Course Content
Python for Applied Machine Learning – Getting Started & EDA
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Import ML Libraries
19:38
Exploratory Data Analysis (EDA) – Part 1
17:01
Exploratory Data Analysis (EDA) – Part 2
12:53
Split Data
10:57
Develop ML Models and check accuracy score
10:47
Classification Report
06:49
Accuracy precision recall and f1
02:14
Recap
15:46
Confusion Matrix
06:47
Glimpses of hyperparameter training estimators
05:14
Save and load the model
04:08
LinearSVC
08:14
NeighborsClassifier
06:20
Run multiple models using Functions
11:47
Run multiple models using Parallel
10:09
Cross Validation Score
13:19
Predict vs Predict Proba
04:37
ROC Curve
15:22
Cross tab
01:59
Correlation Analysis
05:30
Feature Importance
10:35
Hyperparameter tuning – Randomized SearchCV
15:07
Hyperparameter tuning – GridSearchCV
08:19
Classification Project – Heart Disease dataset
32:25
Regressor – Train our model Part – 1
14:22
Regressor – Train our model Part – 2
11:55
Regressor – Recap
10:55
Fill missing values using Pandas
20:41
Fill missing values using scikit-learn libraries
21:47
Regression Project – California Housing dataset
25:26
Machine Learning with Scikit-learn
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