├── Hack_the_Hackathon_1.0 ├── readme.md └── images │ └── random.py ├── _config.yml ├── Art of Time Series Analysis and Forecasting ├── src │ └── README.md └── README.md ├── .github └── workflows │ └── jekyll-gh-pages.yml └── README.md /Hack_the_Hackathon_1.0/readme.md: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-minimal 2 | -------------------------------------------------------------------------------- /Hack_the_Hackathon_1.0/images/random.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /Art of Time Series Analysis and Forecasting/src/README.md: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /.github/workflows/jekyll-gh-pages.yml: -------------------------------------------------------------------------------- 1 | # Sample workflow for building and deploying a Jekyll site to GitHub Pages 2 | name: Deploy Jekyll with GitHub Pages dependencies preinstalled 3 | 4 | on: 5 | # Runs on pushes targeting the default branch 6 | push: 7 | branches: ["main"] 8 | 9 | # Allows you to run this workflow manually from the Actions tab 10 | workflow_dispatch: 11 | 12 | # Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages 13 | permissions: 14 | contents: read 15 | pages: write 16 | id-token: write 17 | 18 | # Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued. 19 | # However, do NOT cancel in-progress runs as we want to allow these production deployments to complete. 20 | concurrency: 21 | group: "pages" 22 | cancel-in-progress: false 23 | 24 | jobs: 25 | # Build job 26 | build: 27 | runs-on: ubuntu-latest 28 | steps: 29 | - name: Checkout 30 | uses: actions/checkout@v3 31 | - name: Setup Pages 32 | uses: actions/configure-pages@v3 33 | - name: Build with Jekyll 34 | uses: actions/jekyll-build-pages@v1 35 | with: 36 | source: ./ 37 | destination: ./_site 38 | - name: Upload artifact 39 | uses: actions/upload-pages-artifact@v1 40 | 41 | # Deployment job 42 | deploy: 43 | environment: 44 | name: github-pages 45 | url: ${{ steps.deployment.outputs.page_url }} 46 | runs-on: ubuntu-latest 47 | needs: build 48 | steps: 49 | - name: Deploy to GitHub Pages 50 | id: deployment 51 | uses: actions/deploy-pages@v2 52 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Feel free to reach out for a talk or interview : hriskikesh.yadav332@gmail.com 2 | 3 | ## Talks 4 | 5 | 6 | * [Exploring the World of LLMs with KerasNLP]() Keras Community Day, Mumbai - September 23, 2023 7 | * [Art of Time Series Analysis and Forecasting]() SuperAI Connect - March 18, 2023 8 | * [World of AutoML in GCP and Vision AI]() GDSC WOW - April 8, 2023 9 | * [AI Tools and ChatGPT for Academia]() Faculty Development Program 10 | * [Generative AI Workshop]() Zephyr, TCET - 2023 11 | * [How MindsDB is impacting the ML World](https://www.youtube.com/watch?v=YngjfwJLplA) 12 | * [Explore the world of NLP using Mindsdb and Huggingface](https://www.youtube.com/watch?v=2I96528q2qE) 13 | 14 | 15 | ## Planned Talks 16 | * [Intelligent Data Query with LLMs](https://sessionize.com/app/speaker/session/494393) 17 | * [Communicate your data effectivly](https://github.com/Hrishikesh332/ML_Guide/tree/main/Communicate%20Your%20Data%20Effectively) 18 | 19 | 20 | ## Podcasts/Sessions 21 | * [Getting Started with Machine Learning]() 22 | * [Breaking Down Machine Learning & Building Classification App](https://github.com/Hrishikesh332/ML_Guide/tree/main/Breaking%20Down%20ML%20%26%20Building%20Classification%20Model) 23 | * [Intro to Google Cloud](https://www.youtube.com/watch?v=Eiy9kQTgQSo) 24 | * [Time Series Analysis and Forecasting Workshop]() 25 | * [AutoML on GCP](https://www.youtube.com/watch?v=xr5bHV2syhk) 26 | * [Unlock the Data - EDA & AutoEDA](https://github.com/Hrishikesh332/ML_Guide/tree/main/Unlock%20the%20Data%20(EDA%20%26%20AutoEDA)) 27 | 28 | ## Misc 29 | 30 | * [Exploring Domains in AI & ML](https://github.com/Hrishikesh332/ML_Guide/tree/main/Exploring%20Domains) 31 | 32 | ## Panels 33 | 34 | * [Hacktoberfest 101](https://www.youtube.com/watch?v=Lwb0VA_FIxg) 35 | * [Hack the Hackathon 1.0](https://www.youtube.com/watch?v=ODkXv49GgB4) 36 | * [Kickstart Your Cloud Journey : Become a part of this emerging technology](https://www.youtube.com/watch?v=rqvb5mdGq_0) 37 | * [Ctrl + Alt + Explore]() 38 | -------------------------------------------------------------------------------- /Art of Time Series Analysis and Forecasting/README.md: -------------------------------------------------------------------------------- 1 | ## Session at SuperAI Connect 2 | 3 | 4 | 5 | ## About 6 | 7 | Are you ready to unravel the mysteries hidden within time series data? Look no further than the Time Series Guide! Repository is packed with resources, project ideas, and tips to help you master the art of time series analysis 📈 Join us on a journey of exploration and discovery, and unleash the power of time series in your data science journey! 🚀 8 | 9 | **🟣 Why Time Series Analysis ?** 10 | 11 | Have you ever wondered what the stock market will look like next month? Or how the weather will be next week? Or what will be the sales of the store in next quarter ❓ These questions are perfect examples of the kind of problems that can be solved with the help of time series analysis. Time series analysis allows you to understand, interpret, and predict patterns in data over time. By analyzing past trends and patterns, we can gain insights into what the future may hold with the perspective of data. So get you're ready to unlock the secrets of time series analysis 📊 12 | 13 | **🟣 Difference between Time Series and Interpolation Method ?** 14 | 15 | Major difference between time series analysis and interpolation methods is that time series analysis is focused more on analyzing data over time to identify patterns, and trends. While interpolation methods are focused on estimating missing data points between known data points. 16 | 17 | Time series analysis ----> When predicting future values is important 18 | 19 | Interpolation methods ----> Filling in missing data. 20 | 21 | ## Features 22 | 23 | 👨‍💻 Provides a step by step guide to exploratory data analysis and forcasting modelling for time series. 24 | 25 | 📚 Repository contains a list of resources such as tutorials, courses, books, articles, and videos to deepen their knowledge of time series analysis. 26 | 27 | ⭐ Offers project ideas that users can work on to apply their knowledge of time series analysis. 28 | 29 | 🤝 Encourages collaborative learning and sharing of knowledge among others. 30 | 31 | ## Project Ideas: 32 | 33 | * Sales forecasting 34 | 35 | * Stock market forecasting 36 | 37 | * Weather forecasting 38 | 39 | * Disease outbreak forecasting 40 | 41 | * Traffic forecasting 42 | 43 | * Energy demand forecasting 44 | 45 | * Website traffic forecasting 46 | 47 | * Supply chain forecasting 48 | 49 | 50 | ## Un-Explored and New Ideas: 51 | 52 | * Predicting social trends, for an example fashion and food which will be helpful for industry to understand and adapt according to it. 53 | 54 | * Wildlife population forecasting, to predict wildlife populations based on historical data which is helpful to take the action at correct time. 55 | 56 | ## Resources: 57 | 58 | * [Forecasting: Principles and Practice](https://otexts.com/fpp2/index.html) (Textbook) 59 | 60 | * [Time Series Cheatsheet](https://math.bju.edu/media/bju-math-division/bju-math-department/math-courses/ma-415/time-series_both.pdf) (Cheatsheet) 61 | 62 | * [Machine learning for trading](https://www.udacity.com/course/machine-learning-for-trading--ud501) (Udacity-Course) 63 | 64 | * [Time Series with Python](https://www.datacamp.com/tracks/time-series-with-python) (Datacamp-course) 65 | 66 | * [11 Classical Time Series Forecasting Methods in Python](https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/) (Cheatsheet) 67 | 68 | * [Forecasting Future Prices of Cryptocurrency using Historical Data](https://towardsdatascience.com/forecasting-future-prices-of-cryptocurrency-using-historical-data-83604e72bc68) (Blog) 69 | 70 | For more course reasources, I have created a separate thread: 71 | 72 | https://www.kaggle.com/discussions/general/310100#1706540 73 | 74 | 75 | 76 | 77 | 78 | ## Authors 79 | 80 | 🔆 [@Hrishikesh Yadav](https://github.com/Hrishikesh332) 81 | 82 | 83 | 84 | ## Feedback 85 | 86 | If you have any feedback, please reach out to us at **hrishikesh3321@gmail.com** 87 | 88 | 89 | --------------------------------------------------------------------------------