116 | | 2.0 | Pandas Tutorial by Corey Schafer | [course link](https://www.youtube.com/watch?v=ZyhVh-qRZPA&list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS&t=0s)| This is a playlist where you'll learn how everything there is to know about Data Wrangling with Pandas. | 117 | | 3.0 | Data Science Best Practices with pandas by Data School | [tutorial link](https://www.youtube.com/watch?v=dPwLlJkSHLo&t=0s)| This is an intermediate levet tutorial about Pandas. | 118 | 119 | 120 |
121 | 122 | ### 6. Data Visualization 123 | 124 |
125 | 126 | | Index | Course Name | Link | Description | 127 | | ----- | ------------------- | ----| ------------ | 128 | | 1.0 | Data Visualization by Kaggle | [course link](https://www.kaggle.com/learn/data-visualization) | In this Kaggle tutorial you will use the Seaborn library to create line, bar, scatter plots and explore distributions with a final project at the end. | 129 | | 2.0 | Complete Seaborn, Matplotlib Crash Course by Laxmi Kant Tiwari from KGP Talkie | [course link](https://www.youtube.com/watch?v=GcXcSZ0gQps&t=0s)| A must watch tutorial covers all the most important data visualization plots. | 130 | | 3.0 | Matplotlib Tutorials by Corey Schafer | [tutorial link](https://www.youtube.com/watch?v=UO98lJQ3QGI&list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB_)| This is playlist that covers all the major graphs such as pie charts, stack plots, histograms, scatter plots , time series plots and subplots. | 131 | 132 |
133 | 134 | 135 | ### 7. Data Storytelling (Is an Art) 136 | 137 |
138 | 139 | | Index | Course Name | Link | Description | 140 | | ----- | ------------------- | ----| ------------ | 141 | | 1.0 | Develop Your Data-Driven Storytelling Chops with Four Simple Principles by Cloud Next | [tutorial link](https://www.youtube.com/watch?v=yQp3Y7nf6Hc&t=180s)| This is a presentation provided by Google Cloud about what makes a story great, effective and persuasive. | 142 | 143 |
144 | 145 | ### 8. Tableau 146 | 147 |
148 | 149 | | Index | Course Name | Link | Description | 150 | | ----- | ------------------- | ----| ------------ | 151 | | 1.0 | Hands-On Tableau Training for Data Science by SuperDataScience Team | [course link](https://www.udemy.com/course/tableau10/)| A complete course on Tableau gear towards data analysis and creating dashboards. | 152 | | 2.0 | Data Visualization with Tableau | [course link](https://www.coursera.org/specializations/data-visualization)| This Specialization, in collaboration with Tableau, is intended for newcomers to data visualization with no prior experience using Tableau. We leverage Tableau's library of resources to demonstrate best practices for data visualization and data storytelling. You will view examples from real world business cases and journalistic examples from leading media companies(Financial Aid Available). | 153 | 154 |
155 | 156 | ### 9. Power BI 157 | 158 |
159 | 160 | | Index | Course Name | Link | Description | 161 | | ----- | ------------------- | ----| ------------ | 162 | | 1.0 | Microsoft Power BI - A Complete Introduction by Manuel Lorenz, Maximilian Schwarzmüller from Academind | [course link](https://www.udemy.com/course/powerbi-complete-introduction/) | A complete introduction course to Microsoft Power BI. | 163 | 164 |
165 | 166 | ### 10. Microsoft Excel 167 | 168 |
169 | 170 | | Index | Course Name | Link | Description | 171 | | ----- | ------------------- | ----| ------------ | 172 | | 1.0 | Microsoft Excel - Excel from Beginner to Advanced | [course link](https://www.udemy.com/course/microsoft-excel-2013-from-beginner-to-advanced-and-beyond/)| This is a 17 hour couse that will teach you everything you need to know about Excel and Office 365. | 173 | 174 |
175 | 176 | ## Data Analyst Specializations 177 | 178 |
179 | 180 | | Index | Specialization Name | Link | Description | 181 | | ----- | ------------------- | ----| ------------ | 182 | | 1.0 | Data Analyst Nanodegree by Udacity | [link](https://www.udacity.com/course/data-analyst-nanodegree--nd002)| Use Python, SQL, and statistics to manipulate and prepare data for analysis, visualizations and uncover insights, communicate critical findings, and create data-driven solutions. | 183 | | 2.0 | IBM Data Analyst Professional Certificate | [link](https://www.coursera.org/professional-certificates/ibm-data-analyst) | Build job-ready skills for an in-demand career as a data analyst. This certificatin offers 8 courses where you will learn data analytics, Excel for data analysis, data visualization and how to build dashboards with excel and cognos, and SQL for data analysis.| 184 | | 3.0 | Excel to MySQL: Analytic Techniques for Business Specialization from Duke University | [link](https://www.coursera.org/specializations/excel-mysql) | In this Specialization you will learn powerful tools and methods such as Excel, Tableau, and MySQL to analyze data, create forecasts and models, design visualizations, and communicate your insights. | 185 | | 4.0 | Modern Big Data Analysis with SQL Specialization from Cloudera | [link](https://www.coursera.org/specializations/cloudera-big-data-analysis-sql) | This specialization focuses primarily in learning open source tools like Hive and Impala to learn Data Analysis for big data using SQL. | 186 | 187 |
188 | 189 | More to come! 🔔🔔🔔 190 | -------------------------------------------------------------------------------- /Data Science/README.md: -------------------------------------------------------------------------------- 1 | # Data Science Roadmap 2 | 3 | [![License](https://img.shields.io/badge/License-CC0%201.0%20Universal-brightgreen.svg?style=flat-square)](https://github.com/66daysofdata/License) 4 | [![Contribute](https://img.shields.io/badge/PRs-Contributions%20are%20Welcome-blue.svg?style=flat-square)](https://github.com/66daysofdata/Welcome-to-the-community) 5 | 6 | 🔬🖥📚💖💖💖 7 | 8 | main 9 | 10 | Data science is an interdisciplinary field that includes knowledge of: 11 | 12 | - Programming in languages such as: 13 | - Python (Recommended) 14 | - R 15 | - Scala 16 | - Julia 17 | 18 | Note: For now the majority of resources will be in the python language but others languages such as R will come soon. 19 | As well as, you don't need to learn all just pick one technology or skill. Usually, the most popular will be the one labeled as recommeded. But, feel free to choose the most convenient for you. 😀🤞 20 | 21 | - An understanding of: 22 | - Data Structures and Algorithms 23 | - Object Oriented Programming 24 | - Functional Programming 25 | 26 | - Version Control 27 | - Git (Recommended) 28 | - Gitlab 29 | - Bitbucket 30 | 31 | 32 | 33 | - Data Wrangling -> Please see the [Data Analyst Roadmap](https://github.com/66daysofdata/Resources/tree/main/Data%20Analyst) 34 | 35 | - Data Visualization -> Please see the [Data Analyst Roadmap](https://github.com/66daysofdata/Resources/tree/main/Data%20Analyst) 36 | 37 | - Visual Storytelling -> Please see the [Data Analyst Roadmap](https://github.com/66daysofdata/Resources/tree/main/Data%20Analyst) 38 | 39 | - Probability and Statistics -> Please see the [Data Analyst Roadmap](https://github.com/66daysofdata/Resources/tree/main/Data%20Analyst) 40 | 41 | - Calculus 42 | - Including Derivatives & Integrals 43 | 44 | - Linear Algebra 45 | - Matrices & Eigen Values 46 | 47 | - Relational Database Systems 48 | - MySQL 49 | - PostgreSQL 50 | 51 | - Non-Relational Databases a.k.a NoSQL 52 | - MongoDB (Recommended) 53 | - Cassandra 54 | 55 | - Machine Learning Algorithms -> Please see the [Machine Learning Roadmap](https://github.com/66daysofdata/Resources/tree/main/Machine%20Learning) 56 | - Supervised (Recommended) 57 | - Unsupervised 58 | - Clustering 59 | - Reinforcement 60 | 61 | - Deep Learning -> Please see the [Deep Learning Roadmap](https://github.com/66daysofdata/Resources/tree/main/Deep%20Learning) 62 | - Artificial Neural Networks 63 | - Convolutional Neural Networks 64 | - Recurrent Neural Networks 65 | - Deep Learning Platforms 66 | - Tensorflow 67 | - Pytorch (Recommended) 68 | - Keras 69 | - CNTK 70 | 71 | - Big Data at Scale 72 | - Spark (PySpark) 73 | - Hadoop Ecosystem 74 | - Hbase 75 | - MapReduce 76 | - ZooKeeper 77 | - Spark MLLib (Recommended) 78 | - YARN 79 | - Pig 80 | - Hive 81 | - Sqoop 82 | - Oozie 83 | 84 | - Data Engineering -> please see the [Data Engineering roadmap](https://github.com/66daysofdata/Resources/tree/main/Data%20Engineering) 85 | 86 | - Software Engineering Best Practices 87 | - Writing pythonic code by using PEP8 Standards 88 | - Testing and debugging 89 | 90 | - Machine Learning Deployment Fundamentals (ML/DevOps) 91 | - Flask --> Backend 92 | - FastAPI --> Backend 93 | - Streamlit --> Front-end (Recommended) 94 | - Using Heroku (Recommended), AWS, Azure --> Deployment platforms 95 | 96 | As a consequence, some Data Science specializations overlap and some of their subfields can be studied separately. 97 | Such is the case for Data Analyst, Machine Learning, Deep learning Engineer and as well as Data Engineers. One of the first step to take if you are a beginner is to follow the Data Analyst, then proceed to Data Science, Machine Learning, Deep Learning and finally the Data Engineering roadmap. 98 | 99 | Moreover, Here, you will find some resources to learn Data Science that are not included in the other subfields roadmaps. 100 | If you feel that you do not see what you are looking for please take a look at the other roadmap resources available. 101 | 102 |
103 | 104 | --- 105 | 106 |
107 | 108 | ### 1.0 Version Control 109 | 110 |
111 | 112 | | Index | Course Name | Link | Description | 113 | | ------ | -------------------- | ---- | ------------ | 114 | | 1.0 | A git & github crash course | course link | An introduction to git and github for beginners. | 115 | 116 |
117 | 118 | ### 2.0 Data Structures and Algorithms, Object Oriented and Functional Programming 119 | 120 |
121 | 122 | | Index | Course Name | Link | Description | 123 | | ------ | -------------------- | ----| ------------ | 124 | | 1.0 | Data Structures & Algorithms from Udacity |[course link](https://www.udacity.com/course/data-structures-and-algorithms-nanodegree--nd256)| In this course you will learn data structures and algorithms by solving 80+ practice problems. | 125 | | 2.0 | Data Stuctures by William Fiset | [course link](https://www.youtube.com/watch?v=RBSGKlAvoiM)| This course teaches data structures to beginners using high quality animations to represent the data structures visually. | 126 | | 3.0 | OOP by MIT OpenCourseWare | [course link](https://www.youtube.com/watch?v=-DP1i2ZU9gk)| Introduction to Computer Science and Programming in Python. | 127 | | 4.0 | Functional Programming from Real Python | [course link](https://www.youtube.com/watch?v=xJCPpDlk9_w&list=PLP8GkvaIxJP1z5bu4NX_bFrEInBkAgTMr)| A playlist tha focuses primarily in filter, map and reduce. | 128 | 129 |
130 | 131 | ### 3.0 Calculus 132 | 133 |
134 | 135 | | Index | Course Name | Link | Description | 136 | | ------ | -------------------- | ----| ------------ | 137 | | 1.0 | Calculus from Khan Academy | [course link](https://www.khanacademy.org/math/calculus-1)| A complete course including limits, derivates & integrals. | 138 | 139 |
140 | 141 | ### 4.0 Linear Algebra 142 | 143 |
144 | 145 | | Index | Course Name | Link | Description | 146 | | ------ | -------------------- | ----| ------------ | 147 | | 1.0 | Khan Academy Linear Algebra | [course link](https://www.youtube.com/playlist?list=PLFD0EB975BA0CC1E0) | Covers all topics in a first year college linear algebra course. | 148 | | 2.0 | Linear Algebra | [course link](https://www.youtube.com/watch?v=tVQZvJwi-ec) | A youtube course in linear algebra for machine learning. | 149 | 150 |
151 | 152 | ### 5.0 Relational Database Systems 153 | 154 |
155 | 156 | | Index | Course Name | Link | Description | 157 | | ------ | -------------------- | ----| ------------ | 158 | | 1.0 | MySQL course by Programming with Mosh | [course link](https://www.youtube.com/results?search_query=mysql)| A complete course of MySQL for beginners. | 159 | | 2.0 | Intro to Relational DB by Coursera | [course link](https://www.coursera.org/projects/introduction-to-relational-database-and-sql?=)| Getting hands-on experience working with a relational database using MySQL Workbench. | 160 | | 3.0 | Course from Amigoscode | [course link](https://www.youtube.com/watch?v=qw--VYLpxG4)| A complete introductory course for PostgreSQL. | 161 | 162 |
163 | 164 | ### 6.0 NoSQL Databases 165 | 166 |
167 | 168 | | Index | Course Name | Link | Description | 169 | | ------ | -------------------- | ----| ------------ | 170 | | 1.0 | Introduction to MongoDB by Coursera | [course link](https://www.coursera.org/learn/introduction-mongodb?=) | Introductory course that will teach the fundamentals of MongoDB, including MongoDB’s Document data model, importing data into a cluster, working with CRUD API and Aggregation Framework. | 171 | | 2.0 | Introduction to NoSQL by w3resource | [course link](https://www.w3resource.com/mongodb/nosql.php)| Introduction to NoSQL concept such as ACID, Distributed Systems, Scalability and a comparison of SQL vs NOSQL systems. | 172 | 173 |
174 | 175 | ### 7.0 Big Data at Scale 176 | 177 |
178 | 179 | | Index | Course Name | Link | Description | 180 | | ------ | -------------------- | ----| ------------ | 181 | | 1.0 | Data Analysis using PySpark by Coursera | [course link](https://www.coursera.org/projects/data-analysis-using-pyspark)| Use PySpark alongside Colab to handle distributed data processing. | 182 | | 2.0 | Spark by SimplyLearn | [course link](https://www.youtube.com/watch?v=vqEF9F7pH40) | Introductory course to Apache Spark. | 183 | | 3.0 | Hadoop tutorial by Frank Kane | [course_link](https://www.youtube.com/watch?v=DCaiZq3aBSc)| Hadoop ecosystem tutorial for beginners. | 184 | | 4.0 | Hadoop Platform and Application Framework by Coursera | [course link](https://www.coursera.org/learn/hadoop)| Complete Hadoop Tutorial for beginners. | 185 | 186 |
187 | 188 | ### 8.0 Software Engineering Best Practices 189 | 190 |
191 | 192 | | Index | Course Name | Link | Description | 193 | | ------ | -------------------- | ----| ------------ | 194 | | 1.0 | Code style tutorial by The Hitchhiker's | [tutorial link](https://docs.python-guide.org/writing/style/)| An introduction to writing more pythonic code. | 195 | | 2.0 | Pythonic code for DS | [webcast link ](https://www.youtube.com/watch?v=EihDgHxWiKk)| Michael Kennedy explains how to write pythonic code for data science. | 196 | | 3.0 | Unit testing by datacamp | [course link](https://www.datacamp.com/courses/unit-testing-for-data-science-in-python)| Learn how to write unit tests for your Data Science projects in Python using pytest. | 197 | | 4.0 | Unit testing by ProgrammingKnowledge| [tutorial link](https://www.youtube.com/watch?v=bbp_849-RZ4)| A tutorial that will introduce you to unit testing in python. | 198 | 199 |
200 | 201 | ### 9.0 Machine Learning Algorithms Deployment 202 | 203 |
204 | 205 | | Index | Course Name | Link | Description | 206 | | ------ | -------------------- | ----| ------------ | 207 | | 1.0 | ML Deployments by Krish Naik | [playlist link](https://www.youtube.com/watch?v=bjsJOl8gz5k&list=PLZoTAELRMXVOAvUbePX1lTdxQR8EY35Z1)| A playlist to learn ML deployments using Heroku, AWS and GCloud. | 208 | | 2.0 | Streamlit tutorials by JCharisTech | [course link](https://www.youtube.com/watch?v=_9WiB2PDO7k&list=PLJ39kWiJXSixyRMcn3lrbv8xI8ZZoYNZU)| A complete playlist showing the full stack for ML production. | 209 | | 2.0 | ML serving with Tiangolo FastAPI's creator. | [tutorial link](https://www.youtube.com/watch?v=1zMQBe0l1bM&t=3534s)| Build a ML API from scratch using FastAPI. | 210 | | 3.0 | Serving ML models by JCharisTech | [course link](https://www.youtube.com/watch?v=mkDxuRvKUL8&t=2s)| Serving ML models as API with FastAPI. | 211 | | 4.0 | Deploying ML models by Coursera | [course link](https://www.coursera.org/learn/deploying-machine-learning-models)| Deploying ML models. | 212 | | 5.0 | ML modes using Flask by Analytics Vidhya | [tutorial link](https://www.analyticsvidhya.com/blog/2020/04/how-to-deploy-machine-learning-model-flask/)| How to deploy ML models using flask. | 213 | 214 |
215 | 216 | ## Data Science Specializations 217 | 218 |
219 | 220 | | Index | Specialization Name | Link | Description | 221 | | ------ | -------------------- | ----| ------------ | 222 | | 1.0 | Complete Data Science Bootcamp | [link](https://www.udemy.com/course/the-data-science-course-complete-data-science-bootcamp/)| This is a great introductory course to Data Science. It is very friendly and gear towards beginners. It covers topics such as statistical analysis, numpy, pandas, matplotlib, scikit-learn and tensorflow. | 223 | | 2.0 | Data Scientist Nanodegree | [link](https://www.udacity.com/course/data-scientist-nanodegree--nd025[link]) | This specialization created by Udacity focuses in real-world data science experience with projects designed by industry experts. You will learn to run data pipelines, design experiments and deploy models to the cloud. | 224 | | 3.0 | IBM Data Science Professional Certificate | [link](https://www.coursera.org/professional-certificates/ibm-data-science) | This specialization covers courses that will provide you with the latest job-ready tools and skills, including open source tools and libraries, databases, SQL, data visualization, data analysis, statistical analysis, predictive modeling and machine learning algorithms. | 225 | | 4.0 | Applied Data Science with Python Specialization | [link](https://www.coursera.org/specializations/data-science-python) | This specialization from the University of Michigan provides hands-on projects using python toolkits such as pandas, matplotlib, scikit-lean, nltk, and networkx to gain insights from data. | 226 | 227 |
228 | 229 | More to come! 🔔🔔🔔 230 | 231 |
232 | 233 | 234 | 235 | -------------------------------------------------------------------------------- /Deep Learning/README.md: -------------------------------------------------------------------------------- 1 | # Deep Learning Roadmap 2 | 3 | [![License](https://img.shields.io/badge/License-CC0%201.0%20Universal-brightgreen.svg?style=flat-square)](https://github.com/66daysofdata/License) 4 | 5 | [![Contribute](https://img.shields.io/badge/PRs-Contributions%20are%20Welcome-blue.svg?style=flat-square)](https://github.com/66daysofdata/Welcome-to-the-community) 6 | 7 | 🎯🎯🎯🐱‍👤✨✨✨ 8 | 9 | Deep Learning also known as (deep structured learning) is a subset of Machine Learning which in turn is a subset of Artificial Intelligence, which is a part of a greater field of Computer Science. [1] 10 | 11 | It is part of a broader family of machine learning methods based on Artificial Neural Networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. [1][2] 12 | 13 | Natural Language Processing will be included in the Deep Learning roadmap because now a days is used heavily with big data. Natural Language Processing can use Deep Learning techiques such as ANNs, RNNs and LSTMs for building chatbots for instance. However, it can also be consider a separate field since it involves Linguistics, Sentiment Analysis, Information Extraction and Retrieval just to name a few. [2] 14 | 15 | Thus, Machine Learning, Deep Learning and Natural Language Processing have some overlaps. Yet, Deep Learning and Natural Language Processing rely heavily on the tools use for Machine Learning practices. 16 | 17 | - [text source 1](https://en.wikipedia.org/wiki/Deep_learning) 18 | - [text source 2](https://rutumulkar.com/blog/2016/NLP-ML/) 19 | 20 | 21 | > The below image depicts the relationship among Aritificial Intelligence, Deep Learning and Machine Learning: 22 | 23 |
24 | 25 | main 26 | 27 | [image source](https://en.wikipedia.org/wiki/Deep_learning#/media/File:AI-ML-DL.svg) 28 | 29 | 30 | Some Deep Learning algorithms include: 31 | 32 | - Artificial Neural Networks (ANNs) 33 | - Convolutional Neural Networks (CNNs) 34 | - Recurrent Neural Networks (RNNs) 35 | - Generative Adversial Networks (GANs) 36 | - Long Short-Term Memory (LSTM) 37 | - Restricted Boltzmann Machine (RBMs) 38 | - Deep Belief Networks (DBNs) 39 | 40 | Some of the most popular Deep Learning Platforms are: 41 | 42 | - Tensorflow 43 | - Keras 44 | - Pytorch (Recommended) 45 | - Google Cloud ML Engine 46 | - Microsoft Cognitive Toolkit (CNTK) 47 | - Apache Mahout 48 | - Apache mxnet 49 | - Apple Core ML 50 | - Shogun 51 | - Caffe 52 | 53 | Note: Pytorch is the more pythonic one of them all. That is why most ML/DL engineers like it more. However, Tensorflow along with Keras is another popular option. 54 | 55 | 56 | For the math needed for Deep Learning check the [data science roadmap](https://github.com/66daysofdata/Resources/tree/main/data_science) and especifically see calculus and linear algebra. 57 | 58 |
59 | 60 | --- 61 | 62 |
63 | 64 | ## Introduction to Deep Learning 65 | 66 |
67 | 68 | | Index | Course Name | Link | Description | 69 | | ------ | -------------------- | ---- | ------------ | 70 | | 1.0 | Friendly intro to DL and NN | [tutorial link](https://www.youtube.com/watch?v=BR9h47Jtqyw) | This lecture explain what is deep learning and neural networks in an easy to understand manner. | 71 | | 2.0 | Deep Learning Basics by MIT |[course link](https://www.youtube.com/watch?v=O5xeyoRL95U&t=8s) | An introductory lecture for MIT course on the basics of deep learning including a few key ideas, subfields, and the big picture of why neural networks. | 72 | | 3.0 | Introduction to Deep Learning | [course link](https://www.coursera.org/learn/intro-to-deep-learning) | The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. | 73 | 74 |
75 | 76 | ### Artificial Neural Networks (ANNs) 77 | 78 |
79 | 80 | | Index | Course Name | Link | Description | 81 | | ------ | -------------------- | ---- | ------------ | 82 | | 1.0 | What is an ANN? by 3Blue1Brown | [tutorial link](https://www.youtube.com/watch?v=aircAruvnKk) | This tutorial covers the main concepts of what a neural network is. | 83 | | 2.0 | Neural Networks and DL by deeplearning.ai | [course link](https://www.coursera.org/learn/neural-networks-deep-learning) | In this course, you will learn the foundations of deep learning such ans ANNs, Backpropagating by using python. | 84 | 85 |
86 | 87 | ### Convolutional Neural Networks (CNNs) 88 | 89 |
90 | 91 | | Index | Course Name | Link | Description | 92 | | ------ | -------------------- | ---- | ------------ | 93 | | 1.0 | A friendly intro to CNNs and Images by Luis Serrano | [tutorial link](https://www.youtube.com/watch?v=2-Ol7ZB0MmU) | A friendly introduction to CNNs and Image Recognition. | 94 | | 2.0 | Convolutional Neural Networks by MIT | [course link](https://www.youtube.com/watch?v=iaSUYvmCekI) | An introduction to CNNs for Computer Vision. | 95 | 96 |
97 | 98 | ### Recurrent Neural Networks (RNNs) 99 | 100 |
101 | 102 | | Index | Course Name | Link | Description | 103 | | ------ | -------------------- | ---- | ------------ | 104 | | 1.0 | A friendly intro to RNNs by Luis Serrano | [tutorial link](https://www.youtube.com/results?search_query=recurrent+neural+network) | A friendly explanation of how computers predict and generate sequences, based on Recurrent Neural Networks. | 105 | | 2.0 | Introduction to DL: RNNs by MIT | [course link](https://www.youtube.com/watch?v=SEnXr6v2ifU&t=2s) | An introduction to Deep Learning primarily focusing on what are RNNs. | 106 | | 3.0 | RNNs with Keras by Coursera | [course link](https://www.coursera.org/projects/simple-recurrent-neural-network-keras) | This course involves training a sequence to sequence RNN model to be able to solve simple addition equations given in string format. | 107 | 108 |
109 | 110 | ### Generative Adversial Networks (GANs) 111 | 112 |
113 | 114 | | Index | Course Name | Link | Description | 115 | | ------ | -------------------- | ---- | ------------ | 116 | | 1.O | A friendly intro to GANs by Luis Serrano | [tutorial link](https://www.youtube.com/watch?v=8L11aMN5KY8&t=2s)| In this tutorial (with code included) Luis builds a pair of ONE-layer GANs which will generate some simple 2x2 images (faces). | 117 | | 2.0 | Deep Generative Modeling by MIT | [course link](https://www.youtube.com/watch?v=rZufA635dq4) | An introduction to Deep Learning where GANs are study as well as latent variable models, autoencoders and Debiasing with VAEs. | 118 | | 3.0 | Intro to Generative Models by Stanford | [course link](https://www.youtube.com/watch?v=5WoItGTWV54&t=0s) | A course that discusses generative modeling as a form of unsupervised learning, autoregressive PixelRNN and PixelCNN models, traditional and variational autoencoders (VAEs), and generative adversarial networks (GANs). | 119 | | 4.0 | Build Basic Generating Adversarial Networks (GANs) by DeepLearning.ai | [course link](https://www.coursera.org/learn/build-basic-generative-adversarial-networks-gans) | This course emphasizes on GANs and their applications in order to understand the intuition behind the fundamental components of GANs. | 120 | | 5.0 | Generate Synthetic mages with DCGANs in Keras by Coursera | [course link](https://www.coursera.org/projects/generative-adversarial-networks-keras) | In this hands-on project, you will learn about Generative Adversarial Networks (GANs) and you will build and train a Deep Convolutional GAN (DCGAN) with Keras to generate images of fashionable clothes. | 121 | 122 |
123 | 124 | ### Long Short-Term Memory (LSTM) 125 | 126 |
127 | 128 | | Index | Course Name | Link | Description | 129 | | ------ | -------------------- | ---- | ------------ | 130 | | 1.0 | Long Short Term Memory concepts by UCBerkeley | [course link](https://www.youtube.com/watch?v=qI7oqgbJXJA&t=0s) | A short video explaining the concepts and history of LSTMs. | 131 | | 2.0 | LSTMs Explained easily by Valerio Velardo | [tutorial link](https://www.youtube.com/watch?v=eCvz-kB4yko&t=0s) | In this video, you'll learn how Long Short Term Memory (LSTM) networks work. We'll take a look at LSTM cells both architecturally and mathematically, and compare them against simple RNN cells. | 132 | | 3.0 | Machine Translation and Advanced Recurrent LSTMs and GRUs by Stanford | [course link](https://www.youtube.com/watch?v=QuELiw8tbx8&t=2s) | This lecture recaps the most important concepts and equations covered so far followed by machine translation and RNN models. | 133 | | 4.0 | Named Entity Recognition using LSTMs with Keras by Coursera | [course link](https://www.coursera.org/projects/named-entity-recognition-lstm-keras-tensorflow) | Build and train a bi-directional LSTM with Keras and solve the Named Entity Recognition (NER) problem with LSTMs. | 134 | | 5.0 | Fake News Detection with Machine Learning by Coursera | [course link](https://www.coursera.org/projects/nlp-fake-news-detector) | In this hands-on project, we will train a Bidirectional Neural Network and LSTM based deep learning model to detect fake news from a given news corpus. | 135 | 136 |
137 | 138 | ### Restricted Boltzmann Machine (RBMs) 139 | 140 |
141 | 142 | | Index | Course Name | Link | Description | 143 | | ------ | -------------------- | ---- | ------------ | 144 | | 1.0 | A friendly intro to RBMs by Luis Serrano | [tutorial link](https://www.youtube.com/watch?v=Fkw0_aAtwIw) | A simple introduction to Restricted Boltzmann Machines (RBM) and their training process, using a real-life example with people and pets. | 145 | | 2.0 | RBMs by Geoffrey Hinton | [lecture link](https://www.youtube.com/watch?v=UcAWwySuUZM) | A short video explaninng the main concepts of Restricted Boltzmann Machines. | 146 | | 3.0 | Learning Restricted Boltzmann Machines by MIT | [course link](https://www.youtube.com/watch?v=EpU7wZwoe9A) | A course on RBMs explaining the mathematical concepts behind it. | 147 | | 4.0 | Intro to Deep Learning by Carnegie Mellon | [course link](https://www.youtube.com/watch?v=-FvgowlYGPg) | The content presented in this lectures involves concepts for RBMs and DBMs. | 148 | | 5.0 | Rerstricted Blotzmann Machine Fundamentals by Hugo Larochelle | [playlist link](https://www.youtube.com/watch?v=p4Vh_zMw-HQ&list=PLKfjiRA2lC0rA5RTlxZVw_rryxUTB6k_z&t=0s) | A playlist explaining the concepts of RBMs, autoencoders and sparse coding models. | 149 | 150 |
151 | 152 | ### Deep Belief Networks (DBNs) 153 | 154 |
155 | 156 | | Index | Course Name | Link | Description | 157 | | ------ | -------------------- | ---- | ------------ | 158 | | 1.0 | Deep learning - Deep Belief networks by Hugo Larochelle | [tutorial link](https://www.youtube.com/watch?v=vkb6AWYXZ5I) | A video that explains the foundations behind Deep Belief Networks. | 159 | | 2.0 | Deep Belief Networks concepts by deeplearning.net | [tutorial link](http://deeplearning.net/tutorial/DBN.html) | This tutorial in DBNs, justifying greedy-layer, wise pre-trainning and implemtation. | 160 | 161 |
162 | 163 | ## Deep Learning Specializations 164 | 165 |
166 | 167 | | Index | Specialization Name | Link | Description | 168 | | ------ | -------------------- | ---- | ------------ | 169 | | 1.0 | Deep Learning Specialization | [link](https://www.coursera.org/specializations/deep-learning) | Five courses to learn the foundations of Deep Learning offered by Andrew Ng of deeplearning.ai | 170 | | 2.0 | Deep Learning Nanodegree | [link](https://www.udacity.com/course/deep-learning-nanodegree--nd101) |Created by Udactity where you'll implement ANNs, CNNs and RNNs for image recognition, sequence generation and image generation. | 171 | | 3.0 | Deep Learning A-Z™: Hands-On Artificial Neural Networks | [link](https://www.udemy.com/course/deeplearning/)| A complete intuitive behind ANNs, CNNs, AutoEncoders and RBMs created by the SuperDataScience Team. | 172 | | 4.0 | ColumbiaX's Artificial Intelligence | [link](https://www.edx.org/micromasters/columbiax-artificial-intelligence) | Covers a solid understanding of the guiding principles of AI with an emphasis in applications of AI in fields of robotics, vision and physical simulation. | 173 | | 5.0 | AI for Medicine Specialization | [link](https://www.coursera.org/specializations/ai-for-medicine) | These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. It focuses on medical diagnosis, medical prognosis and medical treatment by deeplearning.ai | 174 | | 6.0 | Advanced Machine Learning Specialization | [link](https://www.coursera.org/specializations/aml) | This specialization from NSU gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. | 175 | | 7.0 | Computer Vision Nanodegree | [link](https://www.udacity.com/course/computer-vision-nanodegree--nd891) | This specialization from Udacity gives a great skills in analyzing images, object recognition and object tracking and location. | 176 | 177 | 178 |
179 | 180 | More to come! 🔔🔔🔔 181 | 182 |
183 | 184 | -------------------------------------------------------------------------------- /Machine Learning/README.md: -------------------------------------------------------------------------------- 1 | # Machine Learning Engineering Roadmap 2 | 3 | [![License](https://img.shields.io/badge/License-CC0%201.0%20Universal-brightgreen.svg?style=flat-square)](https://github.com/66daysofdata/License)[![Contribute](https://img.shields.io/badge/PRs-Contributions%20are%20Welcome-blue.svg?style=flat-square)](https://github.com/66daysofdata/Welcome-to-the-community) 4 | 5 | ⚙⚙⚙🚀🚀🏗🏗 6 | 7 | 8 | main 9 | 10 | [image source](https://www.oreilly.com/library/view/java-deep-learning/9781788997454/a8fce962-51dd-4e29-a7f9-9bf4fd245b1d.xhtml) by Rezaul Karim. 11 | 12 | --- 13 | 14 | 15 | Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. [view source](https://en.wikipedia.org/wiki/Machine_learning) 16 | 17 | Cateogories of Machine Learning include Supervised, Unsupervised, Reinforcement and Clustering learning. 18 | 19 | Supervised learning algorithms can be divided into two categories: 20 | 21 | - Regression 22 | - Classification 23 | 24 | Unsupervised learning can be divided into two categories: 25 | 26 | - Dimensionality Reduction 27 | - Clustering 28 | 29 | Another popular type of Machine Learning is Reinforcement Learning which is used for learning tasks or for game AI. 30 | See the diagram above. 31 | 32 | Some common machine learning algorithms are: 33 | 34 | - Linear Regression 35 | - Logistic Regression 36 | - Decision Trees 37 | - [XGBoost](https://xgboost.readthedocs.io/en/latest/) 38 | - [LightGBM](https://lightgbm.readthedocs.io/en/latest/) 39 | - [CatBoost](https://catboost.ai/) 40 | - Support Vector Machines (SVM) 41 | - Naive Bayes 42 | - Ensemble Methods and Random Forest 43 | - KNN K-Means 44 | - Dimensionality Reduction Algorithms 45 | - Principal Component Analysis 46 | - Singular Value Decomposition 47 | - Reinforcement Learning 48 | 49 | You can check out [scikit-learn documentation](https://scikit-learn.org/stable/user_guide.html) for a complete guide of the most popular machine learning algorithms. 50 | 51 | For statistics check the [data analyst roadmap](https://github.com/66daysofdata/Resources/tree/main/Data%20Analyst), for calculus and linear algebra see the [data science roadmap](https://github.com/66daysofdata/Resources/tree/main/Data%20Science), and for other specializations such as neural networks, natural language processing, reinforcement learning and computer vision please see the [deep learning roadmap](https://github.com/66daysofdata/Resources/tree/main/Deep%20Learning). 52 | 53 | Here, we will focus mainly in supervised and unsupervised learning algorithms, machine learning techniques such as feature engineering and feature selection, model selection and evaluation. 54 | 55 | **Supervised vs. Unsupervised Machine Learning** 56 | 57 | | Parameters | Supervised machine learning technique | Unsupervised machine learning technique | 58 | | ---------- | -------------------- | ---------------------| 59 | |Input Data | Algorithms are trained using labeled data.| Algorithms are used against data which is not labeled. | 60 | Computational Complexity | Supervised learning is a simpler method. | Unsupervised learning is computationally complex. | 61 | Accuracy | Highly accurate and trustworthy method. | Less accurate and trustworthy method. | 62 | 63 | [source](https://www.guru99.com/unsupervised-machine-learning.html) 64 | 65 |
66 | 67 | --- 68 | 69 |
70 | 71 | ## Introduction to Machine Learning 72 | 73 | | Index | Course Name | Link | Description | 74 | | ------ | -------------------- | ---- | ------------ | 75 | | 1.0 | Intro to ML by Udacity | [course link](https://www.udacity.com/course/intro-to-machine-learning--ud120) | An introduction to machine learning. | 76 | | 2.0 | What is ML by Coursera | [course link](https://www.coursera.org/lecture/machine-learning/what-is-machine-learning-Ujm7v) | An introduction to some ML algorithms by Andrew Ng. | 77 | | 3.0 | Intro to ML by Google | [course link](https://developers.google.com/machine-learning/crash-course/ml-intro) | A Machine Learning crash course. | 78 | 79 |
80 | 81 | ## Supervised Learning 82 | 83 | ### 1.0 Linear Regression 84 | 85 |
86 | 87 | | Index | Course Name | Link | Description | 88 | | ------ | -------------------- | ---- | ------------ | 89 | | 1.0 | Linear Regression by Yale | [course link](http://www.stat.yale.edu/Courses/1997-98/101/linreg.htm) | A brief explanation of what is Linear Regression. | 90 | | 2.0 | Supervised Learning by Coursera | [course link](https://www.coursera.org/learn/supervised-learning-regression) | This course introduces you to one of the main types of modeling families of supervised Machine Learning: Regression. | 91 | | 3.0 | Linear Regression by KGP Talkie | [tutorial link](https://kgptalkie.com/linear-regression-with-python-machine-learlearning-kgp-talkie/) | House price prediction using Linear Regression. | 92 | 93 |
94 | 95 | ### 2.0 Logistic Regression 96 | 97 |
98 | 99 | | Index | Course Name | Link | Description | 100 | | ------ | -------------------- | ---- | ------------ | 101 | | 1.0 | Logistic Regression by Luis Serrano | [tutorial link](https://www.youtube.com/watch?v=jbluHIgBmBo&t=1143s) | Logistic regression concepts: A friendly introduction. | 102 | | 2.0 | Machine Learning class | [lecture link](https://www.youtube.com/watch?v=GnkDzIOxfzI) | A lecture in logistic regression from Cornell University. | 103 | | 3.0 | Linear Regression by Coursera | [course link](https://www.coursera.org/learn/supervised-learning-regression) | An introduction to linear regression, ridge, lasso and elastic net techniques. | 104 | 105 |
106 | 107 | ### 3.0 Decision Trees 108 | 109 |
110 | 111 | | Index | Course Name | Link | Description | 112 | | ------ | -------------------- | ---- | ------------ | 113 | | 1.0 | Decision Tree tutorial | [tutorial link](https://www.youtube.com/watch?v=LDRbO9a6XPU) | Building a decision tree classifier from scratch. | 114 | | 2.0 | Decision Tree tutorial by datacamp | [tutorial link](https://www.datacamp.com/community/tutorials/decision-tree-classification-python) | Learn Decision Tree Classification, attribute selection measures, and how to build and optimize Decision Tree Classifier using Python Scikit-learn package. | 115 | 116 |
117 | 118 | ### 4.0 Support Vector Machines 119 | 120 |
121 | 122 | | Index | Course Name | Link | Description | 123 | | ------ | -------------------- | ---- | ------------ | 124 | | 1.0 |SVM by coursera| [course link](https://www.coursera.org/projects/support-vector-machines-in-python) | Support Vector Machine for classification using scikit-learn and the Radial Basis Function (RBF) Kernel. | 125 | 126 |
127 | 128 | ### 5.0 Naive Bayes 129 | 130 |
131 | 132 | | Index | Course Name | Link | Description | 133 | | ------ | -------------------- | ---- | ------------ | 134 | | 1.0 | Language classification with Naive Bayes | [course link](https://www.coursera.org/projects/language-classification)| Clean and preprocess data for language classification. You will learn some theory behind Naive Bayes Modeling. | 135 | 136 |
137 | 138 | ### 6.0 Random Forest 139 | 140 |
141 | 142 | | Index | Course Name | Link | Description | 143 | | ------ | -------------------- | ---- | ------------ | 144 | | 1.0 | Understanding Random Forest by datacamp | [tutorial link](https://www.datacamp.com/community/tutorials/random-forests-classifier-python) | Learn about Random Forest and built your own model in python. | 145 | | 2.0 | Random Forest Aplication | [course link](https://www.youtube.com/watch?v=zFGPjRPwyFw) | Applications of random forests: kinect, object detection and regression by Nando de Freitas. | 146 | | 3.0 | Random Forest lecture| [course link](https://www.youtube.com/watch?v=4EOCQJgqAOY) | An introduction to Random Forest by Cornell University. | 147 | 148 |
149 | 150 | ### 7.0 Ensemble Methods 151 | 152 |
153 | 154 | | Index | Course Name | Link | Description | 155 | | ------ | -------------------- | ---- | ------------ | 156 | | 1.0 | Decision trees & ensemble methods | [course link](https://www.youtube.com/watch?v=wr9gUr-eWdA) | A lecture including concepts for ensemble methods, bagging and boosting. | 157 | | 2.0 | Ensemble Learning in Python | [tutorial link](https://www.datacamp.com/community/tutorials/ensemble-learning-python) | A tutorial to learn what ensemble is and how it improves the performance of a machine learning model. | 158 | | 3.0 | How to win a competition | [course link](https://www.coursera.org/learn/competitive-data-science) | In this course you will learn various techniques such as data leakeage, featuring engineering and using XGBoost and LightGBM. | 159 | 160 |
161 | 162 | ## Unsupervised Learning 163 | 164 | ### 1.0 KNN K-Means 165 | 166 |
167 | 168 | | Index | Course Name | Link | Description | 169 | | ------ | -------------------- | ---- | ------------ | 170 | | 1.0 | kmeans clustering by datacamp | [tutorial link](https://www.datacamp.com/community/tutorials/k-means-clustering-python) | A tutorial that will walk you in understanding the concepts of kmeans clustering. | 171 | | 2.0 | k-means by coursera | [course link](https://www.coursera.org/projects/scikit-learn-k-means-clustering-image-compression) | Apply the k-means clustering unsupervised learning algorithm using scikit-learn to build an image compression application with interactive controls. | 172 | | 3.0 | Interactive visualization of K-means clustering | [blog link](https://www.naftaliharris.com/blog/visualizing-k-means-clustering/) | An interactive blog that will take your input and help you visualize update of centroids and reassigning of points of kmeans clustering. | 173 | 174 |
175 | 176 | ### 2.0 Dimensionality Reduction 177 | 178 |
179 | 180 | | Index | Course Name | Link | Description | 181 | | ------ | -------------------- | ---- | ------------ | 182 | | 1.0 | Dimensionality Reduction using an Autoencoder | [course link](https://www.coursera.org/projects/dimensionality-reduction-autoencoder-python) | Lean how to How to extract the encoder portion from a trained model, and reduce dimensionality of your input data. | 183 | | 2.0 | PCA with numpy | [course link](https://www.coursera.org/projects/principal-component-analysis-numpy) | This course covers how to implement Principal Component Analysis (PCA) from scratch with NumPy and Python. | 184 | | 3.0 | PCA in Python | [tutorial link](https://www.datacamp.com/community/tutorials/principal-component-analysis-in-python) | Learn about PCA and how it can be leveraged to extract information from the data without any supervision using two popular datasets: Breast Cancer and CIFAR-10. | 185 | 186 |
187 | 188 | ## Feature Engineering and Selection 189 | 190 | ### 1.0 Feature Engineering 191 | 192 |
193 | 194 | | Index | Course Name | Link | Description | 195 | | ------ | -------------------- | ---- | ------------ | 196 | | 1.0 | ML with Kaggle | [tutorial link](https://www.datacamp.com/community/tutorials/feature-engineering-kaggle) | Learn how feature engineering can help you to up your game when building machine learning models in Kaggle: create new columns, transform variables and more. | 197 | | 2.0 | Feature Engineering by Kaggle | [course ling](https://www.kaggle.com/learn/feature-engineering) | Discover the most effective way to improve your models by building a baseline model, categorical encoding and feature generation. | 198 | | 3.0 | Feature Engineering and Bias Detection | [course link](https://www.coursera.org/learn/ibm-ai-workflow-feature-engineering-bias-detection) | A course dedicated to learn best practices for feature engineering, handling class imbalances and detecting bias in the data. | 199 | 200 |
201 | 202 | ### 2.0 Feature Selection 203 | 204 |
205 | 206 | | Index | Course Name | Link | Description | 207 | | ------ | -------------------- | ---- | ------------ | 208 | | 1.0 | Beginner's guide to Feature Selection | [tutorial link](https://www.datacamp.com/community/tutorials/feature-selection-python) | Learn about the basics of feature selection and how to implement and investigate various feature selection techniques in Python. | 209 | | 2.0 | ML Feature Selection | [course link](https://www.coursera.org/projects/machine-learning-feature-selection-in-python) | Learn basic principles of feature selection and extraction, and how this can be implemented in Python using RFE and K-Best. | 210 | 211 |
212 | 213 | ## Model Selection and Evaluation 214 | 215 |
216 | 217 | | Index | Course Name | Link | Description | 218 | | ------ | -------------------- | ---- | ------------ | 219 | | 1.0 | Model Selection and Evaluation | [tutorial link](https://sebastianraschka.com/blog/2016/model-evaluation-selection-part1.html)| A tutorial explaining model evaluation, model selection, and algorithm selection. | 220 | | 2.0 | Model Evaluation | [course link](https://www.youtube.com/watch?v=EUiIydNBIbE) | This course covers an introduction to metrics for binary classification, multiclass and regression, ROC curves, precision-recall curves. | 221 | 222 |
223 | 224 | ## Machine Learning Algorithms Implementation from Scratch 225 | 226 |
227 | 228 | | Index | Course Name | Link | Description | 229 | | ------ | -------------------- | ---- | ------------ | 230 | | 1.0 | ML Internals | [repo link](https://github.com/rushter/MLAlgorithms) | Minimal and clean examples of machine learning algorithms implementations from scratch. | 231 | 232 |
233 | 234 | ## Machine Learning Specializations 235 | 236 |
237 | 238 | | Index | Specialization Name | Link | Description | 239 | | ------ | -------------------- | ---- | ------------ | 240 | | 1.0 | Machine Learning A-Z™: Hands-On Python & R In Data Science | [link](https://www.udemy.com/course/machinelearning/)| This specialization from the SuperDataScience Team will give a great intuition of many Machine Learning models. | 241 | | 2.0 | Machine Learning Engineering Nanodegree | [link](https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009t) | An specialization from Udacity will teach you advanced machine learning techniques and algorithms as well as how to package and deploy your models to a production environment. | 242 | | 3.0 | Machine Learning Specialization | [link](https://www.coursera.org/specializations/machine-learning) | This Specialization from leading researchers at the University of Washington will teach you how to build Intelligent Applications. Master machine learning fundamentals in four hands-on courses. | 243 | | 4.0 | Machine Learning with TensorFlow on Google Cloud Platform Specialization | [link](https://www.coursera.org/specializations/machine-learning-tensorflow-gcp) | Learn Machine Learning with Google Cloud. In this specialization you will learn how google does Machine Learning with an intro to TensorFlow and Feature Engineering. | 244 | 245 |
246 | 247 | More to come! 🔔🔔🔔 248 | 249 |
250 | 251 | 252 | --------------------------------------------------------------------------------