Machine Learning for Data Science Handbook (2023)

This book is available now only in paid channel

Pages: 975 pages
Rate: ⭐️⭐️⭐️⭐️⭐️

Cost of subscription in Paid channel is 5$ for one time and forever

Channel link: https://t.iss.one/+LnCmAFJO3tNmYjUy

Paid channel contain important book and udemy and other courses as zip files

Welcome all
Contact @Hussein_sheikho

Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition

🖥 Github: https://github.com/osvai/ske2grid

📕 Paper: https://arxiv.org/pdf/2308.07571v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/ucf101

https://t.iss.one/DataScienceT

EQ-Net: Elastic Quantization Neural Networks

🖥 Github: https://github.com/xuke225/eq-net

📕 Paper: https://arxiv.org/pdf/2308.07650v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/imagenet

https://t.iss.one/DataScienceT

🏅MixSort

MixSort is the proposed baseline tracker in SportMOT.

🖥 Github: https://github.com/MCG-NJU/MixSort

📕 Paper: https://arxiv.org/pdf/2304.05170.pdf

⭐️ SportsMOT: https://github.com/MCG-NJU/SportsMOT

https://t.iss.one/DataScienceT

⚡prompt2model - Generate Deployable Models from Instructions

prompt2model - Generate Deployable Models from Natural Language Instructions

pip install prompt2model

🖥 Github: https://github.com/neulab/prompt2model

📕 Paper: https://arxiv.org/abs/2308.12261v1

⭐️ Demo: https://github.com/facebookresearch/sonar#usage

☑️ Dataset: https://paperswithcode.com/dataset/mconala

https://t.iss.one/DataScienceT

Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression

🖥 Github: https://github.com/llvy21/duic

📕 Paper: https://arxiv.org/pdf/2308.07733v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/pixel-art

https://t.iss.one/DataScienceT

LibCity

🖥 Github: https://github.com/libcity/bigscity-libcity

📕 Paper: https://arxiv.org/pdf/2308.12899v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/taxibj

https://t.iss.one/DataScienceT

Surprise 2
https://t.iss.one/+MhmkscCzIYQ2MmM8

S3A: Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment

🖥 Github: https://github.com/sheng-eatamath/s3a

📕 Paper: https://arxiv.org/pdf/2308.12960v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/cifar-100

https://t.iss.one/DataScienceT

🖥 Roadmap of free courses for learning Python and Machine learning.

▪Data Science
▪ AI/ML
▪ Web Dev

1. Start with this
https://kaggle.com/learn/python

2. Take any one of these

❯ https://openclassrooms.com/courses/6900856-learn-programming-with-python

❯ https://scaler.com/topics/course/python-for-beginners/

❯ https://simplilearn.com/learn-python-basics-free-course-skillup

3. Then take this
https://netacad.com/courses/programming/pcap-programming-essentials-python

4. Attempt for this certification
https://freecodecamp.org/learn/scientific-computing-with-python/

5. Take it to next level

❯ Data Scrapping, NumPy, Pandas
https://scaler.com/topics/course/python-for-data-science/

❯ Data Analysis
https://openclassrooms.com/courses/2304731-learn-python-basics-for-data-analysis

❯ Data Visualization
https://kaggle.com/learn/data-visualization

❯ Django
https://openclassrooms.com/courses/6967196-create-a-web-application-with-django

❯ Machine Learning
https://developers.google.com/machine-learning/crash-course

❯ Deep Learning (TensorFlow)
https://kaggle.com/learn/intro-to-deep-learning

https://t.iss.one/CodeProgrammer

Please more reaction with our posts

🐕 Reprogramming Under Constraints

🖥 Github: https://github.com/landskape-ai/reprogram_lt

📕 Paper: https://arxiv.org/pdf/2308.14969v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/cifar-10

https://t.iss.one/DataScienceT

🔥 Master Data Science for free

📂 Computer Science 101
https://online.stanford.edu/courses/soe-ycscs101-computer-science-101

📂 Machine Learning Specialization
https://coursera.org/specializations/machine-learning-introduction

📂 Artificial Intelligence for Robotics
https://udacity.com/course/artificial-intelligence-for-robotics--cs373

📂 Designing Your Career
https://online.stanford.edu/courses/tds-y0003-designing-your-career

📂 Stanford: Game Theory
https://online.stanford.edu/courses/soe-ycs0002-game-theory

📂 Machine Learning with Python
https://www.freecodecamp.org/learn/machine-learning-with-python/

📂 Probability and Statistics: To P or Not To P? (Coursera)
https://www.coursera.org/learn/probability-statistics

📂 Numpy complete free course
https://www.youtube.com/playlist?list=PLysMDSbb9Hcz3Gdi9oV-btohZ9zhths-r

📂Advanced Machine Learning
https://www.kaggle.com/learn/intro-to-machine-learning

📂 Stat 110: Harvard University (YouTube)
https://www.youtube.com/watch?v=KbB0FjPg0mw&list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo&index=1

📂 The Open Source Data Science Masters
https://github.com/datasciencemasters/go

📂 Google - artificial intelligence for everyone
https://edx.org/learn/artificial-intelligence/google-google-ai-for-anyone

📂Microsoft - AI for Beginners
https://microsoft.github.io/AI-For-Beginners

📂 IBM - AI for Everyone: Master the Basics
https://edx.org/learn/artificial-intelligence/ibm-ai-for-everyone-master-the-basics

📂 Harvard - Introduction to Artificial Intelligence with Python
https://cs50.harvard.edu/ai/2023

📂 Introduction to Generative AI
https://cloudskillsboost.google/journeys/118

📂 Deep Learning - Finetuning Large Language Models
https://deeplearning.ai/short-courses/finetuning-large-language-models/

📂Microsoft - AI Basics in Azure
https://learn.microsoft.com/en-us/training/paths/create-no-code-predictive-models-azure-machine-learning/

https://t.iss.one/CodeProgrammer

📂Linux Foundation
https://edx.org/learn/computer-programming/the-linux-foundation-data-and-ai-fundamentals

📂 12 Linux courses:
https://t.iss.one/linuxkalii/538

📂 Alison - 13 free AI courses
https://alison.com/tag/artificial-intelligence

📂 Artificial Intelligence Projects:
https://mygreatlearning.com/academy/learn-for-free/courses/artificial-intelligence-projects

📂 Introduction to Internet of Things:
https://online.stanford.edu/courses/xee100-introduction-internet-things

📂 Graph Search, Shortest Paths, and Data Structures
https://coursera.org/learn/algorithms-graphs-data-structures

📂 Python:
https://cs50.harvard.edu/python/2022/

📂 Machine Learning:
https://developers.google.com/machine-learning/crash-course

📂 Deep Learning
https://introtodeeplearning.com

📂 Data Analysis
https://pll.harvard.edu/course/data-analysis-life-sciences-4-high-dimensional-data-analysis

📂 Linear algebra:
https://pll.harvard.edu/course/data-analysis-life-sciences-2-introduction-linear-models-and-matrix-algebra

📂 Algebra basics
https://www.khanacademy.org/math/algebra-basics

📂 Excel and PowerBI
https://learn.microsoft.com/training/paths/modern-analytics/

📂 Data visualization:
https://pll.harvard.edu/course/data-science-visualization

📂 PowerBI
https://learn.microsoft.com/users/collinschedler-0717/collections/m14nt4rdwnwp04

📂 Tableau:
https://tableau.com/learn/training

📂 Statistics:
https://cognitiveclass.ai/courses/statistics-101

📂 SQL:
https://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql

https://t.iss.one/CodeProgrammer

Please more reaction with our posts

imbalanced-DL: Deep Imbalanced Learning in Python

🖥 Github: https://github.com/ntucllab/imbalanced-dl

📕 Paper: https://arxiv.org/pdf/2308.15457v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/cifar-10

https://t.iss.one/DataScienceT