Cluster Analysis and Unsupervised Machine Learning in Python

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Cluster Analysis and Unsupervised Machine Learning in Python

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文件列表

  • Chapter 7 Setting Up Your Environment (Appendix)/002. Anaconda Environment Setup.mp4 69.7 MB
  • Chapter 6 Gaussian Mixture Models (GMMs)/002. Write a Gaussian Mixture Model in Python Code.mp4 65.8 MB
  • Chapter 5 Hierarchical Clustering/005. Application Donald Trump vs. Hillary Clinton Tweets.mp4 52.8 MB
  • Chapter 7 Setting Up Your Environment (Appendix)/003. How to install Numpy, Scipy, Matplotlib, Pandas, and Tensorflow.mp4 51.5 MB
  • Chapter 4 K-Means Clustering/007. Hard K-Means Exercise 3 Solution.mp4 47.8 MB
  • Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/002. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 43.6 MB
  • Chapter 5 Hierarchical Clustering/004. Application Evolution.mp4 39.5 MB
  • Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/004. What order should I take your courses in (part 2).mp4 39.3 MB
  • Chapter 4 K-Means Clustering/021. K-Means Application Finding Clusters of Related Words.mp4 37.2 MB
  • Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/003. Proof that using Jupyter Notebook is the same as not using it.mp4 36.2 MB
  • Chapter 6 Gaussian Mixture Models (GMMs)/001. Gaussian Mixture Model (GMM) Algorithm.mp4 31.9 MB
  • Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/001. How to Code Yourself (part 1).mp4 31.0 MB
  • Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/004. How to use Github & Extra Coding Tips (Optional).mp4 30.6 MB
  • Chapter 4 K-Means Clustering/003. Hard K-Means Exercise 1 Solution.mp4 30.6 MB
  • Chapter 4 K-Means Clustering/013. Soft K-Means in Python Code.mp4 30.4 MB
  • Chapter 4 K-Means Clustering/008. Hard K-Means Objective Theory.mp4 29.9 MB
  • Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/003. What order should I take your courses in (part 1).mp4 29.5 MB
  • Chapter 4 K-Means Clustering/002. Hard K-Means Exercise Prompt 1.mp4 25.9 MB
  • Chapter 6 Gaussian Mixture Models (GMMs)/008. Expectation-Maximization (pt 1).mp4 24.5 MB
  • Chapter 6 Gaussian Mixture Models (GMMs)/007. GMM vs Bayes Classifier (pt 2).mp4 23.2 MB
  • Chapter 3 Unsupervised Learning/002. Why Use Clustering.mp4 22.3 MB
  • Chapter 4 K-Means Clustering/016. Examples of where K-Means can fail.mp4 21.1 MB
  • Chapter 6 Gaussian Mixture Models (GMMs)/006. GMM vs Bayes Classifier (pt 1).mp4 20.5 MB
  • Chapter 4 K-Means Clustering/022. Clustering for NLP and Computer Vision Real-World Applications.mp4 20.4 MB
  • Chapter 6 Gaussian Mixture Models (GMMs)/003. Practical Issues with GMM.mp4 20.2 MB
  • Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/002. How to Code Yourself (part 2).mp4 20.0 MB
  • Chapter 4 K-Means Clustering/006. Hard K-Means Exercise Prompt 3.mp4 20.0 MB
  • Chapter 4 K-Means Clustering/001. An Easy Introduction to K-Means Clustering.mp4 18.4 MB
  • Chapter 4 K-Means Clustering/019. Using K-Means on Real Data MNIST.mp4 18.2 MB
  • Chapter 4 K-Means Clustering/005. Hard K-Means Exercise 2 Solution.mp4 18.1 MB
  • Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/001. How to Succeed in this Course (Long Version).mp4 18.1 MB
  • Chapter 6 Gaussian Mixture Models (GMMs)/010. Expectation-Maximization (pt 3).mp4 16.2 MB
  • z.9781836649373_Code/nlp_class/electronics/unlabeled.review 14.6 MB
  • Chapter 4 K-Means Clustering/009. Hard K-Means Objective Code.mp4 14.5 MB
  • Chapter 1 Welcome/001. Introduction.mp4 14.3 MB
  • Chapter 6 Gaussian Mixture Models (GMMs)/005. Kernel Density Estimation.mp4 14.3 MB
  • Chapter 4 K-Means Clustering/018. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).mp4 13.4 MB
  • Chapter 3 Unsupervised Learning/001. What is unsupervised learning used for.mp4 13.1 MB
  • Chapter 4 K-Means Clustering/004. Hard K-Means Exercise Prompt 2.mp4 12.2 MB
  • Chapter 4 K-Means Clustering/023. Suggestion Box.mp4 11.7 MB
  • Chapter 7 Setting Up Your Environment (Appendix)/001. Pre-Installation Check.mp4 11.6 MB
  • Chapter 5 Hierarchical Clustering/003. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.mp4 11.5 MB
  • Chapter 4 K-Means Clustering/020. One Way to Choose K.mp4 10.9 MB
  • Chapter 4 K-Means Clustering/011. Soft K-Means.mp4 10.6 MB
  • Chapter 2 Getting Set Up/001. Where to get the code.mp4 10.4 MB
  • Chapter 1 Welcome/002. Course Outline.mp4 9.9 MB
  • z.9781836649373_Code/tensorflow/MNIST_data/train-images-idx3-ubyte.gz 9.9 MB
  • Chapter 6 Gaussian Mixture Models (GMMs)/004. Comparison between GMM and K-Means.mp4 9.4 MB
  • Chapter 4 K-Means Clustering/015. Visualizing Each Step of K-Means.mp4 8.2 MB
  • Chapter 4 K-Means Clustering/014. How to Pace Yourself.mp4 8.1 MB
  • Chapter 5 Hierarchical Clustering/002. Agglomerative Clustering Options.mp4 5.8 MB
  • Chapter 6 Gaussian Mixture Models (GMMs)/009. Expectation-Maximization (pt 2).mp4 5.2 MB
  • Chapter 4 K-Means Clustering/010. Visual Walkthrough of the K-Means Clustering Algorithm (Legacy).mp4 4.3 MB
  • Chapter 5 Hierarchical Clustering/001. Visual Walkthrough of Agglomerative Hierarchical Clustering.mp4 4.0 MB
  • Chapter 4 K-Means Clustering/017. Disadvantages of K-Means Clustering.mp4 3.9 MB
  • Chapter 1 Welcome/003. Special Offer.mp4 3.3 MB
  • Chapter 4 K-Means Clustering/012. The K-Means Objective Function.mp4 3.2 MB
  • z.9781836649373_Code/bayesian_ml/3/X_set3.csv 2.4 MB
  • z.9781836649373_Code/tensorflow/MNIST_data/t10k-images-idx3-ubyte.gz 1.6 MB
  • z.9781836649373_Code/bayesian_ml/1/Xtrain.csv 1.4 MB
  • z.9781836649373_Code/bayesian_ml/2/Xtrain.csv 1.4 MB
  • z.9781836649373_Code/rnn_class/gru_nonorm_part1_word_embeddings.npy 1.3 MB
  • z.9781836649373_Code/nlp_class/electronics/negative.review 1.1 MB
  • z.9781836649373_Code/nlp_class/electronics/positive.review 1.1 MB
  • z.9781836649373_Code/mnist_csv/Xtrain.txt 825.8 kB
  • z.9781836649373_Code/nlp_class/spambase.data 702.9 kB
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  • z.9781836649373_Code/bayesian_ml/2/Xtest.csv 241.0 kB
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  • z.9781836649373_Code/mnist_csv/Q.txt 161.3 kB
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  • z.9781836649373_Code/openai/robots_playing_soccer.jpeg 140.3 kB
  • z.9781836649373_Code/nlp_class/all_book_titles.txt 128.0 kB
  • z.9781836649373_Code/bayesian_ml/1/Q.csv 122.0 kB
  • z.9781836649373_Code/bayesian_ml/2/Q.csv 122.0 kB
  • z.9781836649373_Code/bayesian_ml/3/X_set1.csv 97.3 kB
  • z.9781836649373_Code/cnn_class2/styles/flowercarrier.jpg 95.5 kB
  • z.9781836649373_Code/mnist_csv/Xtest.txt 82.5 kB
  • z.9781836649373_Code/cnn_class2/content/sydney.jpg 81.2 kB
  • z.9781836649373_Code/openai/finance.png 75.0 kB
  • z.9781836649373_Code/tf2.0/daily-minimum-temperatures-in-me.csv 68.0 kB
  • z.9781836649373_Code/tf2.0/sbux.csv 61.9 kB
  • z.9781836649373_Code/hmm_class/robert_frost.txt 56.3 kB
  • z.9781836649373_Code/openai/handwriting.jpg 53.4 kB
  • z.9781836649373_Code/openai/webdesign.jpg 50.5 kB
  • z.9781836649373_Code/cnn_class/helloworld.wav 36.9 kB
  • z.9781836649373_Code/hmm_class/helloworld.wav 36.9 kB
  • Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/002. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.en.srt 34.6 kB
  • z.9781836649373_Code/cnn_class2/styles/starrynight.jpg 34.3 kB
  • z.9781836649373_Code/nlp_class2/w2v_word2idx.json 31.5 kB
  • z.9781836649373_Code/rnn_class/gru_nonorm_part1_wikipedia_word2idx.json 31.1 kB
  • z.9781836649373_Code/tf2.0/auto-mpg.data 30.3 kB
  • z.9781836649373_Code/tensorflow/MNIST_data/train-labels-idx1-ubyte.gz 28.9 kB
  • Chapter 6 Gaussian Mixture Models (GMMs)/002. Write a Gaussian Mixture Model in Python Code.en.srt 27.0 kB
  • z.9781836649373_Code/openai/replies.json 26.8 kB
  • z.9781836649373_Code/hmm_class/edgar_allan_poe.txt 26.6 kB
  • Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/004. What order should I take your courses in (part 2).en.srt 26.0 kB
  • z.9781836649373_Code/openai/physics_problem.jpeg 25.4 kB
  • z.9781836649373_Code/pytorch/aapl_msi_sbux.csv 24.1 kB
  • z.9781836649373_Code/tf2.0/aapl_msi_sbux.csv 24.1 kB
  • z.9781836649373_Code/bayesian_ml/1/ytrain.csv 23.6 kB
  • z.9781836649373_Code/bayesian_ml/2/ytrain.csv 23.6 kB
  • Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/001. How to Code Yourself (part 1).en.srt 23.5 kB
  • Chapter 4 K-Means Clustering/007. Hard K-Means Exercise 3 Solution.en.srt 22.6 kB
  • Chapter 7 Setting Up Your Environment (Appendix)/002. Anaconda Environment Setup.en.srt 22.3 kB
  • Chapter 6 Gaussian Mixture Models (GMMs)/001. Gaussian Mixture Model (GMM) Algorithm.en.srt 22.2 kB
  • z.9781836649373_Code/cnn_class2/content/elephant.jpg 22.1 kB
  • Chapter 5 Hierarchical Clustering/005. Application Donald Trump vs. Hillary Clinton Tweets.en.srt 21.0 kB
  • z.9781836649373_Code/data_csv/X.txt 20.1 kB
  • z.9781836649373_Code/unsupervised_class3/dcgan_theano.py 18.5 kB
  • Chapter 5 Hierarchical Clustering/004. Application Evolution.en.srt 18.5 kB
  • Chapter 4 K-Means Clustering/008. Hard K-Means Objective Theory.en.srt 18.4 kB
  • Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/003. What order should I take your courses in (part 1).en.srt 18.4 kB
  • Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/004. How to use Github & Extra Coding Tips (Optional).en.srt 17.0 kB
  • z.9781836649373_Code/unsupervised_class3/dcgan_tf.py 16.4 kB
  • Chapter 9 Effective Learning Strategies for Machine Learning (Appendix)/001. How to Succeed in this Course (Long Version).en.srt 16.4 kB
  • Chapter 6 Gaussian Mixture Models (GMMs)/007. GMM vs Bayes Classifier (pt 2).en.srt 16.3 kB
  • Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/003. Proof that using Jupyter Notebook is the same as not using it.en.srt 16.3 kB
  • Chapter 7 Setting Up Your Environment (Appendix)/003. How to install Numpy, Scipy, Matplotlib, Pandas, and Tensorflow.en.srt 16.3 kB
  • Chapter 6 Gaussian Mixture Models (GMMs)/008. Expectation-Maximization (pt 1).en.srt 16.2 kB
  • Chapter 4 K-Means Clustering/003. Hard K-Means Exercise 1 Solution.en.srt 15.2 kB
  • z.9781836649373_Code/rl2/atari/dqn_theano.py 14.4 kB
  • Chapter 8 Extra Help With Python Coding for Beginners (Appendix)/002. How to Code Yourself (part 2).en.srt 14.2 kB
  • z.9781836649373_Code/kerascv/imagenet_label_names.json 14.2 kB
  • Chapter 6 Gaussian Mixture Models (GMMs)/003. Practical Issues with GMM.en.srt 13.9 kB
  • Chapter 6 Gaussian Mixture Models (GMMs)/006. GMM vs Bayes Classifier (pt 1).en.srt 13.6 kB
  • z.9781836649373_Code/rl2/atari/dqn_tf.py 13.6 kB
  • z.9781836649373_Code/nlp_class3/attention.py 13.4 kB
  • z.9781836649373_Code/nlp_class2/word2vec_tf.py 13.3 kB
  • Chapter 3 Unsupervised Learning/002. Why Use Clustering.en.srt 13.2 kB
  • z.9781836649373_Code/rl/tic_tac_toe.py 13.0 kB
  • Chapter 4 K-Means Clustering/002. Hard K-Means Exercise Prompt 1.en.srt 12.7 kB
  • z.9781836649373_Code/nlp_class3/memory_network.py 12.5 kB
  • z.9781836649373_Code/ann_logistic_extra/ecommerce_data.csv 12.4 kB
  • z.9781836649373_Code/nlp_class2/glove.py 12.0 kB
  • z.9781836649373_Code/nlp_class2/word2vec_theano.py 11.9 kB
  • z.9781836649373_Code/pytorch/rl_trader.py 11.9 kB
  • z.9781836649373_Code/cnn_class2/siamese.py 11.8 kB
  • z.9781836649373_Code/nlp_class2/rntn_theano.py 11.7 kB
  • Chapter 6 Gaussian Mixture Models (GMMs)/010. Expectation-Maximization (pt 3).en.srt 11.1 kB
  • z.9781836649373_Code/nlp_class2/rntn_tensorflow_rnn.py 11.0 kB
  • z.9781836649373_Code/rl3/ddpg.py 11.0 kB
  • z.9781836649373_Code/tf2.0/rl_trader.py 11.0 kB
  • z.9781836649373_Code/nlp_class3/wseq2seq.py 10.7 kB
  • z.9781836649373_Code/supervised_class/dt_without_recursion.py 10.6 kB
  • z.9781836649373_Code/nlp_class2/word2vec.py 10.6 kB
  • Chapter 4 K-Means Clustering/001. An Easy Introduction to K-Means Clustering.en.srt 10.3 kB
  • z.9781836649373_Code/hmm_class/hmmc_scaled_concat_diag.py 10.3 kB
  • z.9781836649373_Code/rl3/a2c/atari_wrappers.py 10.1 kB
  • z.9781836649373_Code/mnist_csv/label_train.txt 10.0 kB
  • z.9781836649373_Code/hmm_class/hmmc.py 10.0 kB
  • z.9781836649373_Code/nlp_class2/recursive_theano.py 9.9 kB
  • z.9781836649373_Code/rl/linear_rl_trader.py 9.8 kB
  • z.9781836649373_Code/rl/grid_world.py 9.8 kB
  • Chapter 4 K-Means Clustering/006. Hard K-Means Exercise Prompt 3.en.srt 9.7 kB
  • Chapter 4 K-Means Clustering/013. Soft K-Means in Python Code.en.srt 9.7 kB
  • Chapter 4 K-Means Clustering/022. Clustering for NLP and Computer Vision Real-World Applications.en.srt 9.7 kB
  • Chapter 4 K-Means Clustering/018. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).en.srt 9.6 kB
  • z.9781836649373_Code/svm_class/svm_smo.py 9.5 kB
  • Chapter 4 K-Means Clustering/021. K-Means Application Finding Clusters of Related Words.en.srt 9.4 kB
  • Chapter 6 Gaussian Mixture Models (GMMs)/005. Kernel Density Estimation.en.srt 9.3 kB
  • z.9781836649373_Code/data_csv/X_orig.txt 9.2 kB
  • z.9781836649373_Code/nlp_class2/pmi.py 9.2 kB
  • Chapter 4 K-Means Clustering/005. Hard K-Means Exercise 2 Solution.en.srt 9.2 kB
  • z.9781836649373_Code/rnn_class/util.py 9.0 kB
  • z.9781836649373_Code/hmm_class/hmmc_scaled_concat.py 9.0 kB
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  • z.9781836649373_Code/unsupervised_class3/vae_tf.py 8.9 kB
  • z.9781836649373_Code/hmm_class/hmmc_concat.py 8.8 kB
  • z.9781836649373_Code/rl3/a2c/a2c.py 8.7 kB
  • z.9781836649373_Code/nlp_class2/glove_theano.py 8.6 kB
  • z.9781836649373_Code/unsupervised_class2/autoencoder.py 8.6 kB
  • z.9781836649373_Code/recommenders/rbm_tf_k.py 8.5 kB
  • z.9781836649373_Code/nlp_class2/glove_tf.py 8.2 kB
  • z.9781836649373_Code/ann_class2/util.py 8.1 kB
  • z.9781836649373_Code/ab_testing/advertisement_clicks.csv 8.0 kB
  • z.9781836649373_Code/unsupervised_class/books.py 8.0 kB
  • z.9781836649373_Code/nlp_class2/rntn_tensorflow.py 8.0 kB
  • Chapter 3 Unsupervised Learning/001. What is unsupervised learning used for.en.srt 7.9 kB
  • z.9781836649373_Code/unsupervised_class2/autoencoder_tf.py 7.9 kB
  • z.9781836649373_Code/rl2/cartpole/pg_tf.py 7.9 kB
  • z.9781836649373_Code/hmm_class/hmmc_tf.py 7.8 kB
  • z.9781836649373_Code/rnn_class/srn_language_tf.py 7.8 kB
  • Chapter 4 K-Means Clustering/011. Soft K-Means.en.srt 7.8 kB
  • z.9781836649373_Code/unsupervised_class3/vae_theano.py 7.7 kB
  • z.9781836649373_Code/rnn_class/srn_language.py 7.7 kB
  • z.9781836649373_Code/cnn_class2/tf_resnet.py 7.7 kB
  • Chapter 1 Welcome/001. Introduction.en.srt 7.6 kB
  • Chapter 4 K-Means Clustering/019. Using K-Means on Real Data MNIST.en.srt 7.4 kB
  • z.9781836649373_Code/recommenders/rbm_tf_k_faster.py 7.4 kB
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