#60daysofmachinelearning resultados de búsqueda
Day 59 of #60daysOfMachineLearning 🔷 Percision, Recall, F1 🔷 Precision is a measure of the accuracy of the model's positive predictions. It is calculated as the number of true positive predictions divided by the total number of positive predictions made by the model.
Day 52 of #60daysOfMachineLearning 🔷 Deep Learning 🔷 Deep learning is a type of machine learning algorithm that uses deep neural networks to learn complex patterns and relationships in data.
Day 51 of #60daysOfMachineLearning 🔷 Ensemble Learning 🔷 Ensemble learning is a machine learning technique that combines multiple models to improve the performance and robustness of the final model.
#Day11 of #60daysofMachineLearning Learned about -Different encoding techniques -Imputation -Pipelines #MachineLearning #DailyMLChallenge
Day 47 of #60daysOfMachineLearning 🔷 K-Means Clustering 🔷 K-means clustering is a popular and simple unsupervised learning algorithm for clustering data into groups.
Day 58 of #60daysOfMachineLearning 🔷 Accuracy, Overfitting, Underfitting 🔷 In machine learning, accuracy is a measure of how well the model is able to make predictions on new examples. It is usually measured as the percentage of correct predictions made by the model.
#Day18 of #60daysofMachineLearning Encoding Pipelines Column Transformer Mathematical transformers #MachineLearning #DailyMLChallenge
#Day19 of #60daysofMachineLearning Handling missing data, datetime, mixed variables, binarization and binning #MachineLearning #DailyMLChallenge
#Day17 of #60daysofMachineLearning -Univariate and Bivariate -Pandas profiling -Standardization and normalization techniques #MachineLearning #DailyMLChallenge
#Day16 of #60daysofMachineLearning Refreshed Python Intermediate I used online compiler. No screenshots taken. #MachineLearning #DailyMLChallenge
#Day10 of #60daysofMachineLearning -Random forest classifier and regression -Intermediate Machine Learning(Kaggle) #MachineLearning #DailyMLChallenge
#Day21 of #60daysofMachineLearning -Studied neural networks -Studied pytorch documents -Build a neural network from examples. #MachineLearning #DailyMLChallenge
#Day12 of #60daysofMachineLearning -Learned about KNN models. -Apply machine learning pipeline on "Online Shopper Intention" Dataset #MachineLearning #DailyMLChallenge
Day 55 of #60daysOfMachineLearning 🔷 Convolutional Neural Networks 🔷 Convolutional Neural Networks (CNNs) are a type of artificial neural network that is specifically designed to process data with a grid-like topology, such as images.
#Day23 of #60DaysOfMachineLearning Learned about Eigenvalues & Eigenvectors Understood how PCA works. Use PCA in scikit learn and from scratch #MachineLearning #DailyMLChallenge
#Day9 of #60daysofMachineLearning Learned about Decision Trees Practiced building a Decision Tree Looked into GridSearchCV Checked pruning in DT #MachineLearning #DailyMLChallenge
#Day13 and #Day14 of #60daysofMachineLearning Learned about perceptron Support Vector Machines intuition Kernel trick Confusion matrix #MachineLearning #DailyMLChallenge
Day 8 of #60DaysOfMachineLearning Topic: Advanced Regression & Handling Imbalanced Data Used models like Decision Trees, Random Forests, and SVR Datasets: Magic Gamma Telescope and Cars dataset. #MachineLearning #DailyMLChallenge
Day 6 of #60DaysOfMachineLearning Explored Regularization in Linear Regression using the Diabetes dataset.Ridge (L2),Lasso (L1),ElasticNet for handling overfitting. #MachineLearning #LearnInPublic #DataScience #AI #MLAlgorithms #Python #DailyMLChallenge
#Day15 of #60daysofMachineLearning Learned about neural network. ALso did dog vs cat classification. I got quite different result than the source though may be because data was not same #MachineLearning #DailyMLChallenge
#Day18 of #60daysofMachineLearning Encoding Pipelines Column Transformer Mathematical transformers #MachineLearning #DailyMLChallenge
Day 6 of #60DaysOfMachineLearning Explored Regularization in Linear Regression using the Diabetes dataset.Ridge (L2),Lasso (L1),ElasticNet for handling overfitting. #MachineLearning #LearnInPublic #DataScience #AI #MLAlgorithms #Python #DailyMLChallenge
#Day19 of #60daysofMachineLearning Handling missing data, datetime, mixed variables, binarization and binning #MachineLearning #DailyMLChallenge
#Day16 of #60daysofMachineLearning Refreshed Python Intermediate I used online compiler. No screenshots taken. #MachineLearning #DailyMLChallenge
#Day10 of #60daysofMachineLearning -Random forest classifier and regression -Intermediate Machine Learning(Kaggle) #MachineLearning #DailyMLChallenge
#Day13 and #Day14 of #60daysofMachineLearning Learned about perceptron Support Vector Machines intuition Kernel trick Confusion matrix #MachineLearning #DailyMLChallenge
#Day21 of #60daysofMachineLearning -Studied neural networks -Studied pytorch documents -Build a neural network from examples. #MachineLearning #DailyMLChallenge
#Day11 of #60daysofMachineLearning Learned about -Different encoding techniques -Imputation -Pipelines #MachineLearning #DailyMLChallenge
#Day15 of #60daysofMachineLearning Learned about neural network. ALso did dog vs cat classification. I got quite different result than the source though may be because data was not same #MachineLearning #DailyMLChallenge
#Day12 of #60daysofMachineLearning -Learned about KNN models. -Apply machine learning pipeline on "Online Shopper Intention" Dataset #MachineLearning #DailyMLChallenge
#Day9 of #60daysofMachineLearning Learned about Decision Trees Practiced building a Decision Tree Looked into GridSearchCV Checked pruning in DT #MachineLearning #DailyMLChallenge
#Day23 of #60DaysOfMachineLearning Learned about Eigenvalues & Eigenvectors Understood how PCA works. Use PCA in scikit learn and from scratch #MachineLearning #DailyMLChallenge
Day 7 of #60DaysOfMachineLearning Topic: K-Fold Cross-Validation Purpose: Evaluate model performance reliably How: Split data into multiple folds, train & test on each Dataset: Iris #MachineLearning #DataScience #AI #Python #MLAlgorithms #DailyMLChallenge
Day 8 of #60DaysOfMachineLearning Topic: Advanced Regression & Handling Imbalanced Data Used models like Decision Trees, Random Forests, and SVR Datasets: Magic Gamma Telescope and Cars dataset. #MachineLearning #DailyMLChallenge
Day 59 of #60daysOfMachineLearning 🔷 Percision, Recall, F1 🔷 Precision is a measure of the accuracy of the model's positive predictions. It is calculated as the number of true positive predictions divided by the total number of positive predictions made by the model.
#Day17 of #60daysofMachineLearning -Univariate and Bivariate -Pandas profiling -Standardization and normalization techniques #MachineLearning #DailyMLChallenge
Day 51 of #60daysOfMachineLearning 🔷 Ensemble Learning 🔷 Ensemble learning is a machine learning technique that combines multiple models to improve the performance and robustness of the final model.
Day 55 of #60daysOfMachineLearning 🔷 Convolutional Neural Networks 🔷 Convolutional Neural Networks (CNNs) are a type of artificial neural network that is specifically designed to process data with a grid-like topology, such as images.
Day 47 of #60daysOfMachineLearning 🔷 K-Means Clustering 🔷 K-means clustering is a popular and simple unsupervised learning algorithm for clustering data into groups.
Day 50 of #60daysOfMachineLearning 🔷 Q-Learning 🔷 Q-learning is a popular and effective reinforcement learning algorithm for solving Markov Decision Processes (MDPs).
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