| 1.1 - How to Learn from Appliedaicourse/1.1 - How to Learn from Appliedaicourse.mp4 | 443.51 MB |
| 34.2 - Productionization and deployment of Machine Learning Models/34.2 - Productionization and deployment of Machine Learning Models.mp4.mkv | 267.27 MB |
| 1.2 - How the Job Guarantee program works/1.2 - How the Job Guarantee program works.mp4 | 243.85 MB |
| 5.1 - Numpy Introduction/5.1 - Numpy Introduction.mp4 | 157.08 MB |
| 5.2 - Numerical operations on Numpy/5.2 - Numerical operations on Numpy.mp4 | 156.02 MB |
| 45.9 - Univariate AnalysisGene feature/45.9 - Univariate AnalysisGene feature.mp4 | 144.17 MB |
| 3.1 - Lists/3.1 - Lists.mp4 | 141.28 MB |
| 49.6 - Softmax Classifier on MNIST dataset/49.6 - Softmax Classifier on MNIST dataset..mp4 | 140.09 MB |
| 57.26 - Data Control Language GRANT, REVOKE/57.26 - Data Control Language GRANT, REVOKE.mp4 | 138.66 MB |
| 51.6 - LSTM/51.6 - LSTM..mp4 | 137.09 MB |
| 54.4 - Char-RNN with abc-notation Data preparation/54.4 - Char-RNN with abc-notation Data preparation..mp4 | 131.69 MB |
| 41.9 - EDA Advanced Feature Extraction/41.9 - EDA Advanced Feature Extraction.mp4 | 131.34 MB |
| 51.10 - Code example IMDB Sentiment classification/51.10 - Code example IMDB Sentiment classification.mp4 | 122.78 MB |
| 23.5 - Naive Bayes algorithm/23.5 - Naive Bayes algorithm.mp4 | 116.76 MB |
| 42.13 - Code for bag of words based product similarity/42.13 - Code for bag of words based product similarity.mp4 | 116.3 MB |
| 50.2 - ConvolutionEdge Detection on images/50.2 - ConvolutionEdge Detection on images..mp4 | 115.95 MB |
| 23.6 - Toy example Train and test stages/23.6 - Toy example Train and test stages.mp4 | 115.88 MB |
| 45.13 - Baseline Model Naive Bayes/45.13 - Baseline Model Naive Bayes.mp4 | 115.39 MB |
| 53.12 - Test and visualize the output/53.12 - Test and visualize the output..mp4 | 113.79 MB |
| 17.1 - Dataset overview Amazon Fine Food reviews(EDA)/17.1 - Dataset overview Amazon Fine Food reviews(EDA).mp4 | 111 MB |
| 50.14 - Residual Network/50.14 - Residual Network..mp4 | 108.53 MB |
| 24.16 - Code sample Logistic regression, GridSearchCV, RandomSearchCV/24.16 - Code sample Logistic regression, GridSearchCV, RandomSearchCV.mp4 | 106.88 MB |
| 51.2 - Recurrent Neural Network/51.2 - Recurrent Neural Network..mp4 | 105.18 MB |
| 53.10 - NVIDIA’s end to end CNN model/53.10 - NVIDIA’s end to end CNN model..mp4 | 103.55 MB |
| 47.8 - Training an MLP Chain Rule/47.8 - Training an MLP Chain Rule.mp4 | 102.08 MB |
| 48.3 - Rectified Linear Units (ReLU)/48.3 - Rectified Linear Units (ReLU)..mp4 | 102.05 MB |
| 11.9 - Q-Q plotHow to test if a random variable is normally distributed or not/11.9 - Q-Q plotHow to test if a random variable is normally distributed or not.mp4 | 101.62 MB |
| 48.18 - Auto Encoders/48.18 - Auto Encoders..mp4 | 97.55 MB |
| 4.2 - Types of functions/4.2 - Types of functions.mp4 | 96.07 MB |
| 18.27 - LSH for cosine similarity/18.27 - LSH for cosine similarity.mp4 | 96 MB |
| 18.30 - Code SampleDecision boundary/18.30 - Code SampleDecision boundary ..mp4 | 95.52 MB |
| 20.17 - curse of dimensionality/20.17 - curse of dimensionality.mp4 | 94.95 MB |
| 49.8 - Model 1 Sigmoid activation/49.8 - Model 1 Sigmoid activation.mp4 | 94.95 MB |
| 42.6 - Data cleaning and understandingMissing data in various features/42.6 - Data cleaning and understandingMissing data in various features.mp4 | 94.76 MB |
| 4.8 - File Handling/4.8 - File Handling.mp4 | 92.9 MB |
| 32.16 - Stacking models/32.16 - Stacking models.mp4 | 92.87 MB |
| 36.3 - Proximity methods Advantages and Limitations/36.3 - Proximity methods Advantages and Limitations..mp4 | 91.85 MB |
| 57.20 - Sub QueriesNested QueriesInner Queries/57.20 - Sub QueriesNested QueriesInner Queries.mp4 | 90.52 MB |
| 7.3 - Key Operations on Data Frames/7.3 - Key Operations on Data Frames.mp4 | 90.37 MB |
| 24.2 - Sigmoid function Squashing/24.2 - Sigmoid function Squashing.mp4 | 90.08 MB |
| 57.13 - Logical Operators/57.13 - Logical Operators.mp4 | 88.27 MB |
| 17.5 - Text Preprocessing Stemming/Stop-word removal, Tokenization, Lemmatization (Featurizations - convert text to numeric vectors).mp4 | 88.26 MB |
| 54.3 - Char-RNN with abc-notation Char-RNN model/54.3 - Char-RNN with abc-notation Char-RNN model.mp4 | 86.88 MB |
| 20.11 - Local outlier Factor(A)/20.11 - Local outlier Factor(A).mp4 | 86.75 MB |
| 49.12 - MNIST classification in Keras/49.12 - MNIST classification in Keras..mp4 | 86.71 MB |
| 48.16 - Softmax and Cross-entropy for multi-class classification/48.16 - Softmax and Cross-entropy for multi-class classification..mp4 | 85.89 MB |
| 14.9 - PCA Code example/14.9 - PCA Code example.mp4 | 85.47 MB |
| 48.9 - Batch SGD with momentum/48.9 - Batch SGD with momentum..mp4 | 85.03 MB |
| 20.18 - Bias-Variance tradeoff/20.18 - Bias-Variance tradeoff.mp4 | 84.12 MB |
| 38.1 - Problem formulation Movie reviews/38.1 - Problem formulation Movie reviews.mp4 | 84.02 MB |