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Machine Learning with Python
Machine Learning with Python
Intro to Machine Learning (26:03)
Exploratory Data Analysis (13:05)
Feature Scaling (7:40)
Data Cleaning (7:43)
Feature Engineering (6:11)
Linear Regression Intro (8:17)
Gradient Descent (5:58)
Linear Regression + Correlation Methods (26:33)
Linear Regression Implementation (5:06)
Linear Regression (3:22)
KNN Overview (3:01)
Parametric vs non-parametric models (3:28)
EDA on Iris Dataset (22:08)
KNN - Intuition (2:16)
Implement the KNN algorithm from scratch (11:45)
Compare the result with the sklearn library (3:47)
KNN Hyperparameter tuning using the cross-validation (10:47)
The decision boundary visualization (8:17)
KNN - Manhattan vs Euclidean Distance (11:20)
KNN Scaling in KNN (6:01)
Curse of dimensionality (8:09)
KNN use cases (3:32)
KNN pros and cons (5:32)
Decision Trees Section Overview (4:11)
EDA on Adult Dataset (16:53)
What is Entropy and Information Gain (21:50)
The Decision Tree ID3 algorithm from scratch Part 1 (11:32)
The Decision Tree ID3 algorithm from scratch Part 2 (7:35)
The Decision Tree ID3 algorithm from scratch Part 3 (4:07)
ID3 - Putting Everything Together (21:23)
Evaluating our ID3 implementation (16:53)
Compare with Sklearn implementation (8:51)
Visualizing the Tree (10:15)
Plot the features importance (5:51)
Decision Trees Hyper-parameters (11:39)
Pruning (17:11)
[Optional] Gain Ration (2:49)
Decision Trees Pros and Cons (7:31)
[Project] Predict whether income exceeds $50Kyr - Overview (2:33)
Ensemble Learning Section Overview (3:46)
What is Ensemble Learning? (13:06)
What is Bootstrap Sampling? (8:25)
What is Bagging? (5:20)
Out-of-Bag Error (OOB Error) (7:47)
Implementing Random Forests from scratch Part 1 (22:34)
Implementing Random Forests from scratch Part 2 (6:10)
Compare with sklearn implementation (3:41)
Random Forests Hyper-Parameters (4:23)
Random Forests Pros and Cons (5:25)
What is Boosting? (4:41)
AdaBoost Part 1 (4:10)
AdaBoost Part 2 (14:33)
SVM - Outline (5:15)
SVM - SVM intuition (11:38)
SVM - Hard vs Soft Margin (13:25)
SVM - C Hyper-Parameter (4:17)
SVM - Kernel Trick (12:18)
SVM - Kernel Types (18:13)
SVM - with Linear Dataset (13:35)
SVM - Non-Linear Dataset (12:50)
SVM- Multi _ Regression (5:51)
SVM - Project Overview (Voice Gender Recognition) (4:26)
Unsupervised Machine Learning Intro (20:22)
Unsupervised Machine Learning Continued (20:48)
Data Standardization (19:05)
PCA - Section Overview (5:12)
What is PCA (9:36)
PCA - Drawbacks (3:31)
PCA - Algorithm Steps (13:12)
PCA - Covariance Matrix vs SVD (4:58)
PCA - Main Applications (2:50)
PCA - Image Compression (27:00)
PCA - Data Preprocessing (14:31)
PCA - BiPlot and The Screen Plot (17:27)
PCA - Feature Scaling and Screeplot (9:29)
PCA - Supervised vs unsupervised (4:55)
PCA - Visualization (7:31)
KNN Hyperparameter tuning using the cross-validation
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