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Machine Learning - Fun and Easy using Python and Keras
SECTION 1 - Introduction
Section 1 Lecture 1 - Introduction (2:14)
SECTION 2 - Setting up your Python Integrated Development Environment (IDE) for Course Labs
Section 2 Lecture 2 - Download and Install Python Anaconda Distribution (9:23)
Section 2 Lecture 3 - "Hello World" in Jupyter Notebook (16:22)
Section 2 Lecture 4 - Installation for Mac Users (3:19)
Section 2 Lecture 5 - Datasets, Python Notebooks and Scripts For the Course
SECTION 3 - Regression
Section 3 Lecture 6 - Regression
SECTION 4 - Linear Regression
Section 4 Lecture 7 - Linear Regression - Theory (7:26)
Section 4 Lecture 8 - Linear Regression - Practical Labs (10:13)
SECTION 5 - Decision Tree - Classification and Regression Trees
Section 5 Lecture 9 - Decision Tree - Theory (8:19)
Section 5 Lecture 10 - Decision Tree - Practical Labs (10:38)
SECTION 6 - Random Forests
Section 6 Lecture 11 - Random Forest - Theory (7:14)
Section 6 Lecture 12 - Random Forest Practical Labs (8:02)
SECTION 7 - Classification
Section 7 Lecture 13 - Classification
SECTION 8 - Logistic Regression
Section 8 Lecture 14 - Logistic Regression - Theory (7:43)
Section 8 Lecture 15 - Logistic Regression Classification - Practical Labs (6:57)
SECTION 9 - K Nearest Neighbors
Section 9 Lecture 16 - K -Nearest Neighbors - Theory (5:44)
Section 9 Lecture 17 - KNN Classification - Practical Labs (6:46)
SECTION 10 - Support Vector Machines (SVM)
Section 10 Lecture 18 - Support Vector Machine -Theory (7:27)
Section 10 Lecture 19 - Linear SVM - Practical Labs (2:54)
Section 10 Lecture 20 - Non Linear SVM - Practical Labs (1:53)
SECTION 11 - Naive Bayes
Section 11 Lecture 21 - Naive Bayes - Theory (11:39)
Section 11 Lecture 22 - Naive Bayes - Practical Labs (6:05)
SECTION 12 - Clustering
Section 12 Lecture 23 - Clustering
SECTION 13 - K - Means Clustering
Section 13 Lecture 24 - K - Means Clustering (8:42)
Section 13 Lecture 25 - K - Means Clustering - Practical Labs Part A (6:56)
Section 13 Lecture 26 - K - Means Clustering - Practical Labs Part B (3:44)
SECTION 14 - Hierarchical Clustering
Section 14 Lecture 27 - Hierarchical Clustering - Theory (9:32)
Section 14 Lecture 28 - Hierarchical clustering - Practical Labs (8:07)
Section 14 Lecture 29 - Review Lecture (0:35)
SECTION 15 - Associated Rule Learning
Section 15 Lecture 30 - Associated Rule Learning
SECTION 16 - Eclat and Apior
Section 16 Lecture 31 - Apriori (12:30)
Section 16 Lecture 32 - Apriori - Practical Labs (8:23)
Section 16 Lecture 33 - Eclat - Theory (5:44)
Section 16 Lecture 34 - Eclat Practical Labs (6:53)
SECTION 17 - Dimensionality Reduction
Section 17 Lecture 35 - Dimensionality Reduction
SECTION 18 - Principal Component Analysis
Section 18 Lecture 36 - Principal Component Analysis - Theory (12:48)
Section 18 Lecture 37 - PCA - Practical Labs (3:20)
SECTION 19 - Linear Discriminant Analysis LDA
Section 19 Lecture 38 - Linear Discriminant Analysis - Theory (7:40)
Section 19 Lecture 39 - Linear Discriminant Analysis LDA - Practical Labs (5:17)
SECTION 20 - Neural Networks
Section 20 Lecture 40 - Neural Networks
SECTION 21 - Artificial Neural Networks
Section 21 Lecture 41 - Artificial Neural Networks - Theory (18:30)
Section 21 Lecture 42 - ANN-perceptron - Practical Labs A (3:56)
Section 21 Lecture 43 - ANN Perceptron - Practical Labs_B (2:57)
Section 21 Lecture 44 - ANN MLC - Practical Labs_C (4:06)
SECTION 22 - Convolutional Neural Networks
Section 22 Lecture 45 - Convolutional Neural Networks - Theory (11:17)
Section 22 Lecture 46 - Convolution Neural Networks - Practical Labs (8:08)
SECTION 23 - Recurrent Neural Networks
Section 23 Lecture 47 - Recurrent Neural Networks - Theory (12:02)
Section 23 Lecture 48 - Recurrent Neural Networks - Practical Labs (5:25)
SECTION 24 - Conclusion and Bonus Section
Section 24 Lecture 49 - Conclusion (0:59)
Section 24 Lecture 50 - Little something for our Students
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Section 24 Lecture 50 - Little something for our Students
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