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【影视】 [FreeCourseSite.com] Udemy - Machine Learning & Deep Learning in Python & R

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27. ANN in R/8. Saving - Restoring Models and Using Callbacks.mp4 226.5 MB
37. Time Series - Preprocessing in Python/3. Time Series - Visualization in Python.mp4 173.2 MB
18. Ensemble technique 3 - Boosting/7. XGBoosting in R.mp4 169.1 MB
26. ANN in Python/9. Building Neural Network for Regression Problem.mp4 163.5 MB
26. ANN in Python/11. Saving - Restoring Models and Using Callbacks.mp4 159.0 MB
23. Creating Support Vector Machine Model in R/4. Classification SVM model using Linear Kernel.mp4 145.9 MB
27. ANN in R/6. Building Regression Model with Functional API.mp4 137.5 MB
27. ANN in R/3. Building,Compiling and Training.mp4 137.1 MB
34. Transfer Learning Basics/6. Project - Transfer Learning - VGG16.mp4 135.4 MB
7. Linear Regression/20. Ridge regression and Lasso in Python.mp4 135.1 MB
25. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4 128.1 MB
38. Time Series - Important Concepts/5. Differencing in Python.mp4 118.5 MB
37. Time Series - Preprocessing in Python/5. Time Series - Feature Engineering in Python.mp4 118.2 MB
27. ANN in R/2. Data Normalization and Test-Train Split.mp4 117.2 MB
5. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4 114.5 MB
37. Time Series - Preprocessing in Python/1. Data Loading in Python.mp4 114.2 MB
23. Creating Support Vector Machine Model in R/8. SVM based Regression Model in R.mp4 111.3 MB
7. Linear Regression/21. Ridge regression and Lasso in R.mp4 108.5 MB
14. Simple Decision Trees/13. Building a Regression Tree in R.mp4 108.4 MB
35. Transfer Learning in R/1. Project - Transfer Learning - VGG16 (Implementation).mp4 106.5 MB

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【影视】 [UdemyCourseDownloader] Data Science, Deep Learning, & Machine Learning with Python

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08 Apache Spark_ Machine Learning on Big Data/061 [Activity] Decision Trees in Spark.mp4 92.5 MB
08 Apache Spark_ Machine Learning on Big Data/057 [Activity] Installing Spark - Part 2.mp4 90.0 MB
06 More Data Mining and Machine Learning Techniques/048 Reinforcement Learning.mp4 66.6 MB
05 Recommender Systems/041 [Activity] Making Movie Recommendations to People.mp4 66.3 MB
08 Apache Spark_ Machine Learning on Big Data/062 [Activity] K-Means Clustering in Spark.mp4 65.9 MB
07 Dealing with Real-World Data/052 [Activity] Cleaning web log data.mp4 63.1 MB
08 Apache Spark_ Machine Learning on Big Data/064 [Activity] Searching Wikipedia with Spark.mp4 62.7 MB
06 More Data Mining and Machine Learning Techniques/044 [Activity] Using KNN to predict a rating for a movie.mp4 62.1 MB
06 More Data Mining and Machine Learning Techniques/047 Data Warehousing Overview_ ETL and ELT.mp4 56.7 MB
10 Deep Learning and Neural Networks/084 The Ethics of Deep Learning.mp4 56.3 MB
06 More Data Mining and Machine Learning Techniques/046 [Activity] PCA Example with the Iris data set.mp4 55.9 MB
03 Predictive Models/022 [Activity] Multivariate Regression_ and Predicting Car Prices.mp4 52.4 MB
02 Statistics and Probability Refresher_ and Python Practise/016 [Activity] Covariance and Correlation.mp4 52.2 MB
02 Statistics and Probability Refresher_ and Python Practise/017 [Exercise] Conditional Probability.mp4 52.0 MB
02 Statistics and Probability Refresher_ and Python Practise/015 [Activity] A Crash Course in matplotlib.mp4 51.7 MB
10 Deep Learning and Neural Networks/073 [Activity] Deep Learning in the Tensorflow Playground.mp4 51.4 MB
05 Recommender Systems/039 [Activity] Finding Movie Similarities.mp4 50.8 MB
04 Machine Learning with Python/024 Supervised vs_ Unsupervised Learning_ and Train_Test.mp4 50.1 MB
07 Dealing with Real-World Data/050 [Activity] K-Fold Cross-Validation to avoid overfitting.mp4 48.1 MB
05 Recommender Systems/042 [Exercise] Improve the recommender's results.mp4 48.1 MB

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【影视】 [DesireCourse.Com] Udemy - Data Science Supervised Machine Learning in Python

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09 Appendix/035 How to install Numpy Scipy Matplotlib and Sci-Kit Learn.mp4 46.0 MB
04 Decision Trees/021 Decision Tree in Code.mp4 31.8 MB
09 Appendix/036 How to Code by Yourself part 1.mp4 25.7 MB
02 K-Nearest Neighbor/006 KNN in Code with MNIST.mp4 18.8 MB
06 Practical Machine Learning/030 Sci-Kit Learn.mp4 16.6 MB
03 Naive Bayes and Bayes Classifiers/010 Naive Bayes.mp4 16.5 MB
09 Appendix/037 How to Code by Yourself part 2.mp4 15.5 MB
03 Naive Bayes and Bayes Classifiers/012 Naive Bayes in Code with MNIST.mp4 15.1 MB
04 Decision Trees/019 Maximizing Information Gain.mp4 14.6 MB
05 Perceptrons/023 Perceptron in Code.mp4 14.4 MB
05 Perceptrons/022 Perceptron Concepts.mp4 12.8 MB
07 Building a Machine Learning Web Service/033 Building a Machine Learning Web Service Code.mp4 12.4 MB
06 Practical Machine Learning/031 Regression with Sci-Kit Learn is Easy.mp4 11.3 MB
03 Naive Bayes and Bayes Classifiers/015 Linear Discriminant Analysis LDA and Quadratic Discriminant Analysis QDA.mp4 10.9 MB
01 Introduction and Review/004 How to Succeed in this Course.mp4 9.2 MB
05 Perceptrons/024 Perceptron for MNIST and XOR.mp4 9.2 MB
06 Practical Machine Learning/028 Comparison to Deep Learning.mp4 9.1 MB
02 K-Nearest Neighbor/005 K-Nearest Neighbor Concepts.mp4 9.0 MB
04 Decision Trees/017 Decision Tree Basics.mp4 8.7 MB
02 K-Nearest Neighbor/007 When KNN Can Fail.mp4 8.1 MB

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【影视】 [UdemyCourseDownloader] Regression Analysis for Statistics & Machine Learning in R

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1. Get Started with Practical Regression Analysis in R/8. Basic Exploratory Data Analysis in R.mp4 50.6 MB
1. Get Started with Practical Regression Analysis in R/5. Reading in Data with R.mp4 44.7 MB
1. Get Started with Practical Regression Analysis in R/6. Data Cleaning with R.mp4 42.5 MB
6. Generalized Linear Models(GLMs)/2. Logistic regression.mp4 41.0 MB
7. Working with Non-Parametric and Non-Linear Data/3. Generalized Additive Models (GAMs) in R.mp4 40.4 MB
3. Deal with Multicollinearity in OLS Regression Models/1. Identify Multicollinearity.mp4 40.0 MB
2. Ordinary Least Square Regression Modelling/10. Multiple Linear regression with Interaction and Dummy Variables.mp4 39.4 MB
4. Variable & Model Selection/5. Evaluate Regression Model Performance.mp4 36.5 MB
4. Variable & Model Selection/2. Select the Most Suitable OLS Regression Model.mp4 34.9 MB
2. Ordinary Least Square Regression Modelling/11. Some Basic Conditions that OLS Models Have to Fulfill.mp4 32.0 MB
5. Dealing With Other Violations of the OLS Regression Models/1. Data Transformations.mp4 30.4 MB
7. Working with Non-Parametric and Non-Linear Data/9. Random Forest(RF).mp4 29.6 MB
6. Generalized Linear Models(GLMs)/3. Logistic Regression for Binary Response Variable.mp4 27.6 MB
3. Deal with Multicollinearity in OLS Regression Models/3. Principal Component Regression in R.mp4 26.7 MB
7. Working with Non-Parametric and Non-Linear Data/7. CART-Regression Trees in R.mp4 26.4 MB
2. Ordinary Least Square Regression Modelling/1. OLS Regression- Theory.mp4 26.1 MB
7. Working with Non-Parametric and Non-Linear Data/2. Polynomial and Non-linear regression.mp4 25.4 MB
1. Get Started with Practical Regression Analysis in R/7. Some More Data Cleaning with R.mp4 24.0 MB
4. Variable & Model Selection/7. Identify the Contribution of Predictors in Explaining the Variation in Y.mp4 23.0 MB
7. Working with Non-Parametric and Non-Linear Data/5. Multivariate Adaptive Regression Splines (MARS).mp4 22.7 MB

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【影视】 [ FreeCourseWeb.com ] Udemy - Enticing random Pearls of Machine Learning-The gist of 21-22

收录时间:2022-03-17 文档个数:43 文档大小:1.3 GB 最近下载:2025-05-15 人气:630 磁力链接

~Get Your Files Here !/1. Introduction/7. mean shift from scratch.mp4 167.7 MB
~Get Your Files Here !/1. Introduction/2. K means clustering.mp4 152.8 MB
~Get Your Files Here !/1. Introduction/9. Using clustering for semi supervised learning.mp4 148.7 MB
~Get Your Files Here !/1. Introduction/8. Using clustering with preprocessing.mp4 145.1 MB
~Get Your Files Here !/1. Introduction/6. More Highlight-Unsupervised-Clustering -Kmeans.mp4 94.4 MB
~Get Your Files Here !/1. Introduction/5. hierarchical clustering.mp4 92.5 MB
~Get Your Files Here !/1. Introduction/10. UCSSL Cont.mp4 85.8 MB
~Get Your Files Here !/1. Introduction/11. DBSCAN-1.mp4 82.8 MB
~Get Your Files Here !/2. Introducing a bit off Matplotlib/1. Gridspec and other functions#221.mp4 79.7 MB
~Get Your Files Here !/2. Introducing a bit off Matplotlib/2. Gridspec using Subplotspec#222.mp4 70.0 MB
~Get Your Files Here !/1. Introduction/12. DBSCAN-2.mp4 65.1 MB
~Get Your Files Here !/1. Introduction/4. K Means Clustering finishing.mp4 51.6 MB
~Get Your Files Here !/1. Introduction/3. K Means clustering -cont.mp4 51.5 MB
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~Get Your Files Here !/1. Introduction/2. K means clustering.srt 25.1 kB
~Get Your Files Here !/1. Introduction/9. Using clustering for semi supervised learning.srt 21.6 kB
~Get Your Files Here !/1. Introduction/7. mean shift from scratch.srt 16.9 kB
~Get Your Files Here !/1. Introduction/8. Using clustering with preprocessing.srt 16.7 kB
~Get Your Files Here !/2. Introducing a bit off Matplotlib/1. Gridspec and other functions#221.srt 15.9 kB
~Get Your Files Here !/1. Introduction/5. hierarchical clustering.srt 14.4 kB