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Applied Machine Learning - Foundations\3.2. Exploratory Data Analysis and Data Cleaning\12.Exploring continuous features.mp4
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Applied Machine Learning - Foundations\3.2. Exploratory Data Analysis and Data Cleaning\15.Exploring categorical features.mp4
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Applied Machine Learning - Foundations\3.2. Exploratory Data Analysis and Data Cleaning\14.Continuous data cleaning.mp4
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Applied Machine Learning - Foundations\3.2. Exploratory Data Analysis and Data Cleaning\13.Plotting continuous features.mp4
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Applied Machine Learning - Foundations\3.2. Exploratory Data Analysis and Data Cleaning\11.Why do we need to explore and clean our data.mp4
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Applied Machine Learning - Foundations\3.2. Exploratory Data Analysis and Data Cleaning\17.Categorical data cleaning.mp4
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Applied Machine Learning - Foundations\3.2. Exploratory Data Analysis and Data Cleaning\16.Plotting categorical features.mp4
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Applied Machine Learning - Foundations\Exercise Files\Ex_Files_Applied_Machine_Learning.zip |
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Applied Machine Learning - Foundations\1.Introduction\03.What tools you need.mp4
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Applied Machine Learning - Foundations\1.Introduction\04.Using the exercise files.mp4
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Applied Machine Learning - Foundations\1.Introduction\01.Leveraging machine learning.mp4
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Applied Machine Learning - Foundations\1.Introduction\02.What you should know.mp4
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Applied Machine Learning - Foundations\2.1. Machine Learning Basics\05.What is machine learning.mp4
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Applied Machine Learning - Foundations\2.1. Machine Learning Basics\08.Machine learning vs. Deep learning vs. Artificial intelligence.mp4
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Applied Machine Learning - Foundations\2.1. Machine Learning Basics\10.Common challenges.mp4
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Applied Machine Learning - Foundations\2.1. Machine Learning Basics\06.What kind of problems can this help you solve.mp4
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Applied Machine Learning - Foundations\2.1. Machine Learning Basics\09.Demos of machine learning in real life.mp4
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Applied Machine Learning - Foundations\2.1. Machine Learning Basics\07.Why Python.mp4
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Applied Machine Learning - Foundations\6.5. End-to-End Pipeline\33.Tune hyperparameters.mp4
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Applied Machine Learning - Foundations\6.5. End-to-End Pipeline\32.Fit a basic model using cross-validation.mp4
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Applied Machine Learning - Foundations\6.5. End-to-End Pipeline\31.Split data into train_validation_test set.mp4
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Applied Machine Learning - Foundations\6.5. End-to-End Pipeline\28.Overview of the process.mp4
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Applied Machine Learning - Foundations\6.5. End-to-End Pipeline\29.Clean continuous features.mp4
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Applied Machine Learning - Foundations\6.5. End-to-End Pipeline\34.Evaluate results on validation set.mp4
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Applied Machine Learning - Foundations\6.5. End-to-End Pipeline\30.Clean categorical features.mp4
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Applied Machine Learning - Foundations\6.5. End-to-End Pipeline\35.Final model selection and evaluation on test set.mp4
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Applied Machine Learning - Foundations\7.Conclusion\36.Next steps.mp4
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Applied Machine Learning - Foundations\5.4. Optimizing a Model\27.Regularization.mp4
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Applied Machine Learning - Foundations\5.4. Optimizing a Model\25.Finding the optimal tradeoff.mp4
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Applied Machine Learning - Foundations\5.4. Optimizing a Model\24.What is overfitting.mp4
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Applied Machine Learning - Foundations\5.4. Optimizing a Model\26.Hyperparameter tuning.mp4
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Applied Machine Learning - Foundations\5.4. Optimizing a Model\22.Bias_Variance tradeoff.mp4
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Applied Machine Learning - Foundations\5.4. Optimizing a Model\23.What is underfitting.mp4
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Applied Machine Learning - Foundations\4.3. Measuring Success\19.Split data for train_validation_test set.mp4
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Applied Machine Learning - Foundations\4.3. Measuring Success\18.Why do we split up our data.mp4
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Applied Machine Learning - Foundations\4.3. Measuring Success\21.Establish an evaluation framework.mp4
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Applied Machine Learning - Foundations\4.3. Measuring Success\20.What is cross-validation.mp4
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