This machine learning course is created for beginners who are learning in 2024. The course begins with a Machine Learning Roadmap for 2024, emphasizing career paths and beginner-friendly theory. Then it the course 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-?usp=sharing
Contents
⌨️ (0:00:00) Introduction
⌨️ (0:03:13) Machine Learning Roadmap for 2024
⌨️ (0:10:39) Must Have Skill Set for Career in Machine Learning
⌨️ (0:38:54) Machine Learning Common Career Paths
⌨️ (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) 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 into Train/Test with sklearn
⌨️ (3:34:31) Running Linear Regression - Causal Analysis
⌨️ (4:01:24) Checking OLS Assumptions of Linear Regression Model
⌨️ (4:10:10) Running Linear Regression for Predictive Analytics
⌨️ (4:15:54) Closing: Next Steps and Resources
? Thanks to our Champion and Sponsor supporters:
? davthecoder
? jedi-or-sith
? 南宮千影
? Agustín Kussrow
? Nattira Maneerat
? Heather Wcislo
? Serhiy Kalinets
? Justin Hual
? Otis Morgan
? Oscar Rahnama
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