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AI: Machine Learning with Python - Hands On!
Section 1 Introduction
Lecture 1 Welcome (1:00)
Lecture 2 Transforming Data into Knowledge (4:37)
Lecture 3 Types of Machine Learning (4:35)
Section 2 Training Machine Learning Algorithms for Classification
Lecture 4 Implementing a Perceptron Algorithm in Python (11:35)
Lecture 5 The Iris Dataset (10:58)
Lecture 6 Training the Perceptron (3:33)
Lecture 7 Improving the Visualization (7:55)
Lecture 8 Adaline in Python (15:06)
Lecture 9 Feature Standardization (9:16)
Lecture 10 Implementing Adaline (14:32)
Section 3 A Tour of Machine Learning Classifiers Using Scikit-Learn
Lecture 11 Scikit-Learn Perceptron (15:32)
Lecture 12 Logistic Regression in Scikit-Learn (7:30)
Lecture 13 Predicting Class Probabilities (8:50)
Lecture 14 Training a Support Vector Machine in Scikit-Learn (10:27)
Lecture 15 The Effect of Gamma (6:26)
Lecture 16 Decision Trees (21:02)
Section 4 Building Good Training Sets – Data Preprocessing
Lecture 17 Handling Data (8:14)
Lecture 18 Mapping Ordinal Features (13:11)
Lecture 19 Feature Scaling (15:38)
Lecture 20 Feature Importance's with Random Forests (9:25)
Lecture 16 Decision Trees
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