└─ Udemy ->
├─ Unsupervised Learning ->
├─ Unsupervised Machine Learning Hidden Markov Models in Python 2018-10 ->
├─ 9. Basics Review ->
├─ 3. (Review) Tensorflow Tutorial.vtt - 5.61 KB
├─ 3. (Review) Tensorflow Tutorial.mp4 - 13.89 MB
├─ 2. (Review) Theano Tutorial.vtt - 7.07 KB
├─ 2. (Review) Theano Tutorial.mp4 - 19.86 MB
├─ 1. (Review) Gaussian Mixture Models.vtt - 3.33 KB
└─ 1. (Review) Gaussian Mixture Models.mp4 - 4.99 MB
├─ 8. Bonus Example Parts-of-Speech Tagging ->
├─ 2. POS Tagging with an HMM.vtt - 4.56 KB
├─ 2. POS Tagging with an HMM.mp4 - 14.39 MB
├─ 1. Parts-of-Speech Tagging Concepts.vtt - 6.34 KB
└─ 1. Parts-of-Speech Tagging Concepts.mp4 - 8.51 MB
├─ 7. HMMs for Classification ->
├─ 2. HMM Classification on Poetry Data (Robert Frost vs. Edgar Allan Poe).vtt - 7.79 KB
├─ 2. HMM Classification on Poetry Data (Robert Frost vs. Edgar Allan Poe).mp4 - 24.39 MB
├─ 1. Generative vs. Discriminative Classifiers.vtt - 3.26 KB
└─ 1. Generative vs. Discriminative Classifiers.mp4 - 4.12 MB
├─ 6. HMMs for Continuous Observations ->
├─ 6. Continuous HMM in Tensorflow.vtt - 10.36 KB
├─ 6. Continuous HMM in Tensorflow.mp4 - 22.46 MB
├─ 5. Continuous HMM in Theano.vtt - 10.45 KB
├─ 5. Continuous HMM in Theano.mp4 - 45.41 MB
├─ 4. Continuous-Observation HMM in Code (part 2).vtt - 2.9 KB
├─ 4. Continuous-Observation HMM in Code (part 2).mp4 - 15.29 MB
├─ 3. Continuous-Observation HMM in Code (part 1).vtt - 11.3 KB
├─ 3. Continuous-Observation HMM in Code (part 1).mp4 - 46.69 MB
└─ 2. Generating Data from a Real-Valued HMM.vtt - 3.99 KB
└─ …………………………
├─ 5. Discrete HMMs Using Deep Learning Libraries ->
├─ 6. Discrete HMM in Tensorflow.vtt - 8.44 KB
├─ 6. Discrete HMM in Tensorflow.mp4 - 16.44 MB
├─ 5. Tensorflow Scan Tutorial.vtt - 14.03 KB
├─ 5. Tensorflow Scan Tutorial.mp4 - 23.07 MB
├─ 4. Improving our Gradient Descent-Based HMM.vtt - 5.91 KB
├─ 4. Improving our Gradient Descent-Based HMM.mp4 - 25.95 MB
├─ 3. Discrete HMM in Theano.vtt - 7.4 KB
├─ 3. Discrete HMM in Theano.mp4 - 30.74 MB
└─ 2. Theano Scan Tutorial.vtt - 11.26 KB
└─ …………………………
├─ 4. Hidden Markov Models for Discrete Observations ->
├─ 9. Baum-Welch Explanation and Intuition.vtt - 8.06 KB
├─ 9. Baum-Welch Explanation and Intuition.mp4 - 11.97 MB
├─ 8. The Baum-Welch Algorithm.vtt - 2.97 KB
├─ 8. The Baum-Welch Algorithm.mp4 - 4.35 MB
├─ 7. Visual Intuition for the Viterbi Algorithm.vtt - 3.91 KB
├─ 7. Visual Intuition for the Viterbi Algorithm.mp4 - 15.68 MB
├─ 6. The Viterbi Algorithm.vtt - 3.49 KB
├─ 6. The Viterbi Algorithm.mp4 - 5.04 MB
└─ 5. Visual Intuition for the Forward Algorithm.vtt - 4.46 KB
└─ …………………………
└─ …………………………
├─ Unsupervised Deep Learning in Python 2018-11 ->
├─ 9. Applications to Recommender Systems ->
├─ 9. Recommender RBM Code pt 3.vtt - 11.98 KB
├─ 9. Recommender RBM Code pt 3.mp4 - 128.54 MB
├─ 8. Recommender RBM Code pt 2.vtt - 4.63 KB
├─ 8. Recommender RBM Code pt 2.mp4 - 39.58 MB
├─ 7. Recommender RBM Code pt 1.vtt - 8.74 KB
├─ 7. Recommender RBM Code pt 1.mp4 - 70.42 MB
├─ 6. Categorical RBM for Recommender System Ratings.vtt - 12.05 KB
├─ 6. Categorical RBM for Recommender System Ratings.mp4 - 47.59 MB
└─ 5. AutoRec in Code.vtt - 12.62 KB
└─ …………………………
├─ 8. Applications to NLP (Natural Language Processing) ->
├─ 3. Application of t-SNE + K-Means Finding Clusters of Related Words.vtt - 351 B
├─ 3. Application of t-SNE + K-Means Finding Clusters of Related Words.mp4 - 25.99 MB
├─ 2. Latent Semantic Analysis in Code.vtt - 351 B
├─ 2. Latent Semantic Analysis in Code.mp4 - 25.62 MB
├─ 1. Application of PCA and SVD to NLP (Natural Language Processing).vtt - 351 B
└─ 1. Application of PCA and SVD to NLP (Natural Language Processing).mp4 - 3.93 MB
├─ 7. Extras + Visualizing what features a neural network has learned ->
├─ 1. Exercises on feature visualization and interpretation.vtt - 351 B
└─ 1. Exercises on feature visualization and interpretation.mp4 - 3.75 MB
├─ 6. The Vanishing Gradient Problem ->
├─ 2. The Vanishing Gradient Problem Demo in Code.vtt - 351 B
├─ 2. The Vanishing Gradient Problem Demo in Code.mp4 - 31.29 MB
├─ 1. The Vanishing Gradient Problem Description.vtt - 351 B
└─ 1. The Vanishing Gradient Problem Description.mp4 - 5.2 MB
├─ 5. Restricted Boltzmann Machines ->
├─ 9. RBM Greedy Layer-Wise Pretraining.vtt - 5.19 KB
├─ 9. RBM Greedy Layer-Wise Pretraining.mp4 - 23.62 MB
├─ 8. Training an RBM (part 3) - Free Energy.vtt - 7.03 KB
├─ 8. Training an RBM (part 3) - Free Energy.mp4 - 27.58 MB
├─ 7. Training an RBM (part 2).vtt - 6.44 KB
├─ 7. Training an RBM (part 2).mp4 - 27.34 MB
├─ 6. Training an RBM (part 1).vtt - 11.76 KB
├─ 6. Training an RBM (part 1).mp4 - 49.08 MB
└─ 5. Neural Network Equations.vtt - 7.42 KB
└─ …………………………
├─ 4. Autoencoders ->
├─ 9. Cross Entropy vs. KL Divergence.vtt - 5.48 KB
├─ 9. Cross Entropy vs. KL Divergence.mp4 - 7.42 MB
├─ 8. Testing greedy layer-wise autoencoder training vs. pure backpropagation.vtt - 1.86 KB
├─ 8. Testing greedy layer-wise autoencoder training vs. pure backpropagation.mp4 - 18.53 MB
├─ 7. Autoencoder in Code (Tensorflow).vtt - 8.17 KB
├─ 7. Autoencoder in Code (Tensorflow).mp4 - 24.45 MB
├─ 6. Writing the deep neural network class in code (Theano).vtt - 6.37 KB
├─ 6. Writing the deep neural network class in code (Theano).mp4 - 41.97 MB
└─ 5. Testing our Autoencoder (Theano).vtt - 2.67 KB
└─ …………………………
└─ …………………………
└─ Cluster Analysis and Unsupervised Machine Learning in Python 2018-10 ->
├─ 5. Appendix ->
├─ 9. Python 2 vs Python 3.vtt - 5.35 KB
├─ 9. Python 2 vs Python 3.mp4 - 7.84 MB
├─ 8. Proof that using Jupyter Notebook is the same as not using it.vtt - 78.3 MB
├─ 8. Proof that using Jupyter Notebook is the same as not using it.mp4 - 78.29 MB
├─ 7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt - 27.77 KB
├─ 7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 - 38.95 MB
├─ 6. How to Succeed in this Course (Long Version).vtt - 12.79 KB
├─ 6. How to Succeed in this Course (Long Version).mp4 - 18.31 MB
└─ 5. How to Code by Yourself (part 2).vtt - 11.62 KB
└─ …………………………
├─ 4. Gaussian Mixture Models (GMMs) ->
├─ 7. Future Unsupervised Learning Algorithms You Will Learn.vtt - 1.28 KB
├─ 7. Future Unsupervised Learning Algorithms You Will Learn.mp4 - 1.95 MB
├─ 6. Expectation-Maximization.vtt - 2.42 KB
├─ 6. Expectation-Maximization.mp4 - 3.5 MB
├─ 5. Kernel Density Estimation.vtt - 2.9 KB
├─ 5. Kernel Density Estimation.mp4 - 3.71 MB
├─ 4. Practical Issues with GMM Singular Covariance.vtt - 3.59 KB
├─ 4. Practical Issues with GMM Singular Covariance.mp4 - 4.96 MB
└─ 3. Write a Gaussian Mixture Model in Python Code.vtt - 6.86 KB
└─ …………………………
├─ 3. Hierarchical Clustering ->
├─ 5. Application Donald Trump vs. Hillary Clinton Tweets.vtt - 16.9 KB
├─ 5. Application Donald Trump vs. Hillary Clinton Tweets.mp4 - 35.28 MB
├─ 4. Application Evolution.vtt - 14.31 KB
├─ 4. Application Evolution.mp4 - 26.4 MB
├─ 3. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.vtt - 3.9 KB
├─ 3. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.mp4 - 11.86 MB
├─ 2. Agglomerative Clustering Options.vtt - 4.89 KB
├─ 2. Agglomerative Clustering Options.mp4 - 6.23 MB
└─ 1. Visual Walkthrough of Agglomerative Hierarchical Clustering.vtt - 3.16 KB
└─ …………………………
├─ 2. K-Means Clustering ->
├─ 9. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).vtt - 8.11 KB
├─ 9. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).mp4 - 11.39 MB
├─ 8. Disadvantages of K-Means Clustering.vtt - 2.96 KB
├─ 8. Disadvantages of K-Means Clustering.mp4 - 3.87 MB
├─ 7. Examples of where K-Means can fail.vtt - 4.46 KB
├─ 7. Examples of where K-Means can fail.mp4 - 17 MB
├─ 6. Visualizing Each Step of K-Means.vtt - 2.4 KB
├─ 6. Visualizing Each Step of K-Means.mp4 - 5.26 MB
└─ 5. Soft K-Means in Python Code.vtt - 6.91 KB
└─ …………………………
├─ 1. Introduction to Unsupervised Learning ->
├─ 4. How to Succeed in this Course.vtt - 3.49 KB
├─ 4. How to Succeed in this Course.mp4 - 3.3 MB
├─ 3. Why Use Clustering.vtt - 5.2 KB
├─ 3. Why Use Clustering.mp4 - 6.64 MB
├─ 2. What is unsupervised learning used for.vtt - 5.3 KB
├─ 2. What is unsupervised learning used for.mp4 - 7.58 MB
├─ 1. Introduction and Outline.vtt - 3.18 KB
└─ 1. Introduction and Outline.mp4 - 4.11 MB
├─ README.md - 5.62 KB
└─ 825684_ee00.jpg - 81.73 KB
├─ Udemy_Professional Certificate in Machine Learning ->
├─ 9. Logistic regression ->
├─ 1. Logistic regression.srt - 9.48 KB
└─ 1. Logistic regression.mp4 - 146.99 MB
├─ 8. Linear regression ->
├─ 4. Multivariate Linear Regression Demo [Hands-on] Linear Regression.srt - 17.86 KB
├─ 4. Multivariate Linear Regression Demo [Hands-on] Linear Regression.mp4 - 203.26 MB
├─ 3. Univariate Linear Regression Demo [Hands-on] Part 2- Linear Regression.srt - 26.84 KB
├─ 3. Univariate Linear Regression Demo [Hands-on] Part 2- Linear Regression.mp4 - 267.81 MB
├─ 2. Univariate Linear Regression Demo [Hands-on] Part 1- Linear Regression.srt - 12.16 KB
├─ 2. Univariate Linear Regression Demo [Hands-on] Part 1- Linear Regression.mp4 - 127.13 MB
├─ 1. Linear regression.srt - 2.51 KB
└─ 1. Linear regression.mp4 - 8.68 MB
├─ 7. Natural Language Processing for Data Scientists ->
├─ 9. Part of Speech Tagging Tutorial.srt - 12.54 KB
├─ 9. Part of Speech Tagging Tutorial.mp4 - 73.66 MB
├─ 8. Introduction to Part of Speech Tagging.srt - 12.54 KB
├─ 8. Introduction to Part of Speech Tagging.mp4 - 73.63 MB
├─ 7. Normalization Tutorial.srt - 10.18 KB
├─ 7. Normalization Tutorial.mp4 - 36.17 MB
├─ 6. Introduction to Normalization.srt - 7.81 KB
├─ 6. Introduction to Normalization.mp4 - 32.5 MB
└─ 5. Tokenization Tutorial.srt - 8.77 KB
└─ …………………………
├─ 6. Naive Bayes Classifier with Python [Lecture & Demo] ->
├─ 1. Lecture & Demo Naive bayes classifier.srt - 12.95 KB
└─ 1. Lecture & Demo Naive bayes classifier.mp4 - 108.32 MB
├─ 5. Artificial Neural Networks [ Comprehensive Sessions] ->
├─ 7. Deep Learning -Handwritten Digits Recognition [Step by Step] [Complete Project ].srt - 10.25 KB
├─ 7. Deep Learning -Handwritten Digits Recognition [Step by Step] [Complete Project ].mp4 - 140.28 MB
├─ 6. KERAS Tutorial - Developing an Artificial Neural Network in Python -Step by Step.srt - 17.22 KB
├─ 6. KERAS Tutorial - Developing an Artificial Neural Network in Python -Step by Step.mp4 - 240.25 MB
├─ 5. ANN - Illustrative Example.srt - 9.23 KB
├─ 5. ANN - Illustrative Example.mp4 - 63.78 MB
├─ 4. Creating a simple layer of neurons, with 4 inputs. # Python # From scratch.srt - 23.73 KB
├─ 4. Creating a simple layer of neurons, with 4 inputs. # Python # From scratch.mp4 - 217.06 MB
└─ 3. Multiple Input Neuron.srt - 6.17 KB
└─ …………………………
├─ 4. Data Visualization with Python ->
├─ 2. Data Visualization with Python Histogram , Pie Chart, etc...srt - 3.48 KB
├─ 2. Data Visualization with Python Histogram , Pie Chart, etc...mp4 - 43.59 MB
├─ 1. Data preparation and Bar Chart.srt - 6.51 KB
└─ 1. Data preparation and Bar Chart.mp4 - 72.69 MB
└─ …………………………
├─ Udemy - Master SQL For Data Science 2019-8 ->
├─ 9. Working with Multiple Tables ->
├─ 7. ADVANCED Problems using Joins, Grouping and Subqueries.html - 135 B
├─ 6. Creating Views vs. Inline Views.vtt - 11.69 KB
├─ 6. Creating Views vs. Inline Views.mp4 - 80.44 MB
├─ 5. [EXERCISES] Joins and Subqueries Continued.vtt - 20.3 KB
├─ 5. [EXERCISES] Joins and Subqueries Continued.mp4 - 27.38 MB
├─ 4. Cartesian Product with the CROSS JOIN.vtt - 8.06 KB
├─ 4. Cartesian Product with the CROSS JOIN.mp4 - 11.56 MB
├─ 3. Using UNION, UNION ALL and EXCEPT Clauses + [EXERCISES].vtt - 18.5 KB
└─ 3. Using UNION, UNION ALL and EXCEPT Clauses + [EXERCISES].mp4 - 20.96 MB
└─ …………………………
├─ 8. Advanced Query Techniques using Correlated Subqueries ->
├─ 2. [EXERCISES] Correlated Subqueries Continued.vtt - 18.85 KB
├─ 2. [EXERCISES] Correlated Subqueries Continued.mp4 - 26.11 MB
├─ 1. Understanding Correlated Subqueries.vtt - 24.88 KB
└─ 1. Understanding Correlated Subqueries.mp4 - 32.05 MB
├─ 7. Using the CASE Clause in Interesting Ways ->
├─ 3. Practice Using Case and Transposing Data.html - 135 B
├─ 2. Transposing Data using the CASE Clause + [EXERCISES].vtt - 20.8 KB
├─ 2. Transposing Data using the CASE Clause + [EXERCISES].mp4 - 30.2 MB
├─ 1. Conditional Expressions Using CASE Clause + [EXERCISES].vtt - 24.24 KB
└─ 1. Conditional Expressions Using CASE Clause + [EXERCISES].mp4 - 33.08 MB
├─ 6. Using Subqueries ->
├─ 6. Practice with Subqueries.html - 135 B
├─ 5. [EXERCISES] More Practice with Subqueries.vtt - 18.34 KB
├─ 5. [EXERCISES] More Practice with Subqueries.mp4 - 22.47 MB
├─ 4. Subqueries with ANY and ALL Operators + [EXERCISES].vtt - 23.05 KB
├─ 4. Subqueries with ANY and ALL Operators + [EXERCISES].mp4 - 32.95 MB
├─ 3. Subqueries Continued + [EXERCISES].vtt - 22.12 KB
├─ 3. Subqueries Continued + [EXERCISES].mp4 - 29.73 MB
├─ 2. Introducing Subqueries.vtt - 16.77 KB
└─ 2. Introducing Subqueries.mp4 - 23.89 MB
└─ …………………………
├─ 5. Grouping Data and Computing Aggregates ->
├─ 4. Practice Aggregation Queries.html - 135 B
├─ 3. [EXERCISES] Using GROUP BY and HAVING Clauses.vtt - 20.98 KB
├─ 3. [EXERCISES] Using GROUP BY and HAVING Clauses.mp4 - 25.9 MB
├─ 2. GROUP BY & HAVING Clauses.vtt - 20.89 KB
├─ 2. GROUP BY & HAVING Clauses.mp4 - 25.88 MB
├─ 1. Understanding Grouping.vtt - 7.38 KB
└─ 1. Understanding Grouping.mp4 - 8.91 MB
├─ 4. Using Functions ->
├─ 4. Practice with Functions, Conditional Expressions and Concatenation.html - 135 B
├─ 3. Grouping Functions MIN(), MAX(), AVG(), SUM(), COUNT().vtt - 12.59 KB
├─ 3. Grouping Functions MIN(), MAX(), AVG(), SUM(), COUNT().mp4 - 15.55 MB
├─ 2. String Functions SUBSTRING(), REPLACE(), POSITION() and COALESCE().vtt - 20.18 KB
├─ 2. String Functions SUBSTRING(), REPLACE(), POSITION() and COALESCE().mp4 - 23.87 MB
├─ 1. UPPER(), LOWER(), LENGTH(), TRIM() + Boolean Expressions & Concatenation.vtt - 19.64 KB
└─ 1. UPPER(), LOWER(), LENGTH(), TRIM() + Boolean Expressions & Concatenation.mp4 - 25.01 MB
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