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Introduction to Machine Learning and Python Data Science - Total Overview
Course Overview
Source Files
01 Course Overview (13:46)
Probability and Statistics for Machine Learning
Source Files
01 Probability And Information Theory Overview (5:15)
02 Combinatorics For Probability (8:44)
03 Law Of Large Numbers (10:38)
04 Calculate Center Of Distribution (7:40)
Distributions in Machine Learning
Source Files
01 Uniform Distribution (5:25)
02 Gaussian Distribution (3:45)
03 Log-Normal Distribution (3:28)
04 Exponential Distribution (3:04)
05 Laplace Distribution (1:54)
06 Binomial Distribution (9:05)
07 Multinomial Distribution (3:59)
08 Poisson Distribution (4:21)
Machine Learning Optimization
Source Files
01 Calculate Error Of Machine Learning Model (8:44)
Data Engineering and Machine Learning Masterclass
01 What Is Python (4:48)
02. Intro To Python (4:37)
Course Overview
Source Files
01 Course Overview (3:26)
Load, clean and encode data
01 Load And Clean A Public Dataset (8:55)
02 What Is One-Hot Encoding (10:02)
03 Build X And Y Data With One Hot Encoding (4:57)
04 Logistic Regression With One Hot Encoding (2:20)
Data engineering for machine learning
01 Scale And Encode Data With Scikit-Learn (3:47)
02 What Is Scaling Data (6:36)
03 Build, Train And Test A Machine Learning Model (4:37)
Build regression and discretizer models
01 Compare Decision Tree And Linear Regression Models (6:26)
02 What Is The Kbins Discretizer (4:54)
03 Bin Data With Kbins Discretizer (3:42)
04 Compare Binned Regression Models (3:39)
05 Build A Linear Regression Model On Stacked Data (3:20)
06 What Is K Means Clustering (11:58)
Data transformation and feature selection for ridge regression
01 Build Univariate Nonlinear Transformation (1:55)
02 What Is Gaussian Probability Distribution (2:31)
03 What Is Poisson Distribution (1:08)
04 Build X Y Data With Poisson Distribution In Numpy (3:34)
05 What Is Logarithmic Data Transformation (2:34)
06 Build A Ridge Regression Model (3:41)
06 Build A Ridge Regression Model
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