[Manning] Machine learning bookcamp (hevc) (2021) [EN]
File List
- Manning.Machine.learning.bookcamp.2021.pdf 9.9 MB
- 25 - Ch4 ROC curve and AUC score.m4v 6.8 MB
- 51 - Ch8 Preparing the Docker image.m4v 6.7 MB
- 10 - Ch2 Validating the model.m4v 6.7 MB
- 1 - Ch1 Introduction to machine learning.m4v 6.6 MB
- 50 - Ch8 Serverless deep learning.m4v 6.6 MB
- 53 - Ch9 Running TensorFlow Serving locally.m4v 6.5 MB
- 2 - Ch1 When machine learning isn’t helpful.m4v 6.5 MB
- 20 - Ch3 Model interpretation.m4v 6.5 MB
- 54 - Ch9 Model deployment with Kubernetes.m4v 6.4 MB
- 13 - Ch3 Machine learning for classification.m4v 6.3 MB
- 40 - Ch6 Parameter tuning for XGBoost.m4v 6.2 MB
- 21 - Ch3 Using the model.m4v 6.2 MB
- 3 - Ch1 Evaluation.m4v 6.0 MB
- 26 - Ch4 ROC Curve.m4v 5.9 MB
- 42 - Ch7 Neural networks and deep learning.m4v 5.8 MB
- 23 - Ch4 Confusion table.m4v 5.8 MB
- 49 - Ch7 Using the model.m4v 5.7 MB
- 28 - Ch4 Next steps.m4v 5.7 MB
- 22 - Ch4 Evaluation metrics for classification.m4v 5.6 MB
- 52 - Ch9 Serving models with Kubernetes and Kubeflow.m4v 5.5 MB
- 55 - Ch9 Deploying to Kubernetes.m4v 5.5 MB
- 15 - Ch3 Feature importance, Part 1.m4v 5.4 MB
- 35 - Ch6 Data cleaning.m4v 5.4 MB
- 30 - Ch5 Model serving.m4v 5.4 MB
- 47 - Ch7 Saving the model and checkpointing.m4v 5.4 MB
- 36 - Ch6 Decision trees.m4v 5.4 MB
- 7 - Ch2 Machine learning for regression - again.m4v 5.4 MB
- 14 - Ch3 Initial data preparation.m4v 5.3 MB
- 9 - Ch2 Predicting the price.m4v 5.2 MB
- 19 - Ch3 Training logistic regression.m4v 5.2 MB
- 33 - Ch5 Deployment.m4v 4.9 MB
- 6 - Ch2 Target variable analysis.m4v 4.9 MB
- 8 - Ch2 Linear regression.m4v 4.8 MB
- 57 - Ch9 KFServing transformers.m4v 4.7 MB
- 48 - Ch7 Data augmentation.m4v 4.6 MB
- 37 - Ch6 Decision tree learning algorithm.m4v 4.6 MB
- 46 - Ch7 Training the model - again.m4v 4.6 MB
- 29 - Ch 5 Deploying machine learning models.m4v 4.5 MB
- 5 - Ch2 Exploratory data analysis.m4v 4.5 MB
- 38 - Ch6 Random forest.m4v 4.3 MB
- 17 - Ch3 Feature engineering.m4v 4.3 MB
- 31 - Ch5 Managing dependencies.m4v 4.2 MB
- 11 - Ch2 Regularization.m4v 4.0 MB
- 16 - Ch3 Feature importance, Part 2.m4v 3.9 MB
- 41 - Ch6 Next steps.m4v 3.9 MB
- 4 - Ch2 Machine learning for regression.m4v 3.8 MB
- 56 - Ch9 Model deployment with Kubeflow.m4v 3.8 MB
- 45 - Ch7 Training the model.m4v 3.7 MB
- 12 - Ch2 Using the model.m4v 3.7 MB
- 32 - Ch5 Docker.m4v 3.6 MB
- 27 - Ch4 Parameter tuning.m4v 3.6 MB
- 39 - Ch6 Gradient boosting.m4v 3.5 MB
- 44 - Ch7 Internals of the model.m4v 3.5 MB
- 18 - Ch3 Machine learning for classification.m4v 3.2 MB
- 43 - Ch7 Convolutional neural networks.m4v 3.2 MB
- 24 - Ch4 Precision and recall.m4v 3.2 MB
- 34 - Ch6 Decision trees and ensemble learning.m4v 3.1 MB
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