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Python Machine Learning Bootcamp
Pre-Machine Learning Steps
Course Introduction (0:35)
Setup and Installation (8:42)
Loading Datasets (7:58)
Data Format (7:51)
Train Test Splitting (12:57)
Stratified Splitting (12:41)
Data Preparation and Exploration (22:07)
Machine Learning Workflow
Supervised Learning Introduction (7:08)
Classification Introduction (6:29)
Logistic Regression Theory (18:55)
Gradient Descent (11:14)
Types of Classification Problems (12:47)
Creating and Training a Binary Classifier (25:11)
Creating and Training a Multiclass Classifier (11:55)
Evaluating Classifiers Theory (9:51)
Precision and Recall Theory (16:22)
ROC, Confusion Matrix, and Support Theory (6:02)
MNIST Dataset Introduction (6:13)
Evaluating Classifiers Practical (15:51)
Validation Set (4:21)
Cross-Validation (23:26)
Hyperparameters (13:15)
Regularization Theory (17:17)
Generalization Error Sources (13:06)
Regularization Practical (9:00)
Grid and Randomized Search (29:42)
Handling Missing Values (30:35)
Feature Scaling Theory (21:02)
Feature Scaling Practical (20:15)
Text and Categorical Data (33:54)
Transformation Pipelines (14:14)
Custom Transformers (9:08)
Column Specific Pipelines (14:14)
Over and Undersampling (40:24)
Feature Importance (30:55)
Saving and Loading Models and Pipelines (14:17)
Post Prototyping (22:00)
Classification
Multilabel Classification (16:30)
Polynomial Features (14:43)
SVM Theory (40:28)
SVM Classification Practical (29:57)
KNN Classification Theory (17:11)
KNN Classification Practical (14:09)
Decision Tree Classifier Theory (32:12)
Decision Tree Pruning (3:32)
Decision Tree Practical (14:34)
Random Forest Theory (8:23)
Random Forest Practical (8:42)
Naive Bayes Theory (8:54)
Naive Bayes Practical (8:09)
How to Choose a Model (11:35)
Regression
Regression Introduction (16:37)
Linear Regression Practical (19:01)
Regularized Linear Regression Practical (20:27)
Boston Housing Introduction (17:52)
Polynomial Regression (16:33)
Regression Losses and Learning Rates (11:05)
SGD Regression (19:05)
KNN Regression Theory (3:28)
KNN Regression Practical (8:17)
SVM Regression Theory (5:37)
SVM Regression Practical (8:43)
Decision Tree Regression Theory (6:38)
Decision Tree and Random Forest Regression Practical (11:21)
Additional Regression Metrics (7:10)
Ensembles
Ensembles Introduction (6:03)
Voting Ensembles Theory (6:55)
Voting Classification Practical (12:20)
Voting Regression Practical (4:03)
Bagging and Pasting Theory (9:19)
Bagging and Pasting Classification Practical (9:20)
Bagging and Pasting Regression Practical (7:24)
AdaBoost Theory (10:54)
AdaBoost Classification Practical (12:38)
AdaBoost Regression Practical (4:22)
Gradient Boosting Theory (12:52)
Gradient Boosting Classification Practical (8:26)
Gradient Boosting Regression Practical (7:06)
Stacking and Blending Theory (7:39)
Stacking Classifiers Practical (12:57)
Stacking Regression Practical (6:59)
Dimensionality Reduction
Dimensionality Reduction Introduction (18:00)
PCA Theory (30:38)
PCA Practical (27:57)
NNMF Theory (7:42)
NNMF Practical (14:52)
Isomap Theory (5:17)
Isomap Practical (15:53)
LLE Theory (11:38)
LLE Practical (12:25)
t-SNE Theory (16:53)
t-SNE Practical (13:59)
Unsupervised Learning
Unsupervised Learning Introduction (7:23)
KMeans Theory (11:11)
KMeans Practical (16:41)
Choosing Number of Clusters Theory (19:43)
Choosing Number of Clusters Practical (10:42)
DBSCAN Theory (10:01)
DBSCAN Practical (9:38)
Gaussian Mixture Theory (16:01)
Gaussian Mixture Practical (15:51)
Semi-Supervised Theory (11:15)
Semi-Supervised Practical (12:48)
Unsupervised Learning Introduction
In this video, we will get introduced to unsupervised learning.
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