https://github.com/ashishpatel26/resourcebank_cv_nlp_mlops_2022
This repository offers a goldmine of materials for students of computer vision, natural language processing, and machine learning operations.
https://github.com/ashishpatel26/resourcebank_cv_nlp_mlops_2022
computer-vision data-science deep-learning mlops natural-language-processing
Last synced: 20 days ago
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This repository offers a goldmine of materials for students of computer vision, natural language processing, and machine learning operations.
- Host: GitHub
- URL: https://github.com/ashishpatel26/resourcebank_cv_nlp_mlops_2022
- Owner: ashishpatel26
- Created: 2022-09-06T14:43:41.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-10-31T16:48:34.000Z (over 2 years ago)
- Last Synced: 2025-03-29T19:05:31.609Z (27 days ago)
- Topics: computer-vision, data-science, deep-learning, mlops, natural-language-processing
- Language: Jupyter Notebook
- Homepage: https://t.me/+rqiohEpecDIwOTY1
- Size: 319 KB
- Stars: 418
- Watchers: 14
- Forks: 91
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Resource Bank of Computer Vision, Natural Langauge Processing and MLops
---
This initiative compiles educational resources on a daily basis, making it possible for users to get all the data they want in one place.
## 📝 Table of Contents
---
- [95% Data Science Skill Covered Course](#course)
- [COMPUTER VISION](#CV)
- [NATURAL LANGUAGE PROCESSING](#NLP)
- [MLOPS](#MLOPS)
## ♾️ 95% Data Science Skills Covered Course
---
| Sr.No | Courses | Link |
| ----- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| 1 | **Machine Learning Specialization** | [](https://lnkd.in/dUFfAdNS) |
| 2 | **Deep Learning Specialization** | [](https://lnkd.in/gpQhgA2T) |
| 3 | **Natural Language Processing Specialization** | [](https://lnkd.in/dswF7VtT) |
| 4 | **TensorFlow Developer Professional Certificate** | [](https://lnkd.in/d5jS5gsU) |
| 5 | **TensorFlow: Advanced Techniques Specialization** | [](https://lnkd.in/dnTEBAVk) |
| 6 | **TensorFlow: Data and Deployment Specialization**| [](https://lnkd.in/dG7ZaMjF) |
| 7 | **Machine Learning Engineering for Production (MLOps) Specialization** | [](https://lnkd.in/gicaDub6) |
| 8 | **Generative Adversarial Networks (GANs) Specialization** | [](https://lnkd.in/dye_EFdb) |
| 9 | **Practical Data Science on the Amazon Web Services (AWS) Cloud Specialization** | [](https://lnkd.in/dShdTA2R) |
| 10 | **Mathematics of Data Science** | [](https://www.youtube.com/playlist?list=PLiud-28tsatIKUitdoH3EEUZL-9i516IL) |
| 11 | ** ** | []() |
#### Extra Resources 
| Sr.No | Resource Name | Link |
| ----- | ------------- | ------------------------------------------------------------ |
| 1 | **Intro to optimization in deep learning: Gradient Descent** | [](https://blog.paperspace.com/intro-to-optimization-in-deep-learning-gradient-descent/) |
| 2 | **ML system design usecases** | [](https://github.com/khangich/machine-learning-interview/blob/master/design.md) |
| 2 | **Free datasets for Data Science, Data Analytics, and ML projects** | [](https://www.linkedin.com/posts/dhavalsays_datasciencetraining-datasciencecareers-machinelearningtraining-activity-6973845094596542464-I0Cy/?utm_source=share&utm_medium=member_ios) |
| 3 | **Accuracy and Loss: Things to Know about The Top 1 and Top 5 Accuracy** | [](https://towardsdatascience.com/accuracy-and-loss-things-to-know-about-the-top-1-and-top-5-accuracy-1d6beb8f6df3) |
| 4 | ** ** | []() |
| 5 | ** ** | []() |
| 6 | ** ** | []() |
**Computer Vision Learning Path Recommended by OpenCV**
---
[](https://opencv.org/syllabus/cv1-syllabus.pdf) [](https://opencv.org/syllabus/cv2-syllabus.pdf)
#### Books
| Sr.No | Book Name | Link |
| ----- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| 1 | **Computer Vision: Algorithms and Applications** | [](https://szeliski.org/Book/) |
| 2 | **Practical Deep Learning for Cloud, Mobile & Edge** | [](https://lnkd.in/gX4tKbD4) |
| 3 | **Concise Computer Vision: An Introduction into Theory and Algorithms** | [](https://lnkd.in/g2pxMJax) |
| 4 | **Computer Vision: Principles, Algorithms, Applications, Learning** | [](https://lnkd.in/gukyYGdZ) |
| 5 | **Computer Vision: Models, Learning, and Inference** | [](https://lnkd.in/gDJ3_dbN) |
| 6 | **Deep Learning for Vision Systems** | [](https://lnkd.in/gcqya3RJ) |
| 7 | **Modern Computer Vision with Pytorch** | [](https://lnkd.in/gkY7zWH5) |
| 8 | **Multiple View Geometry in Computer Vision** | [](https://lnkd.in/g963Hkki) |
| 9 | **Learning OpenCV 5 Computer Vision with Python 3** | [](https://lnkd.in/gKs-W4h2) |
| 10. | **Computer Vision Metrics: Survey, Taxonomy, and Analysis** | [](https://lnkd.in/gGx782es) |
#### Articles 
| Sr.No | Article Name | Link |
| ----- | -------------------------------------------- | ------------------------------------------------------------ |
| 1 | Computer Vision: Algorithms and Applications | []() |
| 2 | End to End Learning for Self-Driving Cars using python | [](https://medium.com/@dipeshshtha4/end-to-end-learning-for-self-driving-cars-using-python-c5e8852af3e6) |
| 3 | | []() |
| 4 | | []() |
| 5 | | []() |
| 6 | | []() |
| 7 | | []() |
| 8 | | []() |
| 9 | | []() |
| 10 | | []() |
#### Video 
| Sr.No | Video Name | Link |
| ----- | ---------- | ------------------------------------------------------------ |
| 1 | **Stanford Computer Vision playlist** | [](https://www.youtube.com/playlist?list=PLf7L7Kg8_FNxHATtLwDceyh72QQL9pvpQ) |
| 2 | **Deep Learning for Computer Vision** | [](https://www.youtube.com/playlist?list=PLyqSpQzTE6M_PI-rIz4O1jEgffhJU9GgG) |
| 3 | **Computer Vision (Andreas Geiger)** | [](https://www.youtube.com/playlist?list=PL05umP7R6ij35L2MHGzis8AEHz7mg381_) |
| 4 | **Deep Learning with PyTorch: Zero to GANs** | [](https://www.youtube.com/playlist?list=PLyMom0n-MBroupZiLfVSZqK5asX8KfoHL) |
| 5 | **Digital Image Processing** | [](https://www.youtube.com/playlist?list=PLqhXzDruUpI-UV7R5nuJ6UcuQgK07AuDE) |
| 6 | **Image Signal Processing** | [](https://www.youtube.com/playlist?list=PLyqSpQzTE6M-T5ZrthkU763MHKIKCa0sX) |
| 7 | **The Geometry of vision** | [](https://www.youtube.com/playlist?list=PLyqSpQzTE6M_bepAWap9JE-i9UiO6-y_f) |
| 8 | **UCF Computer Vision Video Lectures 2012** | [](https://www.youtube.com/playlist?list=PLd3hlSJsX_Imk_BPmB_H3AQjFKZS9XgZm) |
| 9 | **CAP5415 Computer Vision - Fall 2021** | [](https://www.youtube.com/playlist?list=PLd3hlSJsX_IkXSinyREhlMjFvpNfpazfN) |
| 10 | **UCF CRCV** | [](https://www.youtube.com/c/UCFCRCV/featured) |
| 11 | **Stanford Computer Vision** | [](https://www.youtube.com/playlist?list=PLf7L7Kg8_FNxHATtLwDceyh72QQL9pvpQ) |
| 12 | **Deep Learning for Computer Vision** | [](https://www.youtube.com/playlist?list=PLyqSpQzTE6M_PI-rIz4O1jEgffhJU9GgG) |
| 13 | **Deep Learning for Computer Vision** | [](https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r) |
| 14 | **Image Processing with C++** | [](https://www.youtube.com/playlist?list=PLG5M8QIx5lkzdGkdYQeeCK__As6sI2tOY) |
| 15 | **Full Stack Deep Learning - 2022** | [](https://www.youtube.com/playlist?list=PL1T8fO7ArWleMMI8KPJ_5D5XSlovTW_Ur) |
| 16 | | []() |
#### Extra Resources 
| Sr.No | Resource Name | Link |
| ----- | ------------- | ------------------------------------------------------------ |
| 1 | **Making Friends with Machine Learning** | [](https://www.youtube.com/playlist?list=PLRKtJ4IpxJpDxl0NTvNYQWKCYzHNuy2xG) |
| 2 | **End to End Tutorial on CNN** | [](https://www.youtube.com/watch?v=58aFgtQRfhA&ab_channel=SatyajitPattnaik) |
| 3 | **Compact GPU Powerhouse** | [](https://www.youtube.com/watch?v=ol27l73-NeE&ab_channel=GIGABYTE) |
| 4 | **Machine Learning from Scratch - Python Tutoria**ls | [](https://www.youtube.com/playlist?list=PLqnslRFeH2Upcrywf-u2etjdxxkL8nl7E) |
| 5 | **CUDA Crash Course** | [](https://www.youtube.com/playlist?list=PLxNPSjHT5qvtYRVdNN1yDcdSl39uHV_sU) |
| 6 | **Object Tracking Using Deep SORT and YOLOv4** | [](https://www.youtube.com/watch?v=kBahrCeaoDQ&ab_channel=CodeWithAarohi) |
| 7 | **Protecting Your Machine Learning Against Drift** | [](https://www.youtube.com/watch?v=tL5sEaQha5o&ab_channel=EuroPythonConference) |
| 8 | **LeetCode** | [](https://www.youtube.com/c/NeetCode/videos) |
| 9 | **Mathematics of Data Science** | [](https://www.youtube.com/playlist?list=PLiud-28tsatIKUitdoH3EEUZL-9i516IL) |
| 10 | **Modern C++ (2021 Lecture & Tutorials)** | [](https://www.youtube.com/playlist?list=PLgnQpQtFTOGRv7VS6fYerEbT4ckBovKur) |
| 11 | **Lecture: Modern C++ (Summer 2018, Uni Bonn)** | [](https://www.youtube.com/playlist?list=PLgnQpQtFTOGR50iIOtO36nK6aNPtVq98C) |
| 12 | **Machine Learning** | [](https://www.youtube.com/playlist?list=PL5bUlblGfe0Ljo83LHtrRPXdQAsklFEFB) |
| 13 | **Krish naik youtuber** | [](https://www.youtube.com/user/krishnaik06) |
| 14 | **NPTEL MOOC Machine Learning 2016** | [](https://www.youtube.com/playlist?list=PL1xHD4vteKYVpaIiy295pg6_SY5qznc77) |
| 15 | **Deep Learning** | [](https://www.youtube.com/playlist?list=PLyqSpQzTE6M9gCgajvQbc68Hk_JKGBAYT) |
| 16 | **3Blue1Brown: Calculus** | [](https://www.youtube.com/playlist?list=PL0-GT3co4r2wlh6UHTUeQsrf3mlS2lk6x) |
| 17 | **Learn TensorFlow and Deep Learning fundamentals with Python** | [](https://www.youtube.com/watch?v=tpCFfeUEGs8&ab_channel=DanielBourke) |
| 18 | **GTC Sept 2022 Keynote with NVIDIA CEO Jensen Huang** | [](https://www.youtube.com/watch?v=PWcNlRI00jo&ab_channel=NVIDIA) |
| 19 | **ML Model Training and Inference with a Data Mesh** | [](https://www.youtube.com/watch?v=3-JwOzqaUDI&ab_channel=SdAmVfGT1K8MnsmuYQby2wJibmeiHnQwVHn) |
| 20 | **Attitude-Guided Loop Closure for Cameras with Negative Plane** | [](https://github.com/flysoaryun/LF-VIO-Loop) |
| 21 | **CVonline: The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer Vision** | [](https://homepages.inf.ed.ac.uk/rbf/CVonline/) |
| 22 | **LearnopenCV** | [](https://github.com/spmallick/learnopencv) |
| 23 | **Expanding Language-Image Pretrained Models for General Video Recognition** | [](https://github.com/microsoft/VideoX/tree/master/X-CLIP) |
| 24 | **Open-Set Semi-Supervised Object Detection** | [](https://ycliu93.github.io/projects/ossod.html) |
| 25 | **How To Deal with Dataset Bias** | [](https://youtu.be/nMDpcqx6ll8) |
| 26 | ** ** | []() |
## 📝 NATURAL LANGUAGE PROCESSING
---
**Learning Path NLP**:
**Credit : [graykode](https://github.com/graykode)**
#### Books 
| Sr.No | Book Name | Link |
| ----- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| 1 | **Natural Language Processing with Transformers, Revised Edition by Lewis Tunstall, Leandro von Werra, Thomas Wolf** | [](https://www.oreilly.com/library/view/natural-language-processing/9781098136789/)[](https://github.com/nlp-with-transformers/notebooks) |
| 2 | **Natural Language Processing with PyTorch Book by Brian McMahan and Delip Rao** | [](https://www.oreilly.com/library/view/natural-language-processing/9781491978221/)[](https://github.com/delip/PyTorchNLPBook) |
| 3 | **Transformers for Natural Language Processing by Denis Rothman, Antonio Gulli** | [](https://www.packtpub.com/product/transformers-for-natural-language-processing/9781803247335?_ga=2.7871282.1232780291.1663736855-925719957.1663736855)[](https://github.com/Denis2054/Transformers-for-NLP-2nd-Edition) |
| 4 | **Mastering Transformers by Savaş Yıldırım , Meysam Asgari-Chenaghlu** | [](https://www.packtpub.com/product/mastering-transformers/9781801077651?_ga=2.48763430.1232780291.1663736855-925719957.1663736855)[](https://github.com/PacktPublishing/Mastering-Transformers) |
| 5 | **Advanced Natural Language Processing with TensorFlow 2 by Ashish Bansal** | [](https://subscription.packtpub.com/book/data/9781800200937/pref)[](https://account.packtpub.com/getfile/9781800200937/code#_ga=2.6058163.1232780291.1663736855-925719957.1663736855) |
| 6 | **Python Natural Language Processing Cookbook By Zhenya Antić** | [](https://www.packtpub.com/product/python-natural-language-processing-cookbook/9781838987312?_ga=2.3481136.1232780291.1663736855-925719957.1663736855)[](https://github.com/PacktPublishing/Python-Natural-Language-Processing-Cookbook) |
| 7 | **Getting Started with Google BERT By Sudharsan Ravichandiran** | [](https://www.packtpub.com/product/getting-started-with-google-bert/9781838821593?_ga=2.179296420.1232780291.1663736855-925719957.1663736855)[](https://github.com/PacktPublishing/Getting-Started-with-Google-BERT) |
| 8 | **Exploring GPT-3 By Steve Tingiris** | [ ](https://www.packtpub.com/product/exploring-gpt-3/9781800563193?_ga=2.253722505.1232780291.1663736855-925719957.1663736855)[](https://github.com/PacktPublishing/Exploring-GPT-3) |
| 9 | **Applied Natural Language Processing in the Enterprise by Ankur A. Patel, Ajay Uppili Arasanipalai** | [](https://www.oreilly.com/library/view/applied-natural-language/9781492062561/)[](https://github.com/nlpbook/nlpbook) |
| 10 | **Natural Language Processing in Action, Second Edition by Hobson Lane and Maria Dyshel** | [](https://www.manning.com/books/natural-language-processing-in-action-second-edition)[](https://github.com/totalgood/nlpia) |
#### Articles 
| Sr.No | Article Name | Link |
| ----- | -------------------------------------------- | ------------------------------------------------------------ |
| 1 | Computer Vision: Algorithms and Applications | []() |
| 2 | | []() |
| 3 | | []() |
| 4 | | []() |
| 5 | | []() |
| 6 | | []() |
| 7 | | []() |
| 8 | | []() |
| 9 | | []() |
| 10 | | []() |
#### Video 
| Sr.No | Video Name | Link |
| ----- | ---------- | ------------------------------------------------------------ |
| 1 | Natural Language Processing (University of Michigan) | [](https://www.youtube.com/playlist?list=PLLssT5z_DsK8BdawOVCCaTCO99Ya58ryR) |
| 2 | Stanford CS224N NLP with Deep Learning | [](https://www.youtube.com/playlist?list=PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ) |
| 3 | Chatbot by Binod Suman Academy | [](https://www.youtube.com/playlist?list=PLIRnO_sdVuEevLMSy7bE-Jaqyf1MK_wtr) |
| 4 | | []() |
| 5 | | []() |
| 6 | | []() |
| 7 | | []() |
| 8 | | []() |
| 9 | | []() |
| 10 | | []() |
#### Extra Resources 
| Sr.No | Resource Name | Link |
| ----- | ------------- | ------------------------------------------------------------ |
| 1 | Designing an ML Minded Product | [](https://www.youtube.com/watch?v=Hv54e-9XnZ0&ab_channel=AssociationforComputingMachinery%28ACM%29) |
| 2 | | []() |
| 3 | | []() |
| 4 | | []() |
| 5 | | []() |
**Credit : ML-ops.org**
#### Books 
| Sr.No | Book Name | Link |
| ----- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| 1 | **Engineering MLOps: By Emmanuel Raj** | [](https://www.packtpub.com/product/engineering-mlops/9781800562882)[](https://github.com/PacktPublishing/EngineeringMLOps) |
| 2 | **Machine Learning Design Patterns Book by Michael Munn, Sara Robinson, and Valliappa Lakshmanan** | [](https://www.oreilly.com/library/view/machine-learning-design/9781098115777/)[](https://github.com/GoogleCloudPlatform/ml-design-patterns) |
| 3 | **Designing Machine Learning Systems Book by Chip Huyen** | [](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/)[](https://github.com/chiphuyen/machine-learning-systems-design) |
| 4 | **Practical MLOps by Noah Gift, Alfredo Deza** | [](https://www.oreilly.com/library/view/practical-mlops/9781098103002/)[](https://github.com/paiml/practical-mlops-book) |
| 5 | **MLOps Engineering at Scale Book by Carl Osipov** | [](https://www.manning.com/books/mlops-engineering-at-scale)[](https://www.manning.com/downloads/2158) |
| 6 | **Machine Learning on Kubernetes by Faisal Masood, Ross Brigoli** | [](https://www.packtpub.com/product/machine-learning-on-kubernetes/9781803241807?_ga=2.220534680.1232780291.1663736855-925719957.1663736855)[](https://github.com/PacktPublishing/Machine-Learning-on-Kubernetes) |
| 7 | **Machine Learning Engineering with MLflow By Natu Lauchande** | [](https://www.packtpub.com/product/machine-learning-engineering-with-mlflow/9781800560796?_ga=2.40907298.1232780291.1663736855-925719957.1663736855)[](https://github.com/PacktPublishing/Machine-Learning-Engineering-with-MLflow) |
| 8 | **Machine Learning Engineering with Python By Andrew P. McMahon** | [ ](https://www.packtpub.com/product/machine-learning-engineering-with-python/9781801079259?_ga=2.48765478.1232780291.1663736855-925719957.1663736855)[](https://github.com/PacktPublishing/Machine-Learning-Engineering-with-Python) |
| 9 | **Kubeflow for Machine Learning by Trevor Grant, Holden Karau, Boris Lublinsky, Richard Liu, Ilan Filonenko** | [](https://www.oreilly.com/library/view/kubeflow-for-machine/9781492050117/)[](https://oreil.ly/Kubeflow_for_ML) |
| 10. | **Production-Ready Applied Deep Learning By Tomasz Palczewski , Jaejun (Brandon) Lee , Lenin Mookiah** | [](https://www.packtpub.com/product/production-ready-applied-deep-learning/9781803243665?_ga=2.238320512.1232780291.1663736855-925719957.1663736855)[](https://github.com/PacktPublishing/Production-Ready-Applied-Deep-Learning) |
#### Articles 
| Sr.No | Article Name | Link |
| ----- | ------------ | ------------------------------------------------------------ |
| 1 | | []() |
| 2 | | []() |
| 3 | | []() |
| 4 | | []() |
| 5 | | []() |
| 6 | | []() |
| 7 | | []() |
| 8 | | []() |
| 9 | | []() |
| 10 | | []() |
#### Video 
| Sr.No | Video Name | Link |
| ----- | ---------- | ------------------------------------------------------------ |
| 1 | **MLOps Zoomcamp** | [](https://www.youtube.com/playlist?list=PL3MmuxUbc_hIUISrluw_A7wDSmfOhErJK) |
| 2 | **Machine Learning Engineering for Production (MLOps) Specialization by Andrew Ng** | [](https://www.youtube.com/playlist?list=PLVd1sFtZgLA7gPFPB8nPVEgOG1a5BkmSR) |
| 3 | **Docker Tutorial in Hindi 2022** | [](https://www.youtube.com/playlist?list=PLfP3JxW-T70HIvqQpoDpP-WK-uzD-wnsH) |
| 4 | **CS 329S: Machine Learning Systems Design** | [](https://stanford-cs329s.github.io/syllabus.html) |
| 5 | **Full Stack Deep Learning 2019** | [](https://fall2019.fullstackdeeplearning.com/) |
| 6 | **MLOps - Machine Learning Operations** | [](https://www.youtube.com/playlist?list=PL3N9eeOlCrP5a6OA473MA4KnOXWnUyV_J) |
| 7 | **MLOps: ML Deployment 2020** | [](https://www.youtube.com/playlist?list=PLzq3B7Hh4uvbe9xXXEa1EuawIXTDHZpg1) |
| 8 | **Mlops Live Webinar** | [](https://www.youtube.com/playlist?list=PLH8M0UOY0uy4qzau2iAFunuT6cTMmTHvB) |
| 9 | **Azure MLops** | [](https://www.youtube.com/playlist?list=PLB1nTQo4_y6sMCAjWpAfEfXjC_t-7LeUm) |
| 10 | **MLOps by Pragmatic AI Labs** | [](https://www.youtube.com/playlist?list=PLdfopzFjkPz9shHCeH9poe9sbAn0pIojX) |
| 11 | **MLops Tutorial by DVC.org** | [](https://www.youtube.com/playlist?list=PL7WG7YrwYcnDBDuCkFbcyjnZQrdskFsBz) |
| 12 | **Kubernetes Tutorial for Beginners [FULL COURSE in 4 Hours]** | [](https://www.youtube.com/watch?v=X48VuDVv0do&ab_channel=TechWorldwithNana) |
| 13 | **Docker Tutorials For Beginner - 2 Million view** | [](https://www.youtube.com/watch?v=fqMOX6JJhGo&ab_channel=freeCodeCamp.org) |
#### Extra Resources 
| Sr.No | Resource Name | Link |
| ----- | --------------------------------------- | ------------------------------------------------------------ |
| 1 | **Awesome MLops** | [](https://github.com/visenger/awesome-mlops) |
| 2 | **MadewithML** | [](https://madewithml.com/#mlops) |
| 3 | **Awesome Production Machine learning** | [](https://github.com/EthicalML/awesome-production-machine-learning) |
| 4 | **MLOps - Best Blog(Neptune.ai)** | [](https://neptune.ai/blog) |
| 5 | **OPERATIONALIZING MACHINE LEARNING** | [](https://chicagodatascience.github.io/MLOps/) |
| 6 | **Practical Guide of MLOps by Google** | [](https://cloud.google.com/resources/mlops-whitepaper) |
| 7 | **Awesome MLOps Guide Tools** | [](https://github.com/kelvins/awesome-mlops) |
| 8 | **MLops Blogs by MLOps BootCamp** | [](https://kargarisaac.github.io/blog/categories/#mlops) |
| 9 | **FedML MLOps Introduction** | [](https://www.youtube.com/watch?v=Xgm0XEaMlVQ&ab_channel=ChaoyangHe) |
| 10 | ** ** | []() |
| 11 | ** ** | []() |
| 12 | ** ** | []() |
| 13 | ** ** | []() |
---
***🙏 Thanks for Reading 🙏 More is coming stay tune ⛹️♂️***