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๐—•๐˜‚๐—ถ๐—น๐—ฑ๐—ถ๐—ป๐—ด ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐— ๐—ผ๐—ฑ๐—ฒ๐—น๐˜€ ๐˜„๐—ถ๐˜๐—ต ๐—–๐—ต๐—ฎ๐˜๐—š๐—ฃ๐—ง Part -2 : Modeling Process and EDA

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1.1 Machine Learning Welcome

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1.2 Machine Learning What is Machine Learning

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2.1 Machine Learning Model Representation

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2.7 Machine Learning Gradient Descent For Linear Regression

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4.5 Machine Learning Features and Polynomial Regression

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5.6 Machine Learning Vectorization

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6.1 Machine Learning Classification

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7.1 Machine Learning The Problem of Overfitting

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7.3 Machine Learning Regularized Linear Regression

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7.4 Machine Learning Regularized Logistic Regression

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8.1 Machine Learning Non-Linear Hypotheses

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8.3 Machine Learning Model Representation I

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8.4 Machine Learning Model Representation II

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8.6 Machine Learning Examples and Intuitions II

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9.2 Machine Learning Backpropagation Algorithm

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10.3 Machine Learning Model Selection and Train,Validation,Test Sets

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10.4 Machine Learning Diagnosing Bias vs Variance

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11.5 Machine Learning Data For Machine Learning

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12.3 Machine Learning Mathematics Behind Large Margin Classification

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12.6 Machine Learning Using An SVM

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13.2 Machine Learning K-Means Algorithm

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13.4 Machine Learning Random Initialization

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14.4 Machine Learning Priciple Component Analysis Algorithm

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14.7 Machine Learning Advice for Applying PCA

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15.1 Machine Learning Problem Motivation

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15.3 Machine Learning Algorithm

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15.6 Machine Learning Choosing What Features to Use

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15.7 Machine Learning Multivariate Gaussian Distribution (Optional)

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