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Machine Learning with Python Part 2 - Model Training and Evaluation

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Machine Learning with Python Part 2 - Model Training and Evaluation
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Welcome to Part 2 of our Machine Learning tutorial series! In this video, we take a deep dive into model training and evaluation using Python. Building upon the concepts covered in Part 1, we guide you through the process of developing and refining predictive models.
Join us as we explore popular Machine Learning algorithms, including decision trees, support vector machines, and ensemble methods. Learn how to train models, optimize hyperparameters, and evaluate their performance using various metrics. We provide practical examples using Python and libraries like scikit-learn.
By the end of this tutorial, you'll have a comprehensive understanding of model training, selection, and evaluation in Machine Learning. Subscribe to our channel to stay updated with our latest tutorials and enhance your skills in Python-based Machine Learning!
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00:00 - Overview
00:56 - Role of Algorithm ?
02:40 - Selecting Algorithm for Machine Learning
11:26 - Training The Model for Machine Learning
18:37 - Splitting Data and Training the Model
05:53 - Supervised Machine Learning Vs. unsupervised Machine Learning
12:35 - Getting started with Python and Jupyter Notebook
33:02 - Testing The Model's Accuracy
55:45 - Summary
Tags: #MachineLearning #PythonTutorial #ModelTraining #ModelEvaluation #DecisionTrees #SupportVectorMachines #EnsembleMethods #HyperparameterOptimization #ScikitLearn #PredictiveModels#ModelTrainingTechniques #ModelSelection #ModelEvaluationMethods #DecisionTreeAlgorithms #SupportVectorMachine #EnsembleLearning #HyperparameterTuning #ModelPerformanceMetrics #ScikitLearnTutorial #MachineLearningModels #ModelOptimization #ModelComparison #ModelMetrics #ModelValidation #Overfitting #Underfitting
🔗 Support Our Channel: Donate through PayPal
Welcome to Part 2 of our Machine Learning tutorial series! In this video, we take a deep dive into model training and evaluation using Python. Building upon the concepts covered in Part 1, we guide you through the process of developing and refining predictive models.
Join us as we explore popular Machine Learning algorithms, including decision trees, support vector machines, and ensemble methods. Learn how to train models, optimize hyperparameters, and evaluate their performance using various metrics. We provide practical examples using Python and libraries like scikit-learn.
By the end of this tutorial, you'll have a comprehensive understanding of model training, selection, and evaluation in Machine Learning. Subscribe to our channel to stay updated with our latest tutorials and enhance your skills in Python-based Machine Learning!
🔗 Support Our Channel: Donate through PayPal
00:00 - Overview
00:56 - Role of Algorithm ?
02:40 - Selecting Algorithm for Machine Learning
11:26 - Training The Model for Machine Learning
18:37 - Splitting Data and Training the Model
05:53 - Supervised Machine Learning Vs. unsupervised Machine Learning
12:35 - Getting started with Python and Jupyter Notebook
33:02 - Testing The Model's Accuracy
55:45 - Summary
Tags: #MachineLearning #PythonTutorial #ModelTraining #ModelEvaluation #DecisionTrees #SupportVectorMachines #EnsembleMethods #HyperparameterOptimization #ScikitLearn #PredictiveModels#ModelTrainingTechniques #ModelSelection #ModelEvaluationMethods #DecisionTreeAlgorithms #SupportVectorMachine #EnsembleLearning #HyperparameterTuning #ModelPerformanceMetrics #ScikitLearnTutorial #MachineLearningModels #ModelOptimization #ModelComparison #ModelMetrics #ModelValidation #Overfitting #Underfitting
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