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Python for Machine Learning - The Complete Beginners Course - Implementation in Python: Splitting Data into Train and Test Sets - Classification Algorithms: K-Nearest Neighbors
In this video, you will learn how to split data into train and test sets. This clip is from the chapter "Classification Algorithms: K-Nearest Neighbors" of the series "Python for Machine Learning - The Complete Beginner's Course".In this...
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Python for Machine Learning - The Complete Beginners Course - Implementation in Python: Results Prediction and Confusion Matrix - Classification Algorithms: Logistic Regression
In this video, you will learn about results prediction and confusion matrix. This clip is from the chapter "Classification Algorithms: Logistic Regression" of the series "Python for Machine Learning - The Complete Beginner's Course".In...
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Python for Machine Learning - The Complete Beginners Course - Implementation in Python: Results Prediction and Accuracy
In this video, you will learn about results prediction and accuracy. This clip is from the chapter "Classification Algorithms: Decision Tree" of the series "Python for Machine Learning - The Complete Beginner's Course".In this section,...
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Python for Machine Learning - The Complete Beginners Course - Implementation in Python: Pre-Processing
In this video, you will learn how to do pre-processing. This clip is from the chapter "Classification Algorithms: Logistic Regression" of the series "Python for Machine Learning - The Complete Beginner's Course".In this section, we will...
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Python for Machine Learning - The Complete Beginners Course - Implementation in Python: Importing the KNN Classifier
In this video, you will learn how to import the KNN classifier. This clip is from the chapter "Classification Algorithms: K-Nearest Neighbors" of the series "Python for Machine Learning - The Complete Beginner's Course".In this section,...
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Python for Machine Learning - The Complete Beginners Course - Implementation in Python: Importing the Dataset
In this video, you will learn how to import the dataset. This clip is from the chapter "Classification Algorithms: K-Nearest Neighbors" of the series "Python for Machine Learning - The Complete Beginner's Course".In this section, we will...
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Python for Machine Learning - The Complete Beginners Course - Implementation in Python: Importing Libraries and Datasets - Recommender System
In this video, you will learn how to import libraries and datasets. This clip is from the chapter "Recommender System" of the series "Python for Machine Learning - The Complete Beginner's Course".In this section, we will cover the...
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Python for Machine Learning - The Complete Beginners Course - Implementation in Python: Importing Libraries and Datasets - Classification Algorithms: Logistic Regression
In this video, you will learn how to import libraries and datasets. This clip is from the chapter "Classification Algorithms: Logistic Regression" of the series "Python for Machine Learning - The Complete Beginner's Course".In this...
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Python for Machine Learning - The Complete Beginners Course - Implementation in Python
In this video, you will learn implementation in Python. This clip is from the chapter "Clustering" of the series "Python for Machine Learning - The Complete Beginner's Course".In this section, you will learn about clustering.
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Python for Machine Learning - The Complete Beginners Course - Grabbing the Ratings for Two Movies
In this video, you will learn how to grab the ratings for two movies. This clip is from the chapter "Recommender System" of the series "Python for Machine Learning - The Complete Beginner's Course".In this section, we will cover the...
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Python for Machine Learning - The Complete Beginners Course - Data Pre-Processing
In this video, you will learn about data pre-processing. This clip is from the chapter "Recommender System" of the series "Python for Machine Learning - The Complete Beginner's Course".In this section, we will cover the recommender system.
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Data Science and Machine Learning (Theory and Projects) A to Z - Features in Data Science: Features Dimensions Activity
In this video, we will cover features dimensions activity. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and...
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Data Science and Machine Learning (Theory and Projects) A to Z - Feature Selection: Filter Methods
In this video, we will cover filter methods. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In...
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Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: Supervised PCA and Fishers Linear Discriminant Analysis Activity
In this video, we will cover Supervised PCA and Fishers linear discriminant analysis activity. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and...
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Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: Supervised PCA and Fishers Linear Discriminant Analysis
In this video, we will cover supervised PCA and Fishers linear discriminant analysis. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine...
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Data Science and Machine Learning (Theory and Projects) A to Z - RNN Architecture: ManyToOne Model Solution
In this video, we will cover ManyToOne model solution. This clip is from the chapter "Deep learning: Recurrent Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section,...
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Data Science and Machine Learning (Theory and Projects) A to Z - Probability Model: Probability Models BayesRule
In this video, we will cover probability models BayesRule. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and Projects) A...
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Data Science and Machine Learning (Theory and Projects) A to Z - Optional Estimation: Logistic Regression
In this video, we will cover logistic regression.
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Data Science and Machine Learning (Theory and Projects) A to Z - Optional Estimation: DNN
In this video, we will understand DNN.
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Data Science and Machine Learning (Theory and Projects) A to Z - Object Detection: Hand Engineering Versus CNNs
In this video, we will cover hand engineering versus CNNs. This clip is from the chapter "Deep learning: Convolutional Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this...
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Data Science and Machine Learning (Theory and Projects) A to Z - Introduction to Machine Learning: Classification
In this video, we will cover classification. This clip is from the chapter "Deep learning: Artificial Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section, we will...
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Data Science and Machine Learning (Theory and Projects) A to Z - Sentiment Classification using RNN: Vocabulary Implementation
In this video, we will cover vocabulary implementation. This clip is from the chapter "Deep learning: Recurrent Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section,...
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Data Science and Machine Learning (Theory and Projects) A to Z - Sentiment Classification using RNN: RNN Setup 2
In this video, we will cover RNN setup 2. This clip is from the chapter "Deep learning: Recurrent Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section, we will cover...
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Data Science and Machine Learning (Theory and Projects) A to Z - Machine Learning Model Performance Metrics: The Confusion Matrix
In this video, we will cover the confusion matrix. This clip is from the chapter "Machine Learning: Machine Learning Crash Course" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section, we will...