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【影视】 [ TutGator.com ] Linkedin - Learning Windows Subsystem for Linux (2022)

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~Get Your Files Here !/03 - 2. Working in the Linux Environment/01 - Using the shell.mp4 25.5 MB
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~Get Your Files Here !/03 - 2. Working in the Linux Environment/02 - Managing software.mp4 19.2 MB
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【影视】 GetFreeCourses.Co-Udemy-Machine Learning & Data Science A-Z Hands-on Python 2021

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6. Supervised Learning - Regression/8. Random Forest Model Development.mp4 258.2 MB
5. Supervised Learning - Classification/1. Supervised Learning Models - Introduction and Understanding the Data.mp4 245.1 MB
5. Supervised Learning - Classification/4. k-NN Training-Set and Test-Set Creation.mp4 239.5 MB
3. Data Preprocessing/6. Missing Values2.mp4 230.0 MB
6. Supervised Learning - Regression/1. Simple and Multiple Linear Regression Concepts.mp4 222.5 MB
3. Data Preprocessing/3. Statistics2.mp4 217.6 MB
6. Supervised Learning - Regression/6. Polynomial Linear Regression Model Development.mp4 217.0 MB
2. Machine Learning Useful Packages (Libraries)/13. Visualization with Matplotlib2.mp4 215.2 MB
2. Machine Learning Useful Packages (Libraries)/11. Pandas4.mp4 212.9 MB
2. Machine Learning Useful Packages (Libraries)/14. Visualization with Matplotlib3.mp4 198.0 MB
3. Data Preprocessing/12. Normalization.mp4 195.9 MB
5. Supervised Learning - Classification/13. Model Evaluation - Calculating with Python.mp4 182.5 MB
6. Supervised Learning - Regression/4. Evaluation Metrics - Implementation.mp4 167.7 MB
3. Data Preprocessing/1. Reading and Modifying a Dataset.mp4 162.1 MB
2. Machine Learning Useful Packages (Libraries)/6. NumPy5.mp4 160.1 MB
7. Unsupervised Learning - Clustering Techniques/10. Hierarchical Clustering Model Development.mp4 153.0 MB
2. Machine Learning Useful Packages (Libraries)/15. Visualization with Matplotlib4.mp4 149.9 MB
5. Supervised Learning - Classification/3. k-NN Model Development.mp4 147.5 MB
2. Machine Learning Useful Packages (Libraries)/7. NumPy6.mp4 141.0 MB
8. Hyper Parameter Optimization (Model Tuning)/4. k-NN - Model Tuning.mp4 140.1 MB

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【影视】 Complete Machine Learning with R Studio - ML for 2021

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18 Ensemble technique 3 - GBM, AdaBoost and XGBoost/004 XGBoosting in R.mp4 195.5 MB
22 Creating Support Vector Machine Model in R/005 Classification SVM model using Linear Kernel.mp4 175.0 MB
07 Regression models other than OLS/005 Ridge regression and Lasso in R.mp4 130.0 MB
22 Creating Support Vector Machine Model in R/010 SVM based Regression Model in R.mp4 130.0 MB
04 Intorduction to Machine Learning/001 Introduction to Machine Learning.mp4 129.3 MB
14 Simple Decision Trees/008 Building a Regression Tree in R.mp4 127.8 MB
02 Setting up R Studio and R crash course/008 Creating Barplots in R.mp4 122.9 MB
05 Data Preprocessing for Regression Analysis/012 Bi-variate Analysis and Variable Transformation.mp4 118.6 MB
05 Data Preprocessing for Regression Analysis/006 EDD in R.mp4 117.4 MB
06 Linear Regression Model/003 Assessing Accuracy of predicted coefficients.mp4 108.9 MB
18 Ensemble technique 3 - GBM, AdaBoost and XGBoost/003 AdaBoosting in R.mp4 108.0 MB
15 Simple Classification Tree/003 Building a classification Tree in R.mp4 104.9 MB
22 Creating Support Vector Machine Model in R/007 Polynomial Kernel with Hyperparameter Tuning.mp4 103.4 MB
02 Setting up R Studio and R crash course/004 Packages in R.mp4 103.3 MB
14 Simple Decision Trees/010 Pruning a Tree in R.mp4 101.7 MB
05 Data Preprocessing for Regression Analysis/018 Correlation Matrix in R.mp4 99.5 MB
06 Linear Regression Model/013 Test-Train Split in R.mp4 95.3 MB
12 K-Nearest Neighbors/002 Test-Train Split in R.mp4 94.5 MB
11 Linear Discriminant Analysis/002 Linear Discriminant Analysis in R.mp4 93.8 MB
08 Classification Models_ Data Preparation/001 The Data and the Data Dictionary.mp4 91.7 MB

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【影视】 [ TutSala.com ] Udemy - Machine Learning for Product Managers - A Practical Guide

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~Get Your Files Here !/6. Build Your Model/3. Machine Learning as a Service (MLaaS).mp4 80.5 MB
~Get Your Files Here !/2. When to ML/7. Exercise #5 To ML or Not to ML.mp4 78.7 MB
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2. Machine Learning Tour/11. Machine Learning - hands on.mp4 50.6 MB
3. Projeto de ML preparando-se para jornada/2. Dataframes no pandas.mp4 49.7 MB
3. Projeto de ML preparando-se para jornada/4. Etapas projeto Machine Learning.mp4 46.7 MB
6. Probabilidades e filtro de spam [Intermediário]/1. Axiomas de probabilidade.mp4 44.5 MB
4. Projeto offline de ML/9. Viés v.s. Variância.mp4 43.6 MB
4. Projeto offline de ML/3. Separando dados.mp4 40.9 MB
3. Projeto de ML preparando-se para jornada/1. Introdução ao pandas - pandas series.mp4 40.6 MB
1. Introdução ao Machine Learning/6. Tipos de aprendizado.mp4 38.3 MB
4. Projeto offline de ML/1. Iniciando projeto.mp4 36.5 MB
4. Projeto offline de ML/7. Codificando dados.mp4 36.1 MB
2. Machine Learning Tour/7. Regressão Logística - Introdução.mp4 34.8 MB
1. Introdução ao Machine Learning/5. Perspectiva do aprendizado de máquina.mp4 33.9 MB
4. Projeto offline de ML/10. Validação de modelos.mp4 33.7 MB
7. Formulação probabilística da regressão linear [Intermediário]/3. Regressão Linear formulação probabilística, parte III.mp4 31.8 MB
5. Classificação/1. Matriz de confusão, acurácia, precisão e revocação.mp4 31.7 MB
2. Machine Learning Tour/6. Random Forest - Introdução.mp4 31.6 MB
4. Projeto offline de ML/6. Preparando dados.mp4 31.5 MB
2. Machine Learning Tour/4. Decision Tree - Prós e contras.mp4 30.4 MB
6. Probabilidades e filtro de spam [Intermediário]/6. Como usar probabilidades para construir um filtro de spam.mp4 28.7 MB
7. Formulação probabilística da regressão linear [Intermediário]/2. Regressão Linear formulação probabilística, parte II.mp4 27.9 MB

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