Python Machine Learning Crash Course for Beginners
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1
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Introduction to the Course
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Introduction To Instructor
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Focus of the Course
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Python Practical of the Course
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2
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Machine Learning Applications-Part 1
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Machine Learning Applications-Part 2
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Why Machine Learning is Trending Now
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3
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Supervised Learning
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UnSupervised Learning and Reinforcement Learning
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4
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Features
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Features Practice with Python
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Regression
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Regression Practice with Python
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Classsification
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Classification Practice with Python
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Clustering
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Clustering Practice with Python
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5
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Handling Image Data
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Handling Video and Audio Data
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Handling Text Data
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One Hot Encoding
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Data Standardization
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6
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Machine Learning Model 1
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Machine Learning Model 2
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Machine Learning Model 3
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Training Process, Error, Cost and Loss
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Optimization
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7
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Linear Regression from Scratch- Part 1
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Linear Regression from Scratch- Part 2
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Minimun-to-mean Distance Classifier from Scratch- Part 1
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Minimun-to-mean Distance Classifier from Scratch- Part 2
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K-means Clustering from Scratch- Part 1
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K-means Clustering from Scratch- Part 2
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8
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Overfitting Introduction
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Overfitting example on Python
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Regularization
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Generalization
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Data Snooping and the Test Set
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Cross-validation
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9
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The Accuracy
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The Confusion Matrix
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10
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The Curse of Dimensionality
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The Principal Component Analysis (PCA)
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11
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Introduction to Deep Neural Networks (DNN)
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Introduction to Convolutional Neural Networks (CNN)
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Introduction to Recurrent Neural Networks (CNN)
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12
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Principal Component Analysis (PCA) with Python
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Pipeline in Scikit-Learn for Machine Learning Project
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Cross-validation with Python
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Face Recognition Project with Python
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13
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Mathematical Wrap-up on Machine Learning