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【影视】 Udemy Машинное обучение в Python Machine Learning Data Science

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Раздел 7. Seaborn/61. Scatterplots - Графики рассеяния (диаграммы рассеяния) .mp4 338.3 MB
Раздел 5. Pandas/48. Решения для проверочных упражнений по Pandas .mp4 337.2 MB
Раздел 23. Иерархическая кластеризация данных/211. Иерархическая кластеризация - Пишем код, часть 2 - Scikit-Learn.mp4 336.7 MB
Раздел 1. Вводная часть курса/4. Установка Anaconda, Python, Jupyter Notebook .mp4 333.2 MB
Раздел 13. Логистическая регрессия/137. Решения для проверочного проекта по логистической регрессии.mp4 324.8 MB
Раздел 5. Pandas/43. Input_Output в Pandas - HTML-таблицы .mp4 311.5 MB
Раздел 16. Деревья решений (Decision Trees)/161. Код в Python для деревьев решений - Часть 2 - Модель.mp4 293.3 MB
Раздел 11. Конструирование признаков (Feature Engineering)/112. Работа с отсутствующими данными (missing data) - Часть 2 - Работа по строкам.mp4 261.3 MB
Раздел 8. Большой Проект по Визуализации Данных/77. Разбор решений проекта - Часть 3.mp4 259.7 MB
Раздел 13. Логистическая регрессия/135. Мульти-классовая классификация - Логистическая регрессия - Модель.mp4 249.0 MB
Раздел 6. Matplotlib/59. Решения для проверочных упражнений по Matplotlib .mp4 242.6 MB
Раздел 10. Линейная регрессия/93. Внедрение модели и интерпретация коэффициентов.mp4 237.3 MB
Раздел 7. Seaborn/73. Решения для проверочных упражнений по Seaborn .mp4 232.7 MB
Раздел 19. Проверочный проект по моделям обучения с учителем (Supervised Learning)/181. Разбор решений - Часть 1 - Исследование данных (Exploratory Data Analysis).mp4 232.6 MB
Раздел 8. Большой Проект по Визуализации Данных/76. Разбор решений проекта - Часть 2.mp4 230.0 MB
Раздел 17. Случайные леса (Random Forests)/168. Классификация данных с помощью RandomForestClassifier - Часть 2.mp4 228.3 MB
Раздел 8. Большой Проект по Визуализации Данных/74. Обзор Проекта по Визуализации Данных.mp4 228.1 MB
Раздел 11. Конструирование признаков (Feature Engineering)/110. Работа с выбросами (outliers).mp4 226.9 MB
Раздел 19. Проверочный проект по моделям обучения с учителем (Supervised Learning)/182. Разбор решений - Часть 2 - Анализ оттока (churn analysis).mp4 224.6 MB
Раздел 7. Seaborn/67. Categorical Plots - Распределения по категориям - Код в Python .mp4 219.5 MB

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7. Confusion matrix e normalização/2. Medição de desempenho ROCAUC (conceito + matemática o que o algoritmo faz debaixo dos panos)/1. ROC_AUC teoria.mp4 1.4 GB
10. Decision Trees/1. Decision Trees (conceito + matemática o que o algoritmo faz debaixo dos panos) – Parte 1 entropia/1. DTentropia.mp4 1.2 GB
6. Regressão Logística/1. Regressão logística (conceito + matemática o que o algoritmo faz debaixo dos panos)/1. RegLogisticaTeoria.mp4 1.2 GB
5. Validação cruzada e ajuste fino dos parâmetros/3. Validação cruzada Kfold (Teoria o que o algoritmo faz debaixo dos panos)/1. kfold teoria.mp4 818.2 MB
4. Outros modelos de regressão linear/1. Ridge regression (conceito + matemática o que o algoritmo faz debaixo dos panos)/1. Ridge teoria.mp4 813.3 MB
10. Decision Trees/4. Decision Trees (conceito + matemática o que o algoritmo faz debaixo dos panos) – Parte 3 regressão/1. DTregressao.mp4 734.4 MB
10. Decision Trees/2. Decision Trees (conceito + matemática o que o algoritmo faz debaixo dos panos) – Parte 2 índice GINI/1. DTgini.mp4 689.0 MB
8. KNN/1. KNN (conceito + matemática o que o algoritmo faz debaixo dos panos)/1. KNN teoria.mp4 471.1 MB
1. Conceitos Básicos/2. Viés e Variância/1. Viés e Variância.mp4 464.8 MB
1. Conceitos Básicos/4. Viés e Variância/1. Viés e Variância.mp4 464.8 MB
1. Conceitos Básicos/3. Visão geral dos algoritmos de machine learning/1. Visão geral algoritmos ML.mp4 388.5 MB
2. Regressão Linear/1. Regressão linear (conceito + matemática o que o algoritmo faz debaixo dos panos)/1. A matemática do Gradiente Descendente & Regressão Linear (machine learning).mp4 382.1 MB
4. Outros modelos de regressão linear/6. Elastic net (conceito + matemática o que o algoritmo faz debaixo dos panos)/1. Elastic Net teoria.mp4 323.2 MB
10. Decision Trees/3. Decision Trees evitando overfitting/1. DToverfitting.mp4 306.2 MB
4. Outros modelos de regressão linear/4. Regularização L1, L2 e regressão Lasso/1. Regularização L1 L2.mp4 265.7 MB
3. Pré-processamento/3. Feature selection correlação (conceito)/1. NatiCorrelacao.mp4 237.7 MB
3. Pré-processamento/1. Pré-processamento dados missing/1. 2_01_DadosMissing.mp4.mp4 158.5 MB
9. Naive Bayes/1. Teorema de Bayes (conceito teórico)/1. Entenda o Teorema de Bayes (ótima explicação!).mp4 125.6 MB
5. Validação cruzada e ajuste fino dos parâmetros/6. Teste de parâmetros específicos em um modelo (aplicação prática em R)/1. 4_4_AjusteFino.mp4 125.5 MB
1. Conceitos Básicos/1. Introdução a Machine Learning e IA/4. O que é Overfitting e Underfitting (Introdução a Machine Learning - Aula 4).mp4 88.6 MB

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[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/20. Competition Section on Kaggle/2. Competitions on Kaggle Lesson 2.mp4 201.0 MB
[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/20. Competition Section on Kaggle/1. Competitions on Kaggle Lesson 1.mp4 197.3 MB
[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/22. Code Section on Kaggle/3. Examining the Code Section in Kaggle Lesson 3.mp4 167.6 MB
[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/21. Dataset Section on Kaggle/1. Datasets on Kaggle.mp4 139.7 MB
[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/19. First Contact with Kaggle/1. What is Kaggle.mp4 135.9 MB
[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/26. Introduction to Machine Learning with Real Hearth Attack Prediction Project/6. Recognizing Variables In Dataset.mp4 133.0 MB
[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/19. First Contact with Kaggle/5. Getting to Know the Kaggle Homepage.mp4 128.9 MB
[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/2. Installations for Python/1. Installing Anaconda Distribution for Windows.mp4 124.1 MB
[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/26. Introduction to Machine Learning with Real Hearth Attack Prediction Project/1. First Step to the Project.mp4 122.8 MB
[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/2. Installations for Python/3. Installing Anaconda Distribution for Linux.mp4 120.4 MB
[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/24. Other Most Used Options on Kaggle/2. Ranking Among Users on Kaggle.mp4 112.2 MB
[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/5. Linear Regression Algorithm in Machine Learning A-Z/3. Linear Regression Algorithm With Python Part 2.mp4 112.1 MB
[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/22. Code Section on Kaggle/2. Examining the Code Section in Kaggle Lesson 2.mp4 110.9 MB
[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/26. Introduction to Machine Learning with Real Hearth Attack Prediction Project/3. Notebook Design to be Used in the Project.mp4 110.1 MB
[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/3. Evaluation Metrics in Machine Learning/2. Machine Learning Model Performance Evaluation Classification Error Metrics.mp4 105.2 MB
[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/3. Evaluation Metrics in Machine Learning/4. Machine Learning With Python.mp4 96.7 MB
[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/30. Exploratory Data Analysis (EDA) - Bi-variate Analysis/8. Examining Statistics of Variables.mp4 95.8 MB
[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/30. Exploratory Data Analysis (EDA) - Bi-variate Analysis/16. Relationships between variables (Analysis with Heatmap) Lesson 2.mp4 95.1 MB
[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/5. Linear Regression Algorithm in Machine Learning A-Z/5. Linear Regression Algorithm With Python Part 4.mp4 94.4 MB
[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z/29. Exploratory Data Analysis (EDA) - Uni-variate Analysis/4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp4 88.2 MB

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【影视】 Learning Deep Learning From Perceptron to Large Language Models

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Lesson 7 Encoder–Decoder Networks, Attention, Transformers, and Neural Machine Translation/003. 7.2 Programming Example Neural Machine Translation with TensorFlow.mp4 114.1 MB
Lesson 7 Encoder–Decoder Networks, Attention, Transformers, and Neural Machine Translation/004. 7.3 Programming Example Neural Machine Translation with PyTorch.mp4 104.7 MB
Lesson 9 Multi-Modal Networks and Image Captioning/007. 9.6 Programming Example Image Captioning with PyTorch.mp4 86.3 MB
Lesson 9 Multi-Modal Networks and Image Captioning/006. 9.5 Programming Example Image Captioning with TensorFlow.mp4 85.1 MB
Lesson 3 Neural Network Fundamentals II/004. 3.3 Programming Example Digit Classification with Python.mp4 77.2 MB
Lesson 5 Recurrent Neural Networks (RNN) and Time Series Prediction/010. 5.9 Programming Example Text Autocompletion with PyTorch.mp4 67.9 MB
Lesson 9 Multi-Modal Networks and Image Captioning/008. 9.7 Multimodal Large Language Models.mp4 63.1 MB
Lesson 5 Recurrent Neural Networks (RNN) and Time Series Prediction/009. 5.8 Programming Example Text Autocompletion with TensorFlow.mp4 63.0 MB
Lesson 2 Neural Network Fundamentals I/010. 2.9 Programming Example Learning the XOR Function.mp4 62.5 MB
Lesson 6 Neural Language Models and Word Embeddings/004. 6.3 Programming Example Language Model and Word Embeddings with TensorFlow.mp4 57.0 MB
Lesson 11 Applying Deep Learning/002. 11.1 Ethical AI and Data Ethics.mp4 55.5 MB
Lesson 8 Large Language Models/004. 8.3 From GPT to GPT4.mp4 54.7 MB
Lesson 3 Neural Network Fundamentals II/007. 3.6 Programming Example Digit Classification with PyTorch.mp4 52.1 MB
Lesson 6 Neural Language Models and Word Embeddings/005. 6.4 Programming Example Language Model and Word Embeddings with PyTorch.mp4 48.3 MB
Lesson 5 Recurrent Neural Networks (RNN) and Time Series Prediction/005. 5.4 Programming Example Forecasting Book Sales with PyTorch.mp4 47.7 MB
Lesson 3 Neural Network Fundamentals II/016. 3.15 Programming Example Regression Problem with PyTorch.mp4 47.3 MB
Lesson 4 Convolutional Neural Networks (CNN) and Image Classification/004. 4.3 Building a Convolutional Neural Network.mp4 45.8 MB
Lesson 2 Neural Network Fundamentals I/008. 2.7 Computing Gradient with the Chain Rule.mp4 43.8 MB
Lesson 7 Encoder–Decoder Networks, Attention, Transformers, and Neural Machine Translation/008. 7.7 Programming Example Machine Translation Using Transformer with Py.mp4 43.0 MB
Lesson 5 Recurrent Neural Networks (RNN) and Time Series Prediction/004. 5.3 Programming Example Forecasting Book Sales with TensorFlow.mp4 42.6 MB

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【影视】 [FreeCourseSite.com] Udemy - Machine Learning Natural Language Processing in Python (V2)

收录时间:2024-02-27 文档个数:320 文档大小:7.2 GB 最近下载:2025-05-14 人气:2473 磁力链接

18. Recurrent Neural Networks/9. Parts-of-Speech (POS) Tagging in Tensorflow.mp4 152.1 MB
3. Vector Models and Text Preprocessing/14. TF-IDF (Code).mp4 131.0 MB
16. Feedforward Artificial Neural Networks/13. CBOW in Tensorflow (Advanced).mp4 123.3 MB
21. Effective Learning Strategies for Machine Learning FAQ/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 113.4 MB
9. Spam Detection/6. Spam Detection in Python.mp4 112.8 MB
7. Cipher Decryption (Advanced)/4. Genetic Algorithms.mp4 110.3 MB
3. Vector Models and Text Preprocessing/10. Count Vectorizer (Code).mp4 106.9 MB
6. Article Spinner (Intermediate)/4. Article Spinner in Python (pt 1).mp4 100.6 MB
16. Feedforward Artificial Neural Networks/4. Activation Functions.mp4 93.7 MB
17. Convolutional Neural Networks/6. CNN Architecture.mp4 93.6 MB
11. Text Summarization/8. TextRank in Python (Advanced).mp4 86.3 MB
18. Recurrent Neural Networks/6. GRU and LSTM (pt 1).mp4 86.3 MB
13. Latent Semantic Analysis (Latent Semantic Indexing)/2. SVD (Singular Value Decomposition) Intuition.mp4 85.8 MB
15. The Neuron/4. Text Classification in Tensorflow.mp4 85.6 MB
17. Convolutional Neural Networks/2. What is Convolution.mp4 83.7 MB
3. Vector Models and Text Preprocessing/16. How to Build TF-IDF From Scratch.mp4 83.7 MB
21. Effective Learning Strategies for Machine Learning FAQ/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 83.5 MB
11. Text Summarization/4. Text Summarization in Python.mp4 82.0 MB
6. Article Spinner (Intermediate)/5. Article Spinner in Python (pt 2).mp4 79.0 MB
17. Convolutional Neural Networks/5. Convolution on Color Images.mp4 78.9 MB

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