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BADIR: Hands-on Predictive Analytics
Section 0
Downloads
Monthly mentoring session - Zoom link and calendar invite
Part 1 - Section 1
Downloads
Handout - Part 1
Introduction & BADIR - Step 1: Business Question Framework (22:08)
BADIR - Step 1: Business Question Result (11:13)
BADIR - Step 2: Analysis Plan for PA (40:18)
BADIR- Step 2: Analysis Plan Result (31:02)
Installing R (4:05)
Section 2
Downloads
R: Introduction (16:18)
R: Reading and Viewing Data (38:38)
R: Indexing and apply family of functions (40:25)
R: Correlation (19:26)
Section 3
Downloads
R: Moffett Exercise Review (17:37)
R: Vectors (49:16)
R: Plotting & Outlier Detection (43:05)
Section 4
Downloads
R: Dplyr (36:21)
BADIR - Step 4: Derive Insights - Data Prep (48:35)
BADIR - Step 4: Derive Insights - Data Prep: Missing Value Treatment (33:42)
BADIR - Step 4: Derive Insights - Data Prep: Outlier Detection & Low Information Variable (19:05)
Section 5
Downloads
BADIR - Step 4:Derive Insights - Build Model: Variable creation and reduction (30:15)
Exercise - Moffett (0:33)
BADIR - Step 4:Derive Insights - Build Model: Variable Transformation (19:57)
BADIR - Step 4:Derive Insights - Build Model: Variable Transformation (16:37)
Exercise - Moffett (0:36)
Mentoring Session
Post your questions here
Section 6
Downloads
BADIR - Step 4:Derive Insights - Build Model: Model Building (19:13)
BADIR - Step 4:Derive Insights - Build Model: Model Comparison (18:42)
R:Model Building regression (36:46)
R: Model Building Logistic regression and Decision tree (28:39)
R: Model Comparison (6:15)
Section 7
Downloads
Moffett_walkthrough (67:30)
Quantifying_impact (11:10)
Recommendation (9:20)
Course completion certificate and survey
Certificate and Survey
Part 2 - Section 8
Downloads
Part 2 Handout Download
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 9
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 10
Download
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 11
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 12
Downloads
BADIR - ML: Classification Walk through (57:09)
Section 13
Download
Unsupervised Learning (7:30)
PCA (4:07)
BADIR: Segmentation (42:16)
BADIR - ML:K-means (43:39)
Section 14
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 15
Downloads
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 16
Downloads
Introduction to CNN (33:18)
Introduction to RNN (28:08)
BADIR: CNN (26:56)
BADIR: RNN (18:26)
Section 17
Downloads
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 18
Downloads
XYZ: Case Simulation - BADIR: Deriving Insight - Variable Creation (51:29)
XYZ: Case Simulation - BADIR: Derive Insight - Model Building: LGBM (32:49)
Section 19
Downloads
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 20
ML in everyday life - 1 (55:11)
ML in everyday life - 2 (41:33)
Handout - Part 1
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