Udemy - Python : Machine Learning, Deep Learning, Pandas, Matplotlib [Giga Course]

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Udemy - Python : Machine Learning, Deep Learning, Pandas, Matplotlib [Giga Course]

Torrent Contents Size: 4.5 GB

Udemy - Python : Machine Learning, Deep Learning, Pandas, Matplotlib [Giga Course]
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001 AI, Machine Learning and Deep Learning.en.srt
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001 AI, Machine Learning and Deep Learning.mp4
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001 BONUS.html
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001 Data Frame Attributes and Methods Part – I.en.srt
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001 Data Frame Attributes and Methods Part – I.mp4
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001 Data Types in Python.en.srt
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001 Data Types in Python.mp4
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001 Data Visualization with Python Masterclass.en.srt
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001 Data Visualization with Python Masterclass.mp4
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001 Installing Anaconda Distribution and Python.en.srt
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001 Installing Anaconda Distribution and Python.mp4
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001 Introduction to Deep Learning with Python.en.srt
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001 Introduction to Deep Learning with Python.mp4
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001 Logic of OOP.en.srt
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001 Logic of OOP.mp4
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001 Project - 1.en.srt
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001 Project - 1.mp4
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001 Understanding RNN and LSTM Networks.en.srt
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001 Understanding RNN and LSTM Networks.mp4
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001 What is Artificial Neural Network (ANN)_.en.srt
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001 What is Artificial Neural Network (ANN)_.mp4
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001 What is CNN_.en.srt
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001 What is CNN_.mp4
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001 What is Geoplotlib_.en.srt
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001 What is Geoplotlib_.mp4
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001 What is Numpy_.en.srt
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001 What is Numpy_.mp4
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001 What is Pandas_.en.srt
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001 What is Pandas_.mp4
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001 What is Seaborn_.en.srt
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001 What is Seaborn_.mp4
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001 What is Transfer Learning.en.srt
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001 What is Transfer Learning.mp4
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002 Anatomy of Neural Network.en.srt
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002 Anatomy of Neural Network.mp4
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002 Constructor.en.srt
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002 Constructor.mp4
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002 Controlling Figure Aesthetics.en.srt
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002 Controlling Figure Aesthetics.mp4
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002 Data Frame attributes and Methods Part – II.en.srt
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002 Data Frame attributes and Methods Part – II.mp4
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002 Example - 1.en.srt
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002 Example - 1.mp4
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002 History of Machine Learning.en.srt
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002 History of Machine Learning.mp4
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002 Operators in Python.en.srt
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002 Operators in Python.mp4
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002 Overview of Jupyter Notebook and Google Colab.en.srt
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002 Overview of Jupyter Notebook and Google Colab.mp4
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002 Project - 2.en.srt
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002 Project - 2.mp4
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002 Project Files and Course Documents.html
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002 Series and Features.en.srt
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002 Series and Features.mp4
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002 Using Matplotlib.en.srt
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002 Using Matplotlib.mp4
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002 Why Numpy_.en.srt
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002 Why Numpy_.mp4
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003 Array and features.en.srt
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003 Array and features.mp4
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003 Conditionals.en.srt
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003 Conditionals.mp4
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003 Creating a Simple ANN.en.srt
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003 Creating a Simple ANN.mp4
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003 Data Frame attributes and Methods Part – III.en.srt
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003 Data Frame attributes and Methods Part – III.mp4
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003 Example - 2.en.srt
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003 Example - 2.mp4
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003 Example.en.srt
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003 Example.mp4
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003 Methods.en.srt
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003 Methods.mp4
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003 Project - 3.en.srt
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003 Project - 3.mp4
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003 Pyplot – Pylab - Matplotlib.en.srt
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003 Pyplot – Pylab - Matplotlib.mp4
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003 Turing Machine and Turing Test.en.srt
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003 Turing Machine and Turing Test.mp4
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004 Array’s Operators.en.srt
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004 Array’s Operators.mp4
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004 Color Palettes.en.srt
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004 Color Palettes.mp4
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004 Example - 3.en.srt
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004 Example - 3.mp4
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004 Figure, Subplot and Axes.en.srt
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004 Figure, Subplot and Axes.mp4
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004 Inheritance.en.srt
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004 Inheritance.mp4
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004 Loops.en.srt
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004 Loops.mp4
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004 Multi index.en.srt
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004 Multi index.mp4
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004 Project - 4.en.srt
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004 Project - 4.mp4
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004 Tensor Operations.en.srt
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004 Tensor Operations.mp4
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004 What is Deep Learning.en.srt
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004 What is Deep Learning.mp4
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005 Basic Plots in Seaborn.mp4
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005 Figure Customization.en.srt
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005 Figure Customization.mp4
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005 Groupby Operations.en.srt
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005 Groupby Operations.mp4
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005 Learning representations from data.en.srt
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005 Learning representations from data.mp4
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005 Lists, Tuples, Dictionaries and Sets.en.srt
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005 Lists, Tuples, Dictionaries and Sets.mp4
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005 Numpy Functions.en.srt
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005 Numpy Functions.mp4
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005 Overriding and Overloading.en.srt
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005 Overriding and Overloading.mp4
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005 Tensor Operations 2.en.srt
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005 Tensor Operations 2.mp4
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006 Data Type Operators and Methods.en.srt
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006 Data Type Operators and Methods.mp4
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006 Indexing and Slicing.en.srt
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006 Indexing and Slicing.mp4
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006 Keras API.en.srt
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006 Keras API.mp4
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006 Missing Data and Data Munging Part I.en.srt
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006 Missing Data and Data Munging Part I.mp4
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006 Multi-Plots in Seaborn.en.srt
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006 Multi-Plots in Seaborn.mp4
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006 Plot Customization.en.srt
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006 Plot Customization.mp4
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006 Workflow of Machine Learning.en.srt
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006 Workflow of Machine Learning.mp4
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007 Grid, Spines, Ticks.en.srt
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007 Grid, Spines, Ticks.mp4
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007 Machine Learning Methods.en.srt
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007 Machine Learning Methods.mp4
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007 Missing Data and Data Munging Part II.en.srt
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007 Missing Data and Data Munging Part II.mp4
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007 Modules in Python.en.srt
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007 Modules in Python.mp4
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007 Numpy Exercises.en.srt
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007 Numpy Exercises.mp4
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007 Optimizers.en.srt
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007 Optimizers.mp4
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007 Regression Plots and Squarify.en.srt
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007 Regression Plots and Squarify.mp4
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008 Basic Plots in Matplotlib I.en.srt
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008 Basic Plots in Matplotlib I.mp4
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008 Dealing with Missing Data.en.srt
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008 Dealing with Missing Data.mp4
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008 Functions in Python.en.srt
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008 Functions in Python.mp4
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008 Supervised Machine Learning Methods - 1.en.srt
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008 Supervised Machine Learning Methods - 1.mp4
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008 Using Numpy in Linear Algebra.en.srt
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008 Using Numpy in Linear Algebra.mp4
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008 Using Numpy in Linear Algebra.mp4.vtx
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008 What is TensorFlow.en.srt
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008 What is TensorFlow.mp4
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009 Basic Plots in Matplotlib II.en.srt
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009 Basic Plots in Matplotlib II.mp4
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009 Combining Data Frames Part – I.en.srt
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009 Combining Data Frames Part – I.mp4
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009 Exercise Analyse.en.srt
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009 Exercise Analyse.mp4
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009 NumExpr Guide.en.srt
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009 NumExpr Guide.mp4
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009 Supervised Machine Learning Methods - 2.en.srt
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009 Supervised Machine Learning Methods - 2.mp4
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010 Combining Data Frames Part – II.en.srt
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010 Combining Data Frames Part – II.mp4
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010 Exercise Solution.en.srt
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010 Exercise Solution.mp4
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010 Supervised Machine Learning Methods - 3.en.srt
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010 Supervised Machine Learning Methods - 3.mp4
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011 Supervised Machine Learning Methods - 4.en.srt
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011 Supervised Machine Learning Methods - 4.mp4
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011 Work with Dataset Files.en.srt
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011 Work with Dataset Files.mp4
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012 Unsupervised Machine Learning Methods.en.srt
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012 Unsupervised Machine Learning Methods.mp4
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013 Gathering data.en.srt
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013 Gathering data.mp4
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014 Data pre-processing.en.srt
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014 Data pre-processing.mp4
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015 Choosing the right algorithm and model.en.srt
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015 Choosing the right algorithm and model.mp4
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016 Training and testing the model.en.srt
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016 Training and testing the model.mp4
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017 Evaluation.en.srt
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017 Evaluation.mp4
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