▲
0
0

report

Udemy-Machine Learning & Data Science with Python, Kaggle & Pandas.2023.VO.WEBrip.720p.x264

seeders: 11
leechers: 0
Download torrent
Added 2 years ago by Neozoon in Other
Download Fast Safe Anonymous
movies, software, shows...

Files

Udemy-Machine Learning & Data Science with Python, Kaggle & Pandas.2023.VO.WEBrip.720p.x264 (Size: 9.01 GB)
  1. Installations
  1. Installing Anaconda Distribution for Windows.mp4 118.3 MB
  2. Notebook Project Files Link regarding NumPy Python Programming Language Library.html 155 B
  3. Installing Anaconda Distribution for MacOs.mp4 46.34 MB
  4. 6 Article Advice And Links about Numpy, Numpy Pyhon.html 4.19 KB
  5. Installing Anaconda Distribution for Linux.mp4 114.79 MB
  10. Element Selection Operations in DataFrame Structures
  1. Element Selection Operations in Pandas DataFrames Lesson 1.mp4 29.88 MB
  2. Element Selection Operations in Pandas DataFrames Lesson 2.mp4 31.83 MB
  3. Top Level Element Selection in Pandas DataFramesLesson 1.mp4 38.31 MB
  4. Top Level Element Selection in Pandas DataFramesLesson 2.mp4 31.42 MB
  5. Top Level Element Selection in Pandas DataFramesLesson 3.mp4 22.08 MB
  6. Element Selection with Conditional Operations in.mp4 46.37 MB
  7. Quiz.html 205 B
  11. Structural Operations on Pandas DataFrame
  1. Adding Columns to Pandas Data Frames.mp4 33.58 MB
  2. Removing Rows and Columns from Pandas Data frames.mp4 15.57 MB
  3. Null Values in Pandas Dataframes.mp4 66.95 MB
  4. Dropping Null Values Dropna() Function.mp4 34.53 MB
  5. Filling Null Values Fillna() Function.mp4 51.61 MB
  6. Setting Index in Pandas DataFrames.mp4 39.7 MB
  7. Quiz.html 205 B
  12. Multi-Indexed DataFrame Structures
  1. Multi-Index and Index Hierarchy in Pandas DataFrames.mp4 42.66 MB
  2. Element Selection in Multi-Indexed DataFrames.mp4 24.59 MB
  3. Selecting Elements Using the xs() Function in Multi-Indexed DataFrames.mp4 31.28 MB
  4. Quiz.html 205 B
  13. Structural Concatenation Operations in Pandas DataFrame
  1. Concatenating Pandas Dataframes Concat Function.mp4 63.88 MB
  2. Merge Pandas Dataframes Merge() Function Lesson 1.mp4 57.3 MB
  3. Merge Pandas Dataframes Merge() Function Lesson 2.mp4 30.52 MB
  4. Merge Pandas Dataframes Merge() Function Lesson 3.mp4 60.14 MB
  5. Merge Pandas Dataframes Merge() Function Lesson 4.mp4 40.7 MB
  6. Joining Pandas Dataframes Join() Function.mp4 56.05 MB
  7. Quiz.html 205 B
  14. Functions That Can Be Applied on a DataFrame
  1. Loading a Dataset from the Seaborn Library.mp4 37.72 MB
  10. Quiz.html 205 B
  2. Examining the Data Set 1.mp4 42.88 MB
  3. Aggregation Functions in Pandas DataFrames.mp4 90.71 MB
  4. Examining the Data Set 2.mp4 46.57 MB
  5. Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes.mp4 88.12 MB
  6. Advanced Aggregation Functions Aggregate() Function.mp4 29.23 MB
  7. Advanced Aggregation Functions Filter() Function.mp4 24.47 MB
  8. Advanced Aggregation Functions Transform() Function.mp4 47.1 MB
  9. Advanced Aggregation Functions Apply() Function.mp4 41.43 MB
  15. Pivot Tables in Pandas Library
  1. Examining the Data Set 3.mp4 39.12 MB
  2. Pivot Tables in Pandas Library.mp4 54.23 MB
  3. Quiz.html 205 B
  16. File Operations in Pandas Library
  1. Accessing and Making Files Available.mp4 34.62 MB
  2. Data Entry with Csv and Txt Files.mp4 64.36 MB
  3. Data Entry with Excel Files.mp4 21.83 MB
  4. Outputting as an CSV Extension.mp4 35.71 MB
  5. Outputting as an Excel File.mp4 19.76 MB
  6. Quiz.html 205 B
  17. First Contact with Machine Learning
  1. What is Machine Learning.mp4 27.58 MB
  2. Machine Learning Terminology.mp4 14.04 MB
  3. Machine Learning Project Files.html 254 B
  4. FAQ regarding Python.html 6.23 KB
  5. FAQ regarding Machine Learning.html 6.59 KB
  18. Evaluation Metrics in Machine Learning
  1. Classification vs Regression in Machine Learning.mp4 19.91 MB
  2. Machine Learning Model Performance Evaluation Classification Error Metrics.mp4 100.29 MB
  3. Evaluating Performance Regression Error Metrics in Python.mp4 45.71 MB
  4. Machine Learning With Python.mp4 92.26 MB
  5. Quiz.html 205 B
  19. Supervised Learning with Machine Learning
  1. What is Supervised Learning in Machine Learning.mp4 31.69 MB
  2. Quiz.html 205 B
  2. NumPy Library Introduction
  1. Introduction to NumPy Library.mp4 45.3 MB
  2. The Power of NumPy.mp4 59.87 MB
  3. Quiz.html 205 B
  20. Linear Regression Algorithm in Machine Learning A-Z
  1. Linear Regression Algorithm Theory in Machine Learning A-Z.mp4 34.06 MB
  2. Linear Regression Algorithm With Python Part 1.mp4 76.18 MB
  3. Linear Regression Algorithm With Python Part 2.mp4 106.94 MB
  4. Linear Regression Algorithm With Python Part 3.mp4 70.28 MB
  5. Linear Regression Algorithm With Python Part 4.mp4 89.99 MB
  21. Bias Variance Trade-Off in Machine Learning
  1. What is Bias Variance Trade-Off.mp4 55.04 MB
  2. Quiz.html 205 B
  22. Logistic Regression Algorithm in Machine Learning A-Z
  1. What is Logistic Regression Algorithm in Machine Learning.mp4 27.84 MB
  2. Logistic Regression Algorithm with Python Part 1.mp4 72.23 MB
  3. Logistic Regression Algorithm with Python Part 2.mp4 81.46 MB
  4. Logistic Regression Algorithm with Python Part 3.mp4 34.78 MB
  5. Logistic Regression Algorithm with Python Part 4.mp4 47.16 MB
  6. Logistic Regression Algorithm with Python Part 5.mp4 39.36 MB
  7. Quiz.html 205 B
  23. K-fold Cross-Validation in Machine Learning A-Z
  1. K-Fold Cross-Validation Theory.mp4 17.46 MB
  2. K-Fold Cross-Validation with Python.mp4 34.66 MB
  24. K Nearest Neighbors Algorithm in Machine Learning A-Z
  1. K Nearest Neighbors Algorithm Theory.mp4 28.67 MB
  2. K Nearest Neighbors Algorithm with Python Part 1.mp4 35.05 MB
  3. K Nearest Neighbors Algorithm with Python Part 2.mp4 59.39 MB
  4. K Nearest Neighbors Algorithm with Python Part 3.mp4 31.39 MB
  5. Quiz.html 205 B
  25. Hyperparameter Optimization
  1. Hyperparameter Optimization Theory.mp4 33.15 MB
  2. Hyperparameter Optimization with Python.mp4 47.47 MB
  26. Decision Tree Algorithm in Machine Learning A-Z
  1. Decision Tree Algorithm Theory.mp4 35.76 MB
  2. Decision Tree Algorithm with Python Part 1.mp4 31.54 MB
  3. Decision Tree Algorithm with Python Part 2.mp4 48.97 MB
  4. Decision Tree Algorithm with Python Part 3.mp4 14.71 MB
  5. Decision Tree Algorithm with Python Part 4.mp4 42.49 MB
  6. Decision Tree Algorithm with Python Part 5.mp4 32.68 MB
  7. Quiz.html 205 B
  27. Random Forest Algorithm in Machine Learning A-Z
  1. Random Forest Algorithm Theory.mp4 22.89 MB
  2. Random Forest Algorithm with Pyhon Part 1.mp4 38.59 MB
  3. Random Forest Algorithm with Pyhon Part 2.mp4 38.74 MB
  28. Support Vector Machine Algorithm in Machine Learning A-Z
  1. Support Vector Machine Algorithm Theory.mp4 21.84 MB
  2. Support Vector Machine Algorithm with Python Part 1.mp4 35.56 MB
  3. Support Vector Machine Algorithm with Python Part 2.mp4 41.72 MB
  4. Support Vector Machine Algorithm with Python Part 3.mp4 47.35 MB
  5. Support Vector Machine Algorithm with Python Part 4.mp4 37.56 MB
  6. Quiz.html 205 B
  29. Unsupervised Learning with Machine Learning
  1. Unsupervised Learning Overview.mp4 16.92 MB
  2. Quiz.html 205 B
  3. Creating NumPy Array in Python
  1. Creating NumPy Array with The Array() Function.mp4 29.49 MB
  10. Quiz.html 205 B
  2. Creating NumPy Array with Zeros() Function.mp4 24.06 MB
  3. Creating NumPy Array with Ones() Function.mp4 15.84 MB
  4. Creating NumPy Array with Full() Function.mp4 11.19 MB
  5. Creating NumPy Array with Arange() Function.mp4 12.09 MB
  6. Creating NumPy Array with Eye() Function.mp4 12.57 MB
  7. Creating NumPy Array with Linspace() Function.mp4 7.34 MB
  8. Creating NumPy Array with Random() Function.mp4 43.3 MB
  9. Properties of NumPy Array.mp4 22.01 MB
  30. K Means Clustering Algorithm in Machine Learning A-Z
  1. K Means Clustering Algorithm Theory.mp4 17.14 MB
  2. K Means Clustering Algorithm with Python Part 1.mp4 29.94 MB
  3. K Means Clustering Algorithm with Python Part 2.mp4 29.65 MB
  4. K Means Clustering Algorithm with Python Part 3.mp4 27.76 MB
  5. K Means Clustering Algorithm with Python Part 4.mp4 29.03 MB
  6. Quiz.html 205 B
  31. Hierarchical Clustering Algorithm in machine learning data science
  1. Hierarchical Clustering Algorithm Theory.mp4 28.57 MB
  2. Hierarchical Clustering Algorithm with Python Part 1.mp4 35.51 MB
  3. Hierarchical Clustering Algorithm with Python Part 2.mp4 28.9 MB
  4. Quiz.html 205 B
  32. Principal Component Analysis (PCA) in Machine Learning A-Z
  1. Principal Component Analysis (PCA) Theory.mp4 37.95 MB
  2. Principal Component Analysis (PCA) with Python Part 1.mp4 26.02 MB
  3. Principal Component Analysis (PCA) with Python Part 2.mp4 8.43 MB
  4. Principal Component Analysis (PCA) with Python Part 3.mp4 37.27 MB
  33. Recommender System Algorithm in Machine Learning A-Z
  1. What is the Recommender System Part 1.mp4 23.02 MB
  2. What is the Recommender System Part 2.mp4 17.96 MB
  3. Quiz.html 205 B
  34. First Contact with Kaggle
  1. What is Kaggle.mp4 129.75 MB
  2. FAQ about Kaggle.html 10.94 KB
  3. Registering on Kaggle and Member Login Procedures.mp4 43.55 MB
  4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html 108 B
  5. Getting to Know the Kaggle Homepage.mp4 122.88 MB
  6. Quiz.html 205 B
  35. Competition Section on Kaggle
  1. Competitions on Kaggle Lesson 1.mp4 188.2 MB
  2. Competitions on Kaggle Lesson 2.mp4 191.71 MB
  3. Quiz.html 205 B
  36. Dataset Section on Kaggle
  1. Datasets on Kaggle.mp4 133.21 MB
  2. Quiz.html 205 B
  37. Code Section on Kaggle
  1. Examining the Code Section in Kaggle Lesson 1.mp4 79.55 MB
  2. Examining the Code Section in Kaggle Lesson 2.mp4 105.81 MB
  3. Examining the Code Section in Kaggle Lesson 3.mp4 159.82 MB
  4. Quiz.html 205 B
  38. Discussion Section on Kaggle
  1. What is Discussion on Kaggle.mp4 40.65 MB
  2. Quiz.html 205 B
  39. Other Most Used Options on Kaggle
  1. Courses in Kaggle.mp4 52.16 MB
  2. Ranking Among Users on Kaggle.mp4 107 MB
  3. Blog and Documentation Sections.mp4 40.91 MB
  4. Quiz.html 205 B
  4. Functions in the NumPy Library
  1. Reshaping a NumPy Array Reshape() Function.mp4 26.15 MB
  2. Identifying the Largest Element of a Numpy Array.mp4 15.14 MB
  3. Detecting Least Element of Numpy Array Min(), Ar.mp4 10.19 MB
  4. Concatenating Numpy Arrays Concatenate() Function.mp4 38.38 MB
  5. Splitting One-Dimensional Numpy Arrays The Split.mp4 20.91 MB
  6. Splitting Two-Dimensional Numpy Arrays Split(),.mp4 35.72 MB
  7. Sorting Numpy Arrays Sort() Function.mp4 17.04 MB
  8. Quiz.html 205 B
  40. Details on Kaggle
  1. User Page Review on Kaggle.mp4 81.57 MB
  2. Treasure in The Kaggle.mp4 74.66 MB
  3. Publishing Notebooks on Kaggle.mp4 38.21 MB
  4. What Should Be Done to Achieve Success in Kaggle.mp4 58.42 MB
  5. Quiz.html 205 B
  41. Introduction to Machine Learning with Real Hearth Attack Prediction Project
  1. First Step to the Project.mp4 117.19 MB
  2. FAQ about Machine Learning, Data Science.html 15.29 KB
  3. Notebook Design to be Used in the Project.mp4 104.96 MB
  4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html 108 B
  5. Examining the Project Topic.mp4 76.53 MB
  6. Recognizing Variables In Dataset.mp4 126.88 MB
  7. Quiz.html 205 B
  42. First Organization
  1. Required Python Libraries.mp4 63.57 MB
  2. Loading the Dataset.mp4 9.99 MB
  3. Initial analysis on the dataset.mp4 63.98 MB
  4. Quiz.html 205 B
  43. Preparation For Exploratory Data Analysis (EDA)
  1. Examining Missing Values.mp4 45.78 MB
  2. Examining Unique Values.mp4 44.56 MB
  3. Separating variables (Numeric or Categorical).mp4 15.84 MB
  4. Examining Statistics of Variables.mp4 91.38 MB
  5. Quiz.html 205 B
  44. Exploratory Data Analysis (EDA) - Uni-variate Analysis
  1. Numeric Variables (Analysis with Distplot) Lesson 1.mp4 80.4 MB
  2. Numeric Variables (Analysis with Distplot) Lesson 2.mp4 19.73 MB
  3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp4 74.77 MB
  4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp4 84.04 MB
  5. Examining the Missing Data According to the Analysis Result.mp4 53.78 MB
  6. Quiz.html 205 B
  45. Exploratory Data Analysis (EDA) - Bi-variate Analysis
  1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.mp4 49.34 MB
  10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp4 68.07 MB
  11. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.mp4 38.07 MB
  12. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.mp4 35.46 MB
  13. Relationships between variables (Analysis with Heatmap) Lesson 1.mp4 36.37 MB
  14. Relationships between variables (Analysis with Heatmap) Lesson 2.mp4 90.66 MB
  15. Quiz.html 205 B
  2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.mp4 35.62 MB
  3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.mp4 24.12 MB
  4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.mp4 56.28 MB
  5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp4 28.36 MB
  6. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp4 47.16 MB
  7. Feature Scaling with the Robust Scaler Method.mp4 35.19 MB
  8. Creating a New DataFrame with the Melt() Function.mp4 52.88 MB
  9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp4 41.7 MB
  46. Preparation for Modelling in Machine Learning
  1. Dropping Columns with Low Correlation.mp4 26.82 MB
  10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.mp4 11.46 MB
  11. Separating Data into Test and Training Set.mp4 29.76 MB
  12. Quiz.html 205 B
  2. Visualizing Outliers.mp4 34.87 MB
  3. Dealing with Outliers – Trtbps Variable Lesson 1.mp4 42.84 MB
  4. Dealing with Outliers – Trtbps Variable Lesson 2.mp4 43.91 MB
  5. Dealing with Outliers – Thalach Variable.mp4 36.23 MB
  6. Dealing with Outliers – Oldpeak Variable.mp4 36.08 MB
  7. Determining Distributions of Numeric Variables.mp4 25.2 MB
  8. Transformation Operations on Unsymmetrical Data.mp4 24 MB
  9. Applying One Hot Encoding Method to Categorical Variables.mp4 24.09 MB
  47. Modelling for machine learning
  1. Logistic Regression.mp4 29.34 MB
  2. Cross Validation.mp4 30.21 MB
  3. Roc Curve and Area Under Curve (AUC).mp4 41.68 MB
  4. Hyperparameter Optimization (with GridSearchCV).mp4 58.77 MB
  5. Decision Tree Algorithm.mp4 25.7 MB
  6. Support Vector Machine Algorithm.mp4 24.5 MB
  7. Random Forest Algorithm.mp4 29.78 MB
  8. Hyperparameter Optimization (with GridSearchCV).mp4 52.67 MB
  9. Quiz.html 205 B
  48. Conclusion
  1. Project Conclusion and Sharing.mp4 28.65 MB
  2. Quiz.html 205 B
  49. Extra
  1. Machine Learning & Data Science with Kaggle, Pandas , Numpy.html 266 B
  5. Indexing, Slicing, and Assigning NumPy Arrays
  1. Indexing Numpy Arrays.mp4 26.6 MB
  2. Slicing One-Dimensional Numpy Arrays.mp4 22.29 MB
  3. Slicing Two-Dimensional Numpy Arrays.mp4 34.27 MB
  4. Assigning Value to One-Dimensional Arrays.mp4 18.2 MB
  5. Assigning Value to Two-Dimensional Array.mp4 35.41 MB
  6. Fancy Indexing of One-Dimensional Arrrays.mp4 20.48 MB
  7. Fancy Indexing of Two-Dimensional Arrrays.mp4 45.72 MB
  8. Combining Fancy Index with Normal Indexing.mp4 12.65 MB
  9. Combining Fancy Index with Normal Slicing.mp4 16.46 MB
  6. Operations in Numpy Library
  1. Operations with Comparison Operators.mp4 21.17 MB
  2. Arithmetic Operations in Numpy.mp4 71.87 MB
  3. Statistical Operations in Numpy.mp4 31.98 MB
  4. Solving Second-Degree Equations with NumPy.mp4 24.2 MB
  7. Pandas Library Introduction
  1. Introduction to Pandas Library.mp4 33.93 MB
  2. Pandas Project Files Link.html 180 B
  3. Quiz.html 205 B
  8. Series Structures in the Pandas Library
  1. Creating a Pandas Series with a List.mp4 39.2 MB
  2. Creating a Pandas Series with a Dictionary.mp4 18.29 MB
  3. Creating Pandas Series with NumPy Array.mp4 11.96 MB
  4. Object Types in Series.mp4 19.55 MB
  5. Examining the Primary Features of the Pandas Seri.mp4 18.93 MB
  6. Most Applied Methods on Pandas Series.mp4 48.19 MB
  7. Indexing and Slicing Pandas Series.mp4 29.91 MB
  8. Quiz.html 205 B
  9. DataFrame Structures in Pandas Library
  1. Creating Pandas DataFrame with List.mp4 22.56 MB
  2. Creating Pandas DataFrame with NumPy Array.mp4 12.1 MB
  3. Creating Pandas DataFrame with Dictionary.mp4 15.83 MB
  4. Examining the Properties of Pandas DataFrames.mp4 25.95 MB
  5. Quiz.html 205 B
  ▲ 256 total files

Description


Udemy-Machine Learning & Data Science with Python, Kaggle & Pandas

Machine-Learning-Data-Science-with-Python-Kaggle-Pandas.jpg?042148

DESCRIPTION

 L'apprentissage automatique est une branche de l'intelligence artificielle (IA) et de l'informatique qui se concentre sur l'utilisation de données et d'algorithmes pour imiter la façon dont les humains apprennent, en améliorant progressivement leur précision.

Vous pouvez développer les compétences de base dont vous avez besoin pour progresser dans la construction de réseaux de neurones et la création de fonctions plus complexes grâce aux langages de programmation Python et R. L'apprentissage automatique vous aide à garder une longueur d'avance sur les nouvelles tendances, technologies et applications dans ce domaine.

L’apprentissage automatique est aujourd’hui appliqué à pratiquement tous les domaines. Cela inclut les diagnostics médicaux, la reconnaissance faciale, les prévisions météorologiques, le traitement d’images, etc. Dans toute situation où la reconnaissance, la prédiction et l’analyse de formes sont essentielles, l’apprentissage automatique peut être utile. L’apprentissage automatique est souvent une technologie disruptive lorsqu’il est appliqué à de nouveaux secteurs et niches. Les ingénieurs en apprentissage automatique peuvent trouver de nouvelles façons d’appliquer la technologie d’apprentissage automatique pour optimiser et automatiser les processus existants. Avec les bonnes données, vous pouvez utiliser la technologie d’apprentissage automatique pour identifier des modèles extrêmement complexes et produire des prédictions très précises.

Il est difficile d’imaginer nos vies sans l’apprentissage automatique. Les SMS prédictifs, le filtrage des e-mails et les assistants personnels virtuels comme Alexa d’Amazon et Siri d’iPhone sont autant de technologies qui fonctionnent sur la base d’algorithmes d’apprentissage automatique et de modèles mathématiques. Python, machine learning, django, programmation python, machine learning python, python pour débutants, science des données. Kaggle, statistiques, r, science des données python, apprentissage profond, programmation python, django, apprentissage automatique de a à z, data scientist, python pour la science des données.

A qui s'adresse cette formation

  •      Toute personne souhaitant commencer à apprendre le « Machine Learning »
  •      Toute personne ayant besoin d'un guide complet sur la façon de démarrer et de poursuivre sa carrière avec l'apprentissage automatique
  •      Étudiants intéressés à démarrer des applications de science des données dans un environnement Python
  •      Personnes souhaitant se spécialiser dans l’environnement Anaconda Python pour la science des données et le calcul scientifique
  •      Étudiants souhaitant apprendre l'application de l'apprentissage supervisé (classification) sur des données réelles à l'aide de Python
  •      Toute personne désireuse d'apprendre Python pour le bootcamp de science des données et d'apprentissage automatique sans aucune expérience en codage
  •      Toute personne qui envisage une carrière dans le data scientist,
  •      Développeur de logiciels souhaitant apprendre Python,
  •      Toute personne intéressée par l'apprentissage automatique de a à z
  •      Les personnes qui souhaitent devenir data scientist
  •      Les personnes qui souhaitent apprendre l’apprentissage automatique complet

Prérequis
  •     
Connaissance de base du langage de programmation Python
Être capable d'utiliser et d'installer des logiciels sur un ordinateur
Logiciels et outils gratuits utilisés pendant le cours Machine Learning a-z
Détermination à apprendre l’apprentissage automatique et patience.
Motivation à apprendre la deuxième langue du programme relative au plus grand nombre d'offres d'emploi parmi toutes les autres
Bibliothèques de visualisation de données en python telles que seaborn, matplotlib
Curiosité pour l'apprentissage automatique Python
Désir d'apprendre Python
Désir d'apprendre matplotlib
Désir d'apprendre les pandas et numpy
Désir d'apprendre le machine learning de a à z, de compléter le machine learning
N'importe quel appareil sur lequel vous pouvez regarder le cours, comme un téléphone mobile, un ordinateur ou une tablette.
Regarder les vidéos des conférences dans leur intégralité, jusqu'au bout et dans l'ordre.
Rien d'autre! C'est juste vous, votre ordinateur et votre ambition pour commencer aujourd'hui.




L'AUTEUR

Editeur : Udemy
Parution : 2023
Formateur :  Ali CAVDAR
Durée : 29h



INFORMATION SUR L'UPLOAD

Qualité : WEBrip 720p
Format : MP4
Fichies d'exercices: Inclus

Codec Audio : AAC LC 2.0 à  128 Kb/s
Codec Vidéo : AVC à  1 426 kb/s

Langue : Anglais
Résolution:1 280 x 720

TELECHARGEMENT

NB Fichiers: 256
Poids Total : 9 Go

Related Torrents

torrent name size uploader age seed leech
Udemy - Supervised Machine Learning Explained - The Top 5 Models Posted by freecoursewb in Other
3.4 GB freecoursewb 2 days 2 22
Udemy - Machine learning in Python with Google Colab Posted by freecoursewb in Other
1.2 GB freecoursewb 2 weeks 10 10
Udemy - Complete Machine Learning and Data Science With Python A-Z Posted by freecoursewb in Other
1.9 GB freecoursewb 1 month 5 0
Udemy - Bayesian Machine Learning in Python - A - B Testing Posted by freecoursewb in Other
2.6 GB freecoursewb 1 month 6 3
Udemy - Machine Learning - Algorithmic Trading With Random Forests Posted by freecoursewb in Other
2.5 GB freecoursewb 1 month 1 9