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
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