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Machine Learning with Python
Course Introduction
Course Introduction (2:02)
Machine Learning Concepts
Section Introduction (0:38)
Supervised and Unsupervised Learning (8:34)
Semi-Supervised Learning (4:25)
Section Summary (0:23)
First ML Application
Section Introduction (1:49)
Installing the Environment (2:54)
Hello World (7:34)
Installing Aaconda and Deep Learning Libraries (10:18)
Email Spam Checker - Part 1 (7:09)
Email Spam Checker - Part 2 (13:39)
Email Spam Checker Results (8:35)
Iris 70:30 - Part 1 (8:56)
Iris 70:30 - Part 2 (9:18)
Section Summary (0:44)
Data Analysis
Section Introduction (0:32)
Data Analysis - Example 1 (12:57)
Data Analysis - Example 2 (10:47)
Data Visualization (8:44)
Section Summary (0:44)
Linear Algebra
Section Introduction (0:59)
Parametric Algorithms (6:52)
Linear Algebra (9:35)
Linear Regression Calculation - Part 1 (12:42)
Linear Regression Calculation - Part 2 (5:03)
Regression on Larger Dataset - Part 1 (10:18)
Regression on Larger Dataset - Part 2 (7:38)
Regression on Larger Dataset - Part 3 (10:09)
Section Summary (0:38)
Natural Language Processing
Section Introduction (1:03)
Natural Language Processing - Part 1 (8:57)
Natural Language Processing - Part 2 (3:57)
Tokenizing Content (11:21)
Processing Unique Words (13:11)
Summarizing Headlines - Part 1 (9:18)
Summarizing Headlines - Part 2 (11:54)
Summarizing Headlines - Part 3 (8:31)
Section Summary (0:50)
Clustering
Section Introduction (1:03)
Cluster Introduction (8:31)
EM and M Clustering (6:18)
Clustering Code Walkthrough (9:07)
Clustering Iris Data - Part 1 (8:57)
Clustering Iris Data - Part 2 (8:43)
Clustering Iris Data - Part 3 (8:15)
Dendrogram Graphs (10:01)
Section Summary (0:59)
Course Summary (2:00)
Supervised and Unsupervised Learning
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