Machine Learning Bookcamp
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8:59:20
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00001 Chapter 1 Introduction to machine learning.mp4
12:39
00002 Chapter 1 When machine learning isn t helpful.mp4
11:32
00003 Chapter 1 Evaluation.mp4
11:22
00004 Chapter 2 Machine learning for regression.mp4
06:43
00005 Chapter 2 Exploratory data analysis.mp4
08:25
00006 Chapter 2 Target variable analysis.mp4
09:35
00007 Chapter 2 Machine learning for regression - again.mp4
10:24
00008 Chapter 2 Linear regression.mp4
09:35
00009 Chapter 2 Predicting the price.mp4
10:18
00010 Chapter 2 Validating the model.mp4
12:20
00011 Chapter 2 Regularization.mp4
07:35
00012 Chapter 2 Using the model.mp4
06:07
00013 Chapter 3 Machine learning for classification.mp4
11:24
00014 Chapter 3 Initial data preparation.mp4
10:37
00015 Chapter 3 Feature importance Part 1.mp4
09:53
00016 Chapter 3 Feature importance Part 2.mp4
06:45
00017 Chapter 3 Feature engineering.mp4
08:02
00018 Chapter 3 Machine learning for classification.mp4
06:05
00019 Chapter 3 Training logistic regression.mp4
10:10
00020 Chapter 3 Model interpretation.mp4
12:37
00021 Chapter 3 Using the model.mp4
10:05
00022 Chapter 4 Evaluation metrics for classification.mp4
10:28
00023 Chapter 4 Confusion table.mp4
11:07
00024 Chapter 4 Precision and recall.mp4
06:07
00025 Chapter 4 ROC curve and AUC score.mp4
12:46
00026 Chapter 4 ROC Curve.mp4
11:07
00027 Chapter 4 Parameter tuning.mp4
06:43
00028 Chapter 4 Next steps.mp4
08:08
00029 Chapter 5 Deploying machine learning models.mp4
08:51
00030 Chapter 5 Model serving.mp4
10:55
00031 Chapter 5 Managing dependencies.mp4
08:22
00032 Chapter 5 Docker.mp4
07:10
00033 Chapter 5 Deployment.mp4
09:12
00034 Chapter 6 Decision trees and ensemble learning.mp4
05:32
00035 Chapter 6 Data cleaning.mp4
10:34
00036 Chapter 6 Decision trees.mp4
10:35
00037 Chapter 6 Decision tree learning algorithm.mp4
08:56
00038 Chapter 6 Random forest.mp4
08:28
00039 Chapter 6 Gradient boosting.mp4
06:55
00040 Chapter 6 Parameter tuning for XGBoost.mp4
11:59
00041 Chapter 6 Next steps.mp4
06:12
00042 Chapter 7 Neural networks and deep learning.mp4
10:53
00043 Chapter 7 Convolutional neural networks.mp4
06:31
00044 Chapter 7 Internals of the model.mp4
07:05
00045 Chapter 7 Training the model.mp4
07:31
00046 Chapter 7 Training the model - again.mp4
08:55
00047 Chapter 7 Saving the model and checkpointing.mp4
10:22
00048 Chapter 7 Data augmentation.mp4
08:48
00049 Chapter 7 Using the model.mp4
10:11
00050 Chapter 8 Serverless deep learning.mp4
12:42
00051 Chapter 8 Preparing the Docker image.mp4
12:57
00052 Chapter 9 Serving models with Kubernetes and Kubeflow.mp4
10:18
00053 Chapter 9 Running TensorFlow Serving locally.mp4
12:25
00054 Chapter 9 Model deployment with Kubernetes.mp4
12:12
00055 Chapter 9 Deploying to Kubernetes.mp4
10:30
00056 Chapter 9 Model deployment with Kubeflow.mp4
07:22
00057 Chapter 9 KFServing transformers.mp4
08:18
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Manning Publications
View courses Manning PublicationsManning Publications is an American publisher specializing in content relating to computers. Manning mainly publishes textbooks but also release videos and projects for professionals within the computing world.
- language english
- Training sessions 57
- duration 8:59:20
- Release Date 2023/11/06
