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【压缩文件】 [ FreeCourseWeb.com ] Coursera - Machine Learning with Python.zip

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[ FreeCourseWeb.com ] Coursera - Machine Learning with Python.zip 332.2 MB

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【压缩文件】 python-machine-learning-and-deep-learning-with-python-scikit-learn-and-tensorflow-2

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python-machine-learning-and-deep-learning-with-python-scikit-learn-and-tensorflow-2_jp2.zip 261.7 MB
python-machine-learning-and-deep-learning-with-python-scikit-learn-and-tensorflow-2_hocr.html 31.1 MB
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python-machine-learning-and-deep-learning-with-python-scikit-learn-and-tensorflow-2_djvu.xml 16.6 MB
python-machine-learning-and-deep-learning-with-python-scikit-learn-and-tensorflow-2.pdf 9.9 MB
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python-machine-learning-and-deep-learning-with-python-scikit-learn-and-tensorflow-2_djvu.txt 1.3 MB
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rune.nfo 5.9 kB
Free Premium Accounts at OfficialMrB.com.txt 768 Bytes
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【压缩文件】 1993.Space.Machine.Build.7278623.rar

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【压缩文件】 Python, Excel and Machine Learning for Stocks Data Science

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【压缩文件】 [ FreeCourseWeb.com ] Udemy - Machine Learning for Absolute Beginners - Level 3.zip

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【压缩文件】 Udemy.Probabilidad.Machine.Learning.Big.Data.con.R y.Python

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【压缩文件】 [ FreeCourseWeb.com ] Udemy - Machine Learning - Linear Regression [Step by Step Guide].rar

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【压缩文件】 Old Machine Shop Books.zip

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【压缩文件】 Indiana Jones and the Infernal Machine.iso

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【压缩文件】 Packt - Python Machine Learning Tips Tricks and Techniques

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Exercise Files/code_33873.zip 283.4 MB
1.Improving Your Models Using Feature Engineering/02.Using Feature Scaling to Standardize Data.mp4 28.0 MB
1.Improving Your Models Using Feature Engineering/03.Implementing Feature Engineering with Logistic Regression.mp4 10.1 MB
1.Improving Your Models Using Feature Engineering/04.Extracting Data with Feature Selection and Interaction.mp4 17.3 MB
1.Improving Your Models Using Feature Engineering/05.Combining All Together.mp4 11.7 MB
1.Improving Your Models Using Feature Engineering/06.Build Model Based on Real-World Problems.mp4 13.2 MB
2.Feature Improvement with Non Linear Classification Techniques/07.Support Vector Machines.mp4 20.7 MB
2.Feature Improvement with Non Linear Classification Techniques/08.Implementing kNN on the Data Set.mp4 30.9 MB
2.Feature Improvement with Non Linear Classification Techniques/09.Decision Tree as Predictive Model.mp4 24.5 MB
2.Feature Improvement with Non Linear Classification Techniques/10.Tricks with Dimensionality Reduction.mp4 18.9 MB
2.Feature Improvement with Non Linear Classification Techniques/11.Combining All Together.mp4 19.6 MB
3.Power of Ensemble Learning with Python/12.Random Forest for Classification.mp4 21.5 MB
3.Power of Ensemble Learning with Python/13.Gradient Boosting Trees and Bayes Optimization.mp4 30.1 MB
3.Power of Ensemble Learning with Python/14.CatBoost to Handle Categorical Data.mp4 17.9 MB
3.Power of Ensemble Learning with Python/15.Implement Blending.mp4 24.4 MB
3.Power of Ensemble Learning with Python/16.Implement Stacking.mp4 29.7 MB
4.Recommender Systems/17.Memory-Based Collaborative Filtering.mp4 19.7 MB
4.Recommender Systems/18.Item-to-Item Recommendation with kNN.mp4 19.3 MB
4.Recommender Systems/19.Applying Matrix Factorization on Datasets.mp4 24.8 MB
4.Recommender Systems/20.Wordbatch for Real-World Problem.mp4 19.2 MB