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Название [GigaCourse.Com] Udemy - Python Machine Learning, Deep Learning, Pandas, Matplotlib
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[GigaCourse.Com].url 49б
[GigaCourse.Com].url 49б
[GigaCourse.Com].url 49б
001 AI, Machine Learning and Deep Learning.en.srt 5.69Кб
001 AI, Machine Learning and Deep Learning.mp4 16.23Мб
001 BONUS.html 30.16Кб
001 Data Frame Attributes and Methods Part – I.en.srt 16.16Кб
001 Data Frame Attributes and Methods Part – I.mp4 79.75Мб
001 Data Types in Python.en.srt 14.07Кб
001 Data Types in Python.mp4 41.08Мб
001 Data Visualization with Python Masterclass.en.srt 3.67Кб
001 Data Visualization with Python Masterclass.mp4 17.88Мб
001 Installing Anaconda Distribution and Python.en.srt 4.79Кб
001 Installing Anaconda Distribution and Python.mp4 37.54Мб
001 Introduction to Deep Learning with Python.en.srt 5.88Кб
001 Introduction to Deep Learning with Python.mp4 14.51Мб
001 Logic of OOP.en.srt 5.30Кб
001 Logic of OOP.mp4 16.38Мб
001 Project - 1.en.srt 20.90Кб
001 Project - 1.mp4 101.56Мб
001 Understanding RNN and LSTM Networks.en.srt 15.14Кб
001 Understanding RNN and LSTM Networks.mp4 50.85Мб
001 What is Artificial Neural Network (ANN)_.en.srt 8.40Кб
001 What is Artificial Neural Network (ANN)_.mp4 23.01Мб
001 What is CNN_.en.srt 17.86Кб
001 What is CNN_.mp4 72.43Мб
001 What is Geoplotlib_.en.srt 10.58Кб
001 What is Geoplotlib_.mp4 32.21Мб
001 What is Numpy_.en.srt 7.64Кб
001 What is Numpy_.mp4 26.74Мб
001 What is Pandas_.en.srt 6.54Кб
001 What is Pandas_.mp4 22.32Мб
001 What is Seaborn_.en.srt 5.20Кб
001 What is Seaborn_.mp4 12.89Мб
001 What is Transfer Learning.en.srt 19.43Кб
001 What is Transfer Learning.mp4 85.32Мб
002 Anatomy of Neural Network.en.srt 10.74Кб
002 Anatomy of Neural Network.mp4 42.28Мб
002 Constructor.en.srt 7.16Кб
002 Constructor.mp4 33.89Мб
002 Controlling Figure Aesthetics.en.srt 11.19Кб
002 Controlling Figure Aesthetics.mp4 39.19Мб
002 Data Frame attributes and Methods Part – II.en.srt 11.88Кб
002 Data Frame attributes and Methods Part – II.mp4 56.97Мб
002 Example - 1.en.srt 9.95Кб
002 Example - 1.mp4 36.40Мб
002 History of Machine Learning.en.srt 8.05Кб
002 History of Machine Learning.mp4 23.85Мб
002 Operators in Python.en.srt 11.06Кб
002 Operators in Python.mp4 29.62Мб
002 Overview of Jupyter Notebook and Google Colab.en.srt 5.95Кб
002 Overview of Jupyter Notebook and Google Colab.mp4 25.37Мб
002 Project - 2.en.srt 21.62Кб
002 Project - 2.mp4 169.25Мб
002 Project Files and Course Documents.html 1.17Кб
002 Series and Features.en.srt 19.46Кб
002 Series and Features.mp4 83.55Мб
002 Using Matplotlib.en.srt 7.85Кб
002 Using Matplotlib.mp4 26.53Мб
002 Why Numpy_.en.srt 5.05Кб
002 Why Numpy_.mp4 13.64Мб
003 Array and features.en.srt 11.94Кб
003 Array and features.mp4 47.91Мб
003 Conditionals.en.srt 10.03Кб
003 Conditionals.mp4 34.63Мб
003 Creating a Simple ANN.en.srt 15.10Кб
003 Creating a Simple ANN.mp4 79.48Мб
003 Data Frame attributes and Methods Part – III.en.srt 9.79Кб
003 Data Frame attributes and Methods Part – III.mp4 48.05Мб
003 Example.en.srt 10.01Кб
003 Example.mp4 51.32Мб
003 Example - 2.en.srt 19.26Кб
003 Example - 2.mp4 76.27Мб
003 Methods.en.srt 4.30Кб
003 Methods.mp4 23.64Мб
003 Project - 3.en.srt 15.51Кб
003 Project - 3.mp4 82.34Мб
003 Pyplot – Pylab - Matplotlib.en.srt 7.54Кб
003 Pyplot – Pylab - Matplotlib.mp4 26.60Мб
003 Turing Machine and Turing Test.en.srt 13.79Кб
003 Turing Machine and Turing Test.mp4 40.90Мб
004 Array’s Operators.en.srt 4.36Кб
004 Array’s Operators.mp4 17.58Мб
004 Color Palettes.en.srt 15.12Кб
004 Color Palettes.mp4 45.42Мб
004 Example - 3.en.srt 11.78Кб
004 Example - 3.mp4 47.81Мб
004 Figure, Subplot and Axes.en.srt 18.19Кб
004 Figure, Subplot and Axes.mp4 65.66Мб
004 Inheritance.en.srt 7.02Кб
004 Inheritance.mp4 32.63Мб
004 Loops.en.srt 12.43Кб
004 Loops.mp4 49.10Мб
004 Multi index.en.srt 12.49Кб
004 Multi index.mp4 50.80Мб
004 Project - 4.en.srt 15.20Кб
004 Project - 4.mp4 72.26Мб
004 Tensor Operations.en.srt 11.38Кб
004 Tensor Operations.mp4 62.12Мб
004 What is Deep Learning.en.srt 7.38Кб
004 What is Deep Learning.mp4 20.53Мб
005 Basic Plots in Seaborn.mp4 92.79Мб
005 Figure Customization.en.srt 14.54Кб
005 Figure Customization.mp4 59.06Мб
005 Groupby Operations.en.srt 12.81Кб
005 Groupby Operations.mp4 52.59Мб
005 Learning representations from data.en.srt 13.93Кб
005 Learning representations from data.mp4 34.90Мб
005 Lists, Tuples, Dictionaries and Sets.en.srt 18.21Кб
005 Lists, Tuples, Dictionaries and Sets.mp4 66.35Мб
005 Numpy Functions.en.srt 19.86Кб
005 Numpy Functions.mp4 78.52Мб
005 Overriding and Overloading.en.srt 9.74Кб
005 Overriding and Overloading.mp4 58.82Мб
005 Tensor Operations 2.en.srt 8.26Кб
005 Tensor Operations 2.mp4 29.78Мб
006 Data Type Operators and Methods.en.srt 9.31Кб
006 Data Type Operators and Methods.mp4 40.46Мб
006 Indexing and Slicing.en.srt 8.99Кб
006 Indexing and Slicing.mp4 40.39Мб
006 Keras API.en.srt 8.26Кб
006 Keras API.mp4 23.30Мб
006 Missing Data and Data Munging Part I.en.srt 22.86Кб
006 Missing Data and Data Munging Part I.mp4 79.35Мб
006 Multi-Plots in Seaborn.en.srt 11.08Кб
006 Multi-Plots in Seaborn.mp4 40.94Мб
006 Plot Customization.en.srt 6.90Кб
006 Plot Customization.mp4 25.75Мб
006 Workflow of Machine Learning.en.srt 11.15Кб
006 Workflow of Machine Learning.mp4 31.67Мб
007 Grid, Spines, Ticks.en.srt 8.48Кб
007 Grid, Spines, Ticks.mp4 22.39Мб
007 Machine Learning Methods.en.srt 16.62Кб
007 Machine Learning Methods.mp4 45.55Мб
007 Missing Data and Data Munging Part II.en.srt 11.23Кб
007 Missing Data and Data Munging Part II.mp4 40.79Мб
007 Modules in Python.en.srt 5.53Кб
007 Modules in Python.mp4 21.06Мб
007 Numpy Exercises.en.srt 15.41Кб
007 Numpy Exercises.mp4 74.18Мб
007 Optimizers.en.srt 12.44Кб
007 Optimizers.mp4 42.32Мб
007 Regression Plots and Squarify.en.srt 16.11Кб
007 Regression Plots and Squarify.mp4 56.50Мб
008 Basic Plots in Matplotlib I.en.srt 31.55Кб
008 Basic Plots in Matplotlib I.mp4 104.40Мб
008 Dealing with Missing Data.en.srt 16.28Кб
008 Dealing with Missing Data.mp4 69.23Мб
008 Functions in Python.en.srt 9.29Кб
008 Functions in Python.mp4 26.06Мб
008 Supervised Machine Learning Methods - 1.en.srt 10.75Кб
008 Supervised Machine Learning Methods - 1.mp4 30.94Мб
008 Using Numpy in Linear Algebra.en.srt 31.85Кб
008 Using Numpy in Linear Algebra.mp4 112.16Мб
008 Using Numpy in Linear Algebra.mp4.vtx 442.50Кб
008 What is TensorFlow.en.srt 20.79Кб
008 What is TensorFlow.mp4 62.61Мб
009 Basic Plots in Matplotlib II.en.srt 16.24Кб
009 Basic Plots in Matplotlib II.mp4 51.52Мб
009 Combining Data Frames Part – I.en.srt 18.01Кб
009 Combining Data Frames Part – I.mp4 103.55Мб
009 Exercise Analyse.en.srt 2.26Кб
009 Exercise Analyse.mp4 5.70Мб
009 NumExpr Guide.en.srt 8.00Кб
009 NumExpr Guide.mp4 42.19Мб
009 Supervised Machine Learning Methods - 2.en.srt 15.80Кб
009 Supervised Machine Learning Methods - 2.mp4 55.36Мб
010 Combining Data Frames Part – II.en.srt 18.13Кб
010 Combining Data Frames Part – II.mp4 84.17Мб
010 Exercise Solution.en.srt 6.65Кб
010 Exercise Solution.mp4 47.69Мб
010 Supervised Machine Learning Methods - 3.en.srt 16.38Кб
010 Supervised Machine Learning Methods - 3.mp4 56.07Мб
011 Supervised Machine Learning Methods - 4.en.srt 19.46Кб
011 Supervised Machine Learning Methods - 4.mp4 70.31Мб
011 Work with Dataset Files.en.srt 12.09Кб
011 Work with Dataset Files.mp4 70.73Мб
012 Unsupervised Machine Learning Methods.en.srt 27.60Кб
012 Unsupervised Machine Learning Methods.mp4 87.94Мб
013 Gathering data.en.srt 5.86Кб
013 Gathering data.mp4 17.61Мб
014 Data pre-processing.en.srt 6.51Кб
014 Data pre-processing.mp4 25.97Мб
015 Choosing the right algorithm and model.en.srt 9.21Кб
015 Choosing the right algorithm and model.mp4 148.96Мб
016 Training and testing the model.en.srt 6.48Кб
016 Training and testing the model.mp4 83.89Мб
017 Evaluation.en.srt 7.67Кб
017 Evaluation.mp4 24.37Мб
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