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BADIR: Hands-on Machine Learning/AI
Section 1
Important message
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Python: Introduction to jupyter notebook (3:48)
Python: variables, conditional statements and Lists (9:47)
Python: Loops (11:56)
Python: Functions (12:07)
Python: Packages - Numpy & Pandas (14:26)
Python: Packages - Pandas (10:30)
Python: Selecting Data - Indexing, slicing and Data Manipulation (17:38)
Python: Changing values and Concatenation (7:37)
Python: Grouping data with groupby (14:37)
Python: Plotting (3:22)
Section 2
ML: Introduction (41:54)
ML: Loss function, Gradient Descent & Loss functions (23:04)
ML: Regularization, Penalty and Cost function (24:52)
ML: Ensemble techniques and K-fold cross validation (25:37)
ML: Hyper-parameter tuning and Evaluation Metrics (29:23)
Section 3
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BADIR: ML - Regression (33:02)
BADIR - ML: Regression - Missing value Treatment (18:54)
BADIR - ML: regression - Outlier Treatment (20:39)
BADIR - ML: Regression - Variable Reduction (26:15)
BADIR - ML:Regression - Variable Transformation (19:01)
BADIR - ML: Regression - Model Building (25:22)
BADIR - ML:Regression - Hyper-parameter Tuning (28:13)
BADIR - ML: Feature Importance (4:13)
Section 4
Downloads
BADIR - ML: Classification (21:53)
BADIR - ML: Classification - Data Prep (18:58)
BADIR - ML: Classification - Model Building: Decision Tree & Logistic Regression (26:03)
BADIR - ML: Classification - Model Building: Base Model - GBC (9:50)
BADIR - ML:Classification - Model Building: Hyper parameter (41:00)
BADIR - ML: Classification - Final Model and Exercise (3:44)
Section 5
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BADIR - ML: Classification Walk through (57:09)
Section 6
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Unsupervised Learning (7:30)
PCA (4:07)
BADIR: Segmentation (42:16)
BADIR - ML:K-means (43:39)
Section 7
NN - Introduction to neural networks (26:54)
NN: Activation function, Loss and Gradient descent (24:49)
NN: Regularization and Weight optimization (19:56)
BADIR: Model Building - Neural Network & intuition (41:29)
Section 8
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BADIR - Build Model: Neural Network Regression (44:42)
BADIR - Build Model: Neural Network hyper parameter tunning (21:55)
BADIR - Build Model: Neural Network Classification (16:15)
Section 9
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Introduction to CNN (33:18)
Introduction to RNN (28:08)
BADIR: CNN (26:56)
BADIR: RNN (18:26)
Section 10
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XYZ - Case Simulation: BADIR - Analysis Plan (25:36)
XYZ - Case Simulation: BADIR - Deriving Insight: Missing Value (25:19)
XYZ - Case Simulation: BADIR - Deriving Insight: Missing Value Treatment and Outlier (22:13)
XYZ - Case Simulation: BADIR - Deriving Insight: Outlier Treatment (24:02)
Section 11
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XYZ: Case Simulation - BADIR: Deriving Insight - Variable Creation (51:29)
XYZ: Case Simulation - BADIR: Derive Insight - Model Building: LGBM (32:49)
Section 12
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XYZ: Case Simulation - BADIR: Derive Insight - Model Building: LGBM Hyper parameter tuning (21:23)
XYZ: Case Simulation - BADIR: Derive Insight - Model Building: NN (45:47)
XYZ: Case Simulation - BADIR: Derive Insight - Final Model (35:29)
XYZ: Case Simulation - BADIR: Recommendation (8:30)
Section 13
ML in everyday life - 1 (55:11)
ML in everyday life - 2 (41:33)
ML: Loss function, Gradient Descent & Loss functions
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