Data science is just 70% cleaning data (analytics), 20% experimenting, and 10% actual modeling.
I decided to put together all my MCP posts in a single PDF. It covers: - The fundamentals of MCP - Explanations with visuals and code - 11 hands-on projects for AI engineers Download link in next tweet!
Day 6 Explored the 5 components of Data Science: Statistics and Math, Programming, Data Engineering, Data Visualization, and Domain Expertise. Difference between Machine Learning and Deep Learning: complexity, data requirements, accuracy, and training time. #DataScience
Day 5 Data Science Cortana Analytics Suite: features, benefits, and use cases Explored Open-Source Tools for Data Science: R, Python, Julia, and others Learned about the importance of open-source in Data Science #DataScience #CortanaAnalyticsSuite #OpenSource #R #Python #Julia
Day 4 Data Science Explored Matrix Factorization concepts (SVD & NMF) Learned about Support Vector Machines (SVMs) Discussed Data Science Technologies and their applications Introduction to Azure Machine Learning and its capabilities #DataScience #MachineLearning #Azure
Day 2 Data Science Classification Regression Statistical Learning Theory - Understanding the fundamentals of machine learning - Occam's Razor: preferring simpler models over complex ones Clustering Simple Linear Regression Ridge Regression #DataScience
United States Tendencias
- 1. Steelers 45K posts
- 2. Rodgers 19.3K posts
- 3. Chargers 27.4K posts
- 4. Tomlin 6,662 posts
- 5. #HereWeGo 5,333 posts
- 6. Schumer 197K posts
- 7. Keenan Allen 2,831 posts
- 8. #BoltUp 2,277 posts
- 9. Herbert 10.2K posts
- 10. #RHOP 6,256 posts
- 11. Tim Kaine 12.8K posts
- 12. Resign 92.7K posts
- 13. Durbin 18.8K posts
- 14. Ladd 3,869 posts
- 15. Cornyn 12.5K posts
- 16. Jaylen Warren 1,786 posts
- 17. #ITWelcomeToDerry 3,543 posts
- 18. #snfonnbc N/A
- 19. Roman Wilson N/A
- 20. Pistons 5,959 posts
Something went wrong.
Something went wrong.