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Machine Learning and Data Science Essentials with Python & R
Linear Algebra
01-Notations and Definitions (11:14)
02-Introduction (7:10)
03-Operations on matrices and vectors (11:58)
04-Matrix properties inverse and transpose (15:04)
05-Introduction matrix operations on R (11:45)
06-Introduction to matrix operations on python (11:23)
Machine Learning and Linear Regression
01-Introduction to machine learning (14:42)
02-Linear Regression 1 (22:47)
03-Linear Regression 2 (14:06)
04-Linear Regression with python tensorflow (11:25)
05-Linear Regression with R (30:40)
Logistic Regression
01-Classification and logistic regression (11:40)
02Cost function for logistic regression (14:55)
03-Decision Boundary (11:34)
04-Logistic Regression with python tensorflow (32:09)
05-Logistic Regression with R (31:09)
Problems and Solution
01-Multi Class underfitting and overfitting (13:49)
02-Regularization (12:58)
Clustering
01-K means and K NN algorithm (14:23)
02-K means and K NN algorithm on R (19:02)
03-K NN on python using tensorflow (12:44)
01-K means and K NN algorithm
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