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2. Unsupervised Machine Learning.mp4
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1 - Introduction to Machine Learning
10 - Feature Engineering
11 - Saving & Loading Models
12 - Practical ML Projects
13 - Deployment Basics
14 - Best Practices & ML Workflow
15 - Interview Preparation
2 - Python for Machine Learning
3 - ML Libraries in Python
4 - Data Handling & Preprocessing
5 - Exploratory Data Analysis (EDA)
6 - Supervised Learning - Regression
7 - Supervised Learning - Classification
8 - Model Evaluation & Validation
9 - Unsupervised Learning
36. K-Means Clustering.mp4
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16.7 MB
37. Hierarchical Clustering.mp4
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24.1 MB
38. Principal Component Analysis.mp4
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26.6 MB
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1 - Introduction to Data Science and Python
2 - Python Fundamentals for Data Science
3 - Working with Data Using Pandas
4 - Numerical Computing with NumPy
5 - Data Visualization
6 - Exploratory Data Analysis (EDA)
7 - Introduction to Statistics for Data Science
8 - Machine Learning with Scikit Learn
37. Supervised vs Unsupervised Learning.mp4
MP4
72.9 MB
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1 - The Dawn of Intelligence
10 - The Future Horizon AGI Beyond
11 - Real World Application Career
2 - The Learning Machines Machine Learning Basics
13 - Unsupervised Learning Finding Hidden Patterns.mp4
MP4
8.7 MB
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1 - Introduction
10 - Project Customer Segmentation using Clustering
11 - Project Fake News Detection using machine learning Python
12 - Project COVID19 Coronavirus Infection Probability using machine learning
13 - Miscellaneous
14 - Python Crash Course
2 - Installation and Colab
3 - Python for Data Analysis Numpy
4 - Python for Data Analysis Pandas
5 - Python for Data Visualization Matplotlib
6 - Supervised Machine Learning
7 - UnSupervised Machine Learning
44 - What is UnSupervised Machine Learning.mp4
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47.2 MB
45 - Types of Unsupervised Learning.mp4
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47.2 MB
46 - Advantages and Disadvantages of Unsupervised Learning.mp4
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36.5 MB
47 - What is clustering.mp4
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37.6 MB
48 - Kmeans Clustering.mp4
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58.5 MB
49 - Implementing Kmeans Clustering.mp4
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35.3 MB
50 - Image compression using Kmeans clustering Color Quantization using KMeans.mp4
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65.4 MB
8 - Reinforcement Learning
51 - Introduction to Reinforcement Learning.mp4
MP4
38.7 MB
52 - Qlearning algorithm in machine learning.mp4
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18.4 MB
9 - Feature Engineering
53 - What is Feature Engineering.mp4
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26.3 MB
54 - What is Outlier.mp4
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50.5 MB
55 - outlier detection and removal using std deviation.mp4
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66.2 MB
56 - Z score.mp4
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19.5 MB
57 - IQR.mp4
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20.5 MB
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1 - Introduction to Machine Learning
10 - Support Vector Machines
11 - K Nearest Neighbors for Classification and Regression
12 - Decision Tree Classifier Algorithm
13 - Random Forest Classifier Algorithm
14 - Naive Bayes Algorithm
15 - Resources
Notebooks
16 - KMeans Clustering
2 - Types of Machine Learning
4 - 4 Difference between Supervised and Unsupervised Learning.mp4
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39.2 MB
5 - 5 Algorithms in Supervised and Unsupervised Learning.mp4
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12.9 MB
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1 - Python Fundamentals
2 - Data Manipulation with Pandas
3 - Data Visualization with Matplotlib & Seaborn
4 - Data Analysis with NumPy
5 - Introduction to Machine Learning
25. Unsupervised Learning Algorithms.mp4
MP4
101.7 MB
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PLURALSIGHT.ADVANCED.MACHINE.LEARNING.WITH.ENCOG.PART.2
02. Unsupervised Competitive Networks
02_01-Introduction.mp4
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1.77 MB
02_04-Unsupervised Learning Tasks.mp4
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8.09 MB
02_05-Unsupervised Networks.mp4
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2.77 MB
02_06-Self-Organizing Map (SOM) Introduction.mp4
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3.09 MB
02_07-Example Dataset.mp4
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2.66 MB
02_08-SOM Network Topology.mp4
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6.9 MB
02_09-SOM Training.mp4
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14.48 MB
02_10-SOM Training Using ENCOG.mp4
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10.35 MB
02_11-SOM Demo Setup SOM Network (XAML Part).mp4
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13.82 MB
02_12-SOM Demo Setup SOM Network (Code Behind).mp4
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25.84 MB
02_13-SOM Demo Training SOM Network.mp4
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9.44 MB
02_14-SOM Demo Overlay.mp4
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11.81 MB
02_15-SOM Demo Alternative Visualization Approach.mp4
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22.08 MB
02_16-Applications of SOM.mp4
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2.61 MB
02_17-Other Unsupervised Learning Techniques.mp4
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1.24 MB
02_18-Summary.mp4
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2.34 MB
03. Unsupervised Auto-Associative Networks
03_01-Introduction.mp4
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1.62 MB
03_02-Outline.mp4
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781.46 KB
03_03-Associative Memory.mp4
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5.35 MB
03_04-Types of Associative Memory.mp4
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2.71 MB
20 matching files shown out of 26 matching files, 75 total files.
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1 - Pandas
10 - Lets apply what we learned Machine Learning Project Classification
11 - Lets apply what we learned Machine Learning Project Regression
12 - Bonus Section
2 - Numpy
3 - Feature Engineering
4 - Evaluation of the Model Performances
5 - Machine Learning Supervised vs Unsupervised
16 - Supervised vs Unsupervised Machine Learning.mp4
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8.2 MB
6 - Data Set Analysis Feature Engineering for Regression Tasks
18 - EDA.mp4
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30.9 MB
19 - Feature Engineering.mp4
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34.4 MB
7 - Data Set Analysis Feature Engineering for Classification Tasks
21 - EDA.mp4
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26.1 MB
22 - Feature Engineering.mp4
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17.8 MB
8 - Supervised Learning
23 - Linear Regression.mp4
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9 MB
24 - Linear Regression 2.mp4
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8.4 MB
25 - Linear Regression 3.mp4
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4.2 MB
26 - Linear Regression Coding.mp4
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33.1 MB
27 - Logistic Regression.mp4
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5.4 MB
28 - Logistic Regression Coding.mp4
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27.6 MB
29 - K Nearest Neighbors.mp4
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9.6 MB
30 - KNearest Neighbors Coding Elbow Method.mp4
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45.9 MB
31 - KNearest Neighbors Coding.mp4
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33.2 MB
32 - Support Vector Machines.mp4
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8 MB
33 - Support Vector Classifier Coding.mp4
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45.3 MB
34 - Support Vector Regression Coding.mp4
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52.8 MB
35 - Decision Tree.mp4
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5.1 MB
36 - Decision Tree Coding.mp4
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14.7 MB
37 - Random Forest.mp4
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5.8 MB
20 matching files shown out of 24 matching files, 101 total files.
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1 - Introduction to Machine Learning
2 - Data and Feature Engineering
3 - Supervised Learning Core Concepts
4 - Unsupervised Learning Discovering Hidden Patterns
13 - Introduction to Unsupervised Learning.mp4
MP4
51 MB
14 - Clustering Techniques.mp4
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28.1 MB
15 - Dimensionality Reduction.mp4
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33.6 MB
5 - Reinforcement Learning Conceptual Overview
16 - Introduction to Reinforcement Learning.mp4
MP4
79.5 MB
17 - Core Concepts in RL.mp4
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24.9 MB
18 - Popular RL Algorithms Conceptual.mp4
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41.9 MB
6 - Model Evaluation and Improvement
19 - Train Validation and Test Splits.mp4
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34.7 MB
20 - CrossValidation Concept.mp4
MP4
19.2 MB
21 - Model Optimization and Hyperparameter Tuning.mp4
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49.2 MB
22 - Bias and Variance Revisited.mp4
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48.4 MB
7 - Deep Learning Conceptual Foundations
23 - What is Deep Learning.mp4
MP4
55.5 MB
24 - Artificial Neural Networks The Concept.mp4
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65.5 MB
25 - Learning Process in Neural Networks.mp4
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65.3 MB
26 - Applications of Deep Learning.mp4
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29.4 MB
8 - Machine Learning Project Lifecycle
27 - Steps of a Machine Learning Project.mp4
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31.1 MB
28 - Challenges in RealWorld ML Projects.mp4
MP4
39.2 MB
29 - Ethics and Responsible AI.mp4
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61.6 MB
9 - Course Summary and Recap
30 - Big Picture Recap.mp4
MP4
61.1 MB
31 - Next Steps in Your ML Learning Journey.mp4
MP4
46.5 MB
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1 - First Contact With Machine Learning
10 - Hyperparameter Optimization
11 - Decision Tree Algorithm In Machine Learning Az
12 - Random Forest Algorithm In Machine Learning Az
13 - Support Vector Machine Algorithm In Machine Learning Az
14 - Unsupervised Learning With Machine Learning
15 - K Means Clustering Algorithm In Machine Learning Az
52 - K Means Clustering Algorithm With Python Part 2.mp4
MP4
21.7 MB
16 - Hierarchical Clustering Algorithm In Machine Learning Data Science
57 - Hierarchical Clustering Algorithm With Python Part 2.mp4
MP4
21 MB
17 - Principal Component Analysis Pca In Machine Learning Az
60 - Principal Component Analysis Pca With Python Part 2.mp4
MP4
5.1 MB
18 - Recommender System Algorithm In Machine Learning Az
63 - What Is The Recommender System Part 2.mp4
MP4
12.4 MB
19 - Extra
2 - Installations For Python
6 - Installing Anaconda Distribution For Windows.mp4
MP4
69.6 MB
7 - Installing Anaconda Distribution For Macos.mp4
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71.7 MB
8 - Installing Anaconda Distribution For Linux.mp4
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136.2 MB
9 - Overview Of Jupyter Notebook And Google Colab.mp4
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27 MB
3 - Evaluation Metrics In Machine Learning
10 - Classification Vs Regression In Machine Learning.mp4
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12.5 MB
11 - Machine Learning Model Performance Evaluation Classification Error Metrics.mp4
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69.9 MB
12 - Evaluating Performance Regression Error Metrics In Python.mp4
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29.5 MB
13 - Machine Learning With Python.mp4
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69 MB
4 - Supervised Learning With Machine Learning
14 - What Is Supervised Learning In Machine Learning.mp4
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26.5 MB
5 - Linear Regression Algorithm In Machine Learning Az
15 - Linear Regression Algorithm Theory In Machine Learning Az.mp4
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22.3 MB
16 - Linear Regression Algorithm With Python Part 1.mp4
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62.5 MB
17 - Linear Regression Algorithm With Python Part 2.mp4
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78.8 MB
18 - Linear Regression Algorithm With Python Part 3.mp4
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52 MB
19 - Linear Regression Algorithm With Python Part 4.mp4
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67.8 MB
6 - Bias Variance Tradeoff In Machine Learning
20 - What Is Bias Variance Tradeoff.mp4
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36.4 MB
7 - Logistic Regression Algorithm In Machine Learning Az
21 - What Is Logistic Regression Algorithm In Machine Learning.mp4
MP4
17.7 MB
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10 Python Courses For Beginners 2020 [UdemyLibrary.com]
Machine Learning Crash Course for Beginners using Python [UdemyLibrary.com]
3. Process of Learning from Data
2. UnSupervised Learning and Reinforcement Learning.mp4
MP4
70.07 MB
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2 -Machine Learning - Unsupervised and Reinforcement Learning.mp4
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86.1 MB
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2. Unsupervised Machine Learning.mp4
MP4
43.5 MB
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2021 Python for Data Science & Machine Learning from A-Z
[TutsNode.com] - 2021 Python for Data Science & Machine Learning from A-Z
18. K-means
2. Unsupervised Machine Learning Continued.mp4
MP4
83.13 MB
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Cluster Analysis & Unsupervised Machine Learning in R
[TutsNode.com] - Cluster Analysis & Unsupervised Machine Learning in R
9. Applied Example unsupervised K-means learning for mapping applications
2. Import images and their visualization in Earth Engine.mp4
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141.91 MB
2. Software used in this course
5. Interface of Google Earth Engine Code Editor & Explorer.mp4
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127.12 MB
3. Lab Get started with R in RStudio.mp4
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47.7 MB
4. Sign up for Google Earth Engine (needed for your projects later in the course).mp4
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45.67 MB
2. How to install R and RStudio in 2020.mp4
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38.7 MB
1. What is R and RStudio.mp4
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12.23 MB
8. Independent Project in Cluster Analysis based on Case Study
2. Project Assignment.mp4
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68.24 MB
6. More Unsupervised Clustering techniques Hands-On
2. Entropy Weighted K-Means in R.mp4
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61.92 MB
7. Performance Evaluation of Unsupervised Learning Clustering Algorithms in R
2. Selecting the number of clusters for unsupervised Clustering methods (K-Means).mp4
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59.33 MB
1. Introduction
2. What is Machine Leraning and it's main types.mp4
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46.86 MB
4. Unsupervised learning Hierarchical Clustering in R
2. Hierarchical Clustering Example.mp4
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26.64 MB
3. R Crash Course - get started with R-programming in R-Studio
2. Lab Installing Packages and Package Management in R.mp4
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24.15 MB
5. Unsupervised Learning K-Means Clustering
2. Example K-Means Clustering in R Lab.mp4
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22.83 MB
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Unsupervised Machine Learning Hidden Markov Models in Python
[TutsNode.com] - Unsupervised Machine Learning Hidden Markov Models in Python
1. Introduction and Outline
2. Unsupervised or Supervised.mp4
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5.28 MB
2. Markov Models
1. The Markov Property.mp4
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24.06 MB
2. Markov Models.mp4
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32.47 MB
3. The Math of Markov Chains.mp4
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23.94 MB
3. Markov Models Example Problems and Applications
2. Example Problem Expected number of continuously sick days.mp4
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4.63 MB
4. Hidden Markov Models for Discrete Observations
2. HMM - Basic Examples.mp4
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42.35 MB
6. The Forward-Backward Algorithm (part 2).mp4
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27.56 MB
9. The Viterbi Algorithm (part 2).mp4
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59.34 MB
11. HMM Training (part 2).mp4
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39.98 MB
5. Discrete HMMs Using Deep Learning Libraries
2. Theano Scan Tutorial.mp4
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23.76 MB
6. HMMs for Continuous Observations
2. Generating Data from a Real-Valued HMM.mp4
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14.94 MB
4. Continuous-Observation HMM in Code (part 2).mp4
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15.28 MB
7. HMMs for Classification
2. HMM Classification on Poetry Data (Robert Frost vs. Edgar Allan Poe).mp4
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24.39 MB
8. Bonus Example Parts-of-Speech Tagging
2. POS Tagging with an HMM.mp4
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14.38 MB
9. Theano, Tensorflow, and Machine Learning Basics Review
2. (Review) Theano Tutorial.mp4
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19.86 MB
10. Setting Up Your Environment (FAQ by Student Request)
2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4
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43.92 MB
11. Extra Help With Python Coding for Beginners (FAQ by Student Request)
2. How to Code by Yourself (part 2).mp4
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14.8 MB
4. Python 2 vs Python 3.mp4
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7.83 MB
12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)
2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4
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38.95 MB
4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4
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37.62 MB
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[DesireCourse.Net] Udemy - Cluster Analysis and Unsupervised Machine Learning in Python
1. Introduction to Unsupervised Learning
2. What is unsupervised learning used for.mp4
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7.58 MB
2. K-Means Clustering
1. An Easy Introduction to K-Means Clustering.mp4
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12.56 MB
10. Using K-Means on Real Data MNIST.mp4
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10.71 MB
11. One Way to Choose K.mp4
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9.08 MB
12. K-Means Application Finding Clusters of Related Words.mp4
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25.99 MB
2. Visual Walkthrough of the K-Means Clustering Algorithm.mp4
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4.87 MB
3. Soft K-Means.mp4
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25.25 MB
4. The K-Means Objective Function.mp4
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3.02 MB
5. Soft K-Means in Python Code.mp4
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30.21 MB
6. Visualizing Each Step of K-Means.mp4
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5.26 MB
7. Examples of where K-Means can fail.mp4
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17 MB
8. Disadvantages of K-Means Clustering.mp4
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3.87 MB
9. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).mp4
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11.39 MB
3. Hierarchical Clustering
2. Agglomerative Clustering Options.mp4
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6.23 MB
4. Gaussian Mixture Models (GMMs)
2. Comparison between GMM and K-Means.mp4
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3 MB
5. Appendix
11. What order should I take your courses in (part 2).mp4
MP4
37.63 MB
2. Windows-Focused Environment Setup 2018.mp4
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186.31 MB
5. How to Code by Yourself (part 2).mp4
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14.81 MB
9. Python 2 vs Python 3.mp4
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7.84 MB
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Udacity - Machine Learning Engineer Nanodegree
Udacity - Machine Learning Engineer Nanodegree nd009t v1.0.0
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation
02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.mp4
MP4
4.34 MB
10. MLND - Unsupervised Learning - L3 10 Expectation Maximization Pt 2 MAIN V1 V2-B_xXd0mFUm4.mp4
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26.3 MB
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