This machine learning course is created for beginners learning in 2024. The course begins with a machine learning roadmap for 2024, focusing on career paths and beginner-friendly theory. Then the course moves on to hands-on practical applications and a complete end-to-end project using Python.

️ Course created by Tatev Karen Aslanyan.
More information about Tatev here: https://lunartech.ai/

Colab code: https://colab.research.google.com/drive/16HdFVhvRq-DEmNU61Qp8YXMTA3CxUmg-?uspsharing

Content
️ (0:00:00) Presentation
️ (0:03:13) Machine learning roadmap for 2024
️ (0:10:39) Must have skill set for career in machine learning
️ (0:38:54) Common career paths in machine learning
️ (0:45:48) Machine Learning Basics
️ (1:00:59) Bias-variance trade-off
️ (1:08:04) Overfitting and regularization
️ (1:23:38) Linear Regression Basics – Statistical Version
️ (1:36:56) Linear regression model theory
️ (2:00:20) Logistic regression model theory
️ (2:15:37) Case study with linear regression
️ (2:33:44) Loading and exploring data
️ (2:39:54) Define independent and dependent variables
️ (2:45:59) Data cleaning and preprocessing
️ (2:54:39) Descriptive statistics and data visualization
️ (3:03:39) InterQuantileRange for outlier detection
️ (3:14:00) Correlation analysis
️ (3:32:14) Split data into Train/Test with sklearn
️ (3:34:31) Running Linear Regression – Causal Analysis
️ (4:01:24) Checking OLS assumptions of the linear regression model
️ (4:10:10) Running Linear Regression for Predictive Analytics
️ (4:15:54) Closing: Next Steps and Resources

Thank you to our champion supporters and sponsors:
davthecoder
jedi-or-sith
南宮千影
Agustin Kussrow
Nattira Maneerat
Heather Wcislo
Serhiy Kalinets
Justin Hual
Otis Morgan
Oscar Rahnama

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