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Python for Machine Learning - The Complete Beginners Course - Implementation in Python: Splitting Data into Train and Test Sets - Classification Algorithms: Logistic Regression
In this video, you will learn how to split data into train and test sets. 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: Splitting Data into Train and Test Sets - Classification Algorithms: Decision Tree
In this video, you will learn how to split data into train and test sets. This clip is from the chapter "Classification Algorithms: Decision Tree" 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: 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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Python In Practice - 15 Projects to Master Python - Creating a Machine Learning Model with Dataset
This video helps in creating a machine learning model with a dataset. This clip is from the chapter "Machine Learning with Python" of the series "Python in Practice - 15 Projects to Master Python".This section focuses on machine learning...
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Deep Learning - Deep Neural Network for Beginners Using Python - Logistic Regression Algorithm
In this video, we will talk about the logistic regression algorithm. This clip is from the chapter "Deep Learning" of the series "Deep Learning - Deep Neural Network for Beginners Using Python".In this section, we will dive deeper into...
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Deep Learning - Deep Neural Network for Beginners Using Python - How Gradient Descent Works
In this video, we will understand how gradient descent works. This clip is from the chapter "Deep Learning" of the series "Deep Learning - Deep Neural Network for Beginners Using Python".In this section, we will dive deeper into deep...
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Deep Learning - Deep Neural Network for Beginners Using Python - Gradient Descent Versus Perceptron
In this video, we will understand the difference between gradient descent and perceptron. This clip is from the chapter "Deep Learning" of the series "Deep Learning - Deep Neural Network for Beginners Using Python".In this section, we...
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Data Science and Machine Learning (Theory and Projects) A to Z - Deep Neural Networks and Deep Learning Basics: Gradient Descent
In this video, we will cover gradient descent. 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 - Deep Neural Networks and Deep Learning Basics: convergence Animation
In this video, we will cover convergence animation.
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Data Science and Machine Learning (Theory and Projects) A to Z - Basics of Programming: Understanding the Algorithm
In this video, we will understand the algorithm. This clip is from the chapter "Basics for Data Science: Python for Data Science and Data Analysis" 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 - Scikit-Learn for Machine Learning: Introduction to Scikit-Learn
In this video, we will cover an introduction to Scikit-Learn. This clip is from the chapter "Basics for Data Science: Python for Data Science and Data Analysis" of the series "Data Science and Machine Learning (Theory and Projects) A to...
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Data Structures and Algorithms The Complete Masterclass - Section Introduction
This video provides an introduction to this section. This clip is from the chapter "Big O Notation" of the series "Data Structures and Algorithms: The Complete Masterclass".This section introduces you to big O notation in data structures.
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Data Structures and Algorithms The Complete Masterclass - Logarithm
This video focuses on logarithm in data structures. This clip is from the chapter "Essential Concepts - I" of the series "Data Structures and Algorithms: The Complete Masterclass".This is the first part of the two-part section that...
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Data Structures and Algorithms The Complete Masterclass - Linear Search
This video explains the concept of a linear search. This clip is from the chapter "Algorithm: Searching" of the series "Data Structures and Algorithms: The Complete Masterclass".This section explains the concept of searching.