#60daysofmachinelearning résultats de recherche
#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
#Day13 and #Day14 of #60daysofMachineLearning Learned about perceptron Support Vector Machines intuition Kernel trick Confusion matrix #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.
#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
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
#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
#Day12 of #60daysofMachineLearning -Learned about KNN models. -Apply machine learning pipeline on "Online Shopper Intention" Dataset #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
#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
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 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 53 of #60daysOfMachineLearning 🔷 Neural Networks 🔷 A neural network is a computational model that is inspired by the structure and function of the brain. 🧵 👇
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 50 of #60daysOfMachineLearning 🔷 Q-Learning 🔷 Q-learning is a popular and effective reinforcement learning algorithm for solving Markov Decision Processes (MDPs).
#Day17 of #60daysofMachineLearning -Univariate and Bivariate -Pandas profiling -Standardization and normalization techniques #MachineLearning #DailyMLChallenge
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