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1. BiasVariance Tradeoff.mp4
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1. Deep Learning Pre-Requisites.mp4
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1. Deploying Models to Real-Time Systems.mp4
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1. Introduction.mp4
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1. K-Nearest-Neighbors Concepts.mp4
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1. More to Explore.mp4
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1. Supervised vs. Unsupervised Learning, and TrainTest.mp4
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1. Types of Data.mp4
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1. User-Based Collaborative Filtering.mp4
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1. Warning about Java 11 and Spark 3!.html
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1. Your final project assignment.mp4
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51.6 MB
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1. [Activity] Linear Regression.mp4
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100.5 MB
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10. Binning, Transforming, Encoding, Scaling, and Shuffling.mp4
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10. TF IDF.mp4
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10. [Activity] Covariance and Correlation.mp4
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10. [Activity] Covariance and Correlation.srt
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10. [Activity] LINUX Installing Graphviz.mp4
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10. [Activity] LINUX Installing Graphviz.srt
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10. [Activity] Python Basics, Part 4 [Optional].mp4
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10. [Activity] Using Keras to Predict Political Affiliations.mp4
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88.2 MB
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10. [Activity] Using Keras to Predict Political Affiliations.srt
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11. Convolutional Neural Networks (CNN's).mp4
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11. Decision Trees Concepts.mp4
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11. Introducing the Pandas Library [Optional].mp4
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123.1 MB
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11. Introducing the Pandas Library [Optional].srt
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11. [Activity] Searching Wikipedia with Spark.mp4
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103 MB
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11. [Activity] Searching Wikipedia with Spark.srt
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11. [Exercise] Conditional Probability.mp4
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11. [Exercise] Conditional Probability.srt
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12. Exercise Solution Conditional Probability of Purchase by Age.mp4
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12. [Activity] Decision Trees Predicting Hiring Decisions.mp4
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12. [Activity] Using CNN's for handwriting recognition.mp4
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12. [Activity] Using the Spark 2.0 DataFrame API for MLLib.mp4
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13. Bayes' Theorem.mp4
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13. Ensemble Learning.mp4
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13. Recurrent Neural Networks (RNN's).mp4
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14. Support Vector Machines (SVM) Overview.mp4
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14. Support Vector Machines (SVM) Overview.srt
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14. [Activity] Using a RNN for sentiment analysis.mp4
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15. [Activity] Transfer Learning.mp4
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15. [Activity] Transfer Learning.srt
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15. [Activity] Using SVM to cluster people using scikit-learn.mp4
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15. [Activity] Using SVM to cluster people using scikit-learn.srt
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16. Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.mp4
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16. Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.srt
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17. Deep Learning Regularization with Dropout and Early Stopping.mp4
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18. The Ethics of Deep Learning.mp4
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18. The Ethics of Deep Learning.srt
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19. Learning More about Deep Learning.mp4
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2. AB Testing Concepts.mp4
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2. Don't Forget to Leave a Rating!.html
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2. Final project review.mp4
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2. Final project review.srt
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2. Item-Based Collaborative Filtering.mp4
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75 MB
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2. Mean, Median, Mode.mp4
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2. Mean, Median, Mode.srt
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2. Spark installation notes for MacOS and Linux users.html
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2. The History of Artificial Neural Networks.mp4
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80 MB
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2. Udemy 101 Getting the Most From This Course.mp4
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2. [Activity] K-Fold Cross-Validation to avoid overfitting.mp4
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2. [Activity] Polynomial Regression.mp4
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2. [Activity] Using KNN to predict a rating for a movie.mp4
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142.1 MB
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2. [Activity] Using KNN to predict a rating for a movie.srt
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2. [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression.mp4
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58.1 MB
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2. [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression.srt
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3. Bayesian Methods Concepts.mp4
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40.7 MB
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3. Bayesian Methods Concepts.srt
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3. Bonus Lecture More courses to explore!.html
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3. Data Cleaning and Normalization.mp4
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3. Dimensionality Reduction; Principal Component Analysis.mp4
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3. Dimensionality Reduction; Principal Component Analysis.srt
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3. Installation Getting Started.html
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307 B
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3. T-Tests and P-Values.mp4
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64.9 MB
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3. T-Tests and P-Values.srt
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3. [Activity] Deep Learning in the Tensorflow Playground.mp4
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141.6 MB
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3. [Activity] Finding Movie Similarities.mp4
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107.8 MB
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3. [Activity] Finding Movie Similarities.srt
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3. [Activity] Installing Spark - Part 1.mp4
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3. [Activity] Multiple Regression, and Predicting Car Prices.mp4
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3. [Activity] Using mean, median, and mode in Python.mp4
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61.9 MB
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3. [Activity] Using mean, median, and mode in Python.srt
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3.1 winutils.exe.html
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4. Deep Learning Details.mp4
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4. Multi-Level Models.mp4
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4. Multi-Level Models.srt
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4. [Activity] Cleaning web log data.mp4
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4. [Activity] Cleaning web log data.srt
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4. [Activity] Hands-on With T-Tests.mp4
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4. [Activity] Hands-on With T-Tests.srt
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4. [Activity] Implementing a Spam Classifier with Naive Bayes.mp4
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89.1 MB
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4. [Activity] Implementing a Spam Classifier with Naive Bayes.srt
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4. [Activity] Improving the Results of Movie Similarities.mp4
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94.9 MB
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4. [Activity] Improving the Results of Movie Similarities.srt
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4. [Activity] Installing Spark - Part 2.mp4
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112 MB
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4. [Activity] Installing Spark - Part 2.srt
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4. [Activity] PCA Example with the Iris data set.mp4
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109.7 MB
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4. [Activity] PCA Example with the Iris data set.srt
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4. [Activity] Variation and Standard Deviation.mp4
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110.8 MB
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4. [Activity] Variation and Standard Deviation.srt
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4. [Activity] WINDOWS Installing and Using Anaconda & Course Materials.mp4
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102.8 MB
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4. [Activity] WINDOWS Installing and Using Anaconda & Course Materials.srt
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4.1 winutils.exe.html
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102 B
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5. Data Warehousing Overview ETL and ELT.mp4
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103.3 MB
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5. Determining How Long to Run an Experiment.mp4
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5. Determining How Long to Run an Experiment.srt
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5. Introducing Tensorflow.mp4
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5. K-Means Clustering.mp4
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5. K-Means Clustering.srt
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5. Normalizing numerical data.mp4
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5. Normalizing numerical data.srt
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5. Probability Density Function; Probability Mass Function.mp4
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30.1 MB
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5. Probability Density Function; Probability Mass Function.srt
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5. Spark Introduction.mp4
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5. Spark Introduction.srt
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5. [Activity] MAC Installing and Using Anaconda & Course Materials.mp4
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96.5 MB
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5. [Activity] MAC Installing and Using Anaconda & Course Materials.srt
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5. [Activity] Making Movie Recommendations to People.mp4
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132.6 MB
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5. [Activity] Making Movie Recommendations to People.srt
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6. AB Test Gotchas.mp4
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6. AB Test Gotchas.srt
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6. Common Data Distributions.mp4
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6. Common Data Distributions.srt
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6. Important note about Tensorflow 2.html
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614 B
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6. Reinforcement Learning.mp4
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132.3 MB
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6. Reinforcement Learning.srt
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6. Spark and the Resilient Distributed Dataset (RDD).mp4
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98.5 MB
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6. Spark and the Resilient Distributed Dataset (RDD).srt
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6. [Activity] Clustering people based on income and age.mp4
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6. [Activity] Clustering people based on income and age.srt
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6. [Activity] Detecting outliers.mp4
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6. [Activity] Detecting outliers.srt
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6. [Activity] LINUX Installing and Using Anaconda & Course Materials.mp4
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80.2 MB
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6. [Activity] LINUX Installing and Using Anaconda & Course Materials.srt
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6. [Exercise] Improve the recommender's results.mp4
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84.2 MB
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6. [Exercise] Improve the recommender's results.srt
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6.1 Python Markov Decision Process Toolbox.html
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102 B
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6.2 Pac-Man Example.html
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102 B
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6.3 Cat and Mouse Example.html
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102 B
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7. Feature Engineering and the Curse of Dimensionality.mp4
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7. Feature Engineering and the Curse of Dimensionality.srt
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7. Introducing MLLib.mp4
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54.8 MB
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7. Measuring Entropy.mp4
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35 MB
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7. Measuring Entropy.srt
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7. Python Basics, Part 1 [Optional].mp4
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33 MB
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7. Python Basics, Part 1 [Optional].srt
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7. [Activity] Percentiles and Moments.mp4
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114 MB
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7. [Activity] Percentiles and Moments.srt
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7. [Activity] Reinforcement Learning & Q-Learning with Gym.mp4
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78 MB
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7. [Activity] Reinforcement Learning & Q-Learning with Gym.srt
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7. [Activity] Using Tensorflow, Part 1.mp4
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118.2 MB
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8. Imputation Techniques for Missing Data.mp4
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49 MB
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8. Imputation Techniques for Missing Data.srt
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8. Introduction to Decision Trees in Spark.mp4
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134 MB
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8. Introduction to Decision Trees in Spark.srt
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8. Understanding a Confusion Matrix.mp4
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14.8 MB
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8. Understanding a Confusion Matrix.srt
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8. [Activity] A Crash Course in matplotlib.mp4
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8. [Activity] Python Basics, Part 2 [Optional].mp4
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20.6 MB
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8. [Activity] Python Basics, Part 2 [Optional].srt
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8. [Activity] Using Tensorflow, Part 2.mp4
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104.5 MB
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8. [Activity] Using Tensorflow, Part 2.srt
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8. [Activity] WINDOWS Installing Graphviz.mp4
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2.1 MB
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9. Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.mp4
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MP4
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36.3 MB
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9. Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.srt
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SRT
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9.9 KB
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9. Measuring Classifiers (Precision, Recall, F1, ROC, AUC).mp4
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MP4
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25.8 MB
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9. Measuring Classifiers (Precision, Recall, F1, ROC, AUC).srt
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SRT
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10.8 KB
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9. [Activity] Advanced Visualization with Seaborn.mp4
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MP4
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147.8 MB
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9. [Activity] Advanced Visualization with Seaborn.srt
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SRT
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30 KB
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9. [Activity] Introducing Keras.mp4
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MP4
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92.1 MB
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9. [Activity] Introducing Keras.srt
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SRT
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23.7 KB
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9. [Activity] K-Means Clustering in Spark.mp4
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MP4
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117.9 MB
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9. [Activity] K-Means Clustering in Spark.srt
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SRT
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17.7 KB
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9. [Activity] MAC Installing Graphviz.mp4
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MP4
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14.8 MB
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9. [Activity] MAC Installing Graphviz.srt
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SRT
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1.3 KB
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9. [Activity] Python Basics, Part 3 [Optional].mp4
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MP4
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10.1 MB
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9. [Activity] Python Basics, Part 3 [Optional].srt
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SRT
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4.2 KB
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[FreeAllCourse.Com].URL
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URL
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204 B
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