This machine learning course is made for beginners learning in 2024. The course starts with a Machine Learning Roadmap to 2024, focusing on career paths and beginner-friendly theory. The course then moves on to hands-on practical applications and a comprehensive end-to-end project using Python.

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

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

Contents
️ (0:00:00) Introduction
️ (0:03:13) Machine Learning Roadmap 2024
️ (0:10:39) Must have skills for a career in Machine Learning
️ (0:38:54) Common career paths for machine learning
️ (0:45:48) Basics of machine learning
️ (1:00:59) Balancing bias and variance
️ (1:08:04) Overfitting and regularization
️ (1:23:38) Basics of Linear Regression – Statistical Version
️ (1:36:56) Theory of linear regression models
️ (2:00:20) Logistic regression model theory
️ (2:15:37) Linear regression case study
️ (2:33:44) Loading and exploring data
️ (2:39:54) Defining 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) Splitting data in 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) Perform linear regression for predictive analytics
️ (4:15:54) Wrap-up: Next Steps and Resources

Thanks to our champion and sponsor supporters:
davdecoder
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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