Real-World Machine Learning
Focused View
7:01:37
66 View
00001 Chapter 1. What is machine learning.mp4
09:36
00002 Chapter 1. Using data to make decisions.mp4
07:55
00003 Chapter 1. The machine-learning approach.mp4
10:04
00004 Chapter 1. Five advantages to machine learning.mp4
04:28
00005 Chapter 1. Following the ML workflow - from data to deployment.mp4
09:36
00006 Chapter 1. Boosting model performance with advanced techniques.mp4
08:15
00007 Chapter 2. Real-world data.mp4
07:16
00008 Chapter 2. Which features should be included.mp4
08:19
00009 Chapter 2. How much training data is required.mp4
06:43
00010 Chapter 2. Preprocessing the data for modeling.mp4
09:58
00011 Chapter 2. Simple feature engineering.mp4
04:37
00012 Chapter 2. Using data visualization.mp4
08:08
00013 Chapter 2. Density plots.mp4
04:57
00014 Chapter 3. Modeling and prediction.mp4
09:13
00015 Chapter 3. Finding the relationship between input and target.mp4
08:05
00016 Chapter 3. Classification - predicting into buckets.mp4
08:00
00017 Chapter 3. Classifying complex nonlinear data.mp4
08:17
00018 Chapter 3. Regression - predicting numerical values.mp4
08:19
00019 Chapter 3. Summary.mp4
03:52
00020 Chapter 4. Model evaluation and optimization.mp4
08:16
00021 Chapter 4. The solution - cross-validation.mp4
05:55
00022 Chapter 4. Evaluation of classification models.mp4
04:41
00023 Chapter 4. Accuracy trade-offs and ROC curves.mp4
08:09
00024 Chapter 4. Evaluation of regression models.mp4
06:24
00025 Chapter 4. Model optimization through parameter tuning.mp4
08:53
00026 Chapter 4. Summary.mp4
04:36
00027 Chapter 5. Basic feature engineering.mp4
09:49
00028 Chapter 5. Basic feature-engineering processes.mp4
08:59
00029 Chapter 5. Feature selection.mp4
04:11
00030 Chapter 5. Forward selection and backward elimination.mp4
07:04
00031 Chapter 5. Summary.mp4
02:34
00032 Chapter 6. Example - NYC taxi data.mp4
05:47
00033 Chapter 6. Defining the problem and preparing the data.mp4
05:13
00034 Chapter 6. Modeling.mp4
08:55
00035 Chapter 6. Summary.mp4
02:09
00036 Chapter 7. Advanced feature engineering.mp4
07:29
00037 Chapter 7. Topic modeling.mp4
08:18
00038 Chapter 7. Content expansion.mp4
04:20
00039 Chapter 7. Image features.mp4
04:49
00040 Chapter 7. Extracting objects and shapes.mp4
07:06
00041 Chapter 7. Time-series features.mp4
06:58
00042 Chapter 7. Classical time-series features.mp4
08:45
00043 Chapter 7. Summary.mp4
06:17
00044 Chapter 8. Advanced NLP example - movie review sentiment.mp4
08:12
00045 Chapter 8. So what s the use case.mp4
08:02
00046 Chapter 8. Extracting basic NLP features and building the initial model.mp4
08:31
00047 Chapter 8. Normalizing bag-of-words features with the tf-idf algorithm.mp4
08:34
00048 Chapter 8. Advanced algorithms and model deployment considerations.mp4
10:37
00049 Chapter 9. Scaling machine-learning workflows.mp4
07:35
00050 Chapter 9. Subsampling training data in lieu of scaling.mp4
09:22
00051 Chapter 9. Scaling ML modeling pipelines.mp4
10:06
00052 Chapter 9. Scaling predictions.mp4
06:02
00053 Chapter 9. Summary.mp4
05:36
00054 Chapter 10. Example - digital display advertising.mp4
06:04
00055 Chapter 10. Feature engineering and modeling strategy.mp4
06:24
00056 Chapter 10. Singular value decomposition.mp4
09:07
00057 Chapter 10. Modeling.mp4
07:27
00058 Chapter 10. Summary.mp4
08:43
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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 58
- duration 7:01:37
- Release Date 2023/11/06
