#60daysofmachinelearning search results

#Day24 and #Day25 of #60DaysOfMachineLearning Learned about gradient descent types, Learning rate, batch , epochs. Learned about SVM, ensemble methods. #MachineLearning #DailyMLChallenge

#Day23 of #60DaysOfMachineLearning Learned about Eigenvalues & Eigenvectors Understood how PCA works. Use PCA in scikit learn and from scratch #MachineLearning #DailyMLChallenge

#Day22 of #60daysofMachineLearning -Learned about steps in creating an Artificial Neural Network (ANN) using PyTorch. -Understood designing a neural network (architecture, activation functions, overfitting, optimization). #MachineLearning #DailyMLChallenge

#Day21 of #60daysofMachineLearning -Studied neural networks -Studied pytorch documents -Build a neural network from examples. #MachineLearning #DailyMLChallenge

#Day20 of #60daysofMachineLearning -Python Exercises -Updated my portfolio. -Added README in my github projects. #MachineLearning #DailyMLChallenge

#Day19 of #60daysofMachineLearning Handling missing data, datetime, mixed variables, binarization and binning #MachineLearning #DailyMLChallenge

#Day18 of #60daysofMachineLearning Encoding Pipelines Column Transformer Mathematical transformers #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

#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

#Day13 and #Day14 of #60daysofMachineLearning Learned about perceptron Support Vector Machines intuition Kernel trick Confusion matrix #MachineLearning #DailyMLChallenge

#Day12 of #60daysofMachineLearning -Learned about KNN models. -Apply machine learning pipeline on "Online Shopper Intention" Dataset #MachineLearning #DailyMLChallenge

#Day11 of #60daysofMachineLearning Learned about -Different encoding techniques -Imputation -Pipelines #MachineLearning #DailyMLChallenge

#Day10 of #60daysofMachineLearning -Random forest classifier and regression -Intermediate Machine Learning(Kaggle) #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 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 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

Great progress, Dan! Deep learning’s ability to uncover complex patterns is what makes it so powerful in fields like image recognition and natural language processing. Excited to see where you go in the final stretch of #60DaysOfMachineLearning! 💪

#Day18 of #60daysofMachineLearning Encoding Pipelines Column Transformer Mathematical transformers #MachineLearning #DailyMLChallenge

Day 45 of #60daysOfMachineLearning 🔷 K-Nearest Neighbor 🔷 KNN is a popular and simple machine learning algorithm for classification and regression tasks.

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

#Day12 of #60daysofMachineLearning -Learned about KNN models. -Apply machine learning pipeline on "Online Shopper Intention" Dataset #MachineLearning #DailyMLChallenge

#Day10 of #60daysofMachineLearning -Random forest classifier and regression -Intermediate Machine Learning(Kaggle) #MachineLearning #DailyMLChallenge

#Day19 of #60daysofMachineLearning Handling missing data, datetime, mixed variables, binarization and binning #MachineLearning #DailyMLChallenge

#Day21 of #60daysofMachineLearning -Studied neural networks -Studied pytorch documents -Build a neural network from examples. #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

#Day11 of #60daysofMachineLearning Learned about -Different encoding techniques -Imputation -Pipelines #MachineLearning #DailyMLChallenge

#Day13 and #Day14 of #60daysofMachineLearning Learned about perceptron Support Vector Machines intuition Kernel trick Confusion matrix #MachineLearning #DailyMLChallenge

#Day16 of #60daysofMachineLearning Refreshed Python Intermediate I used online compiler. No screenshots taken. #MachineLearning #DailyMLChallenge

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.

#Day9 of #60daysofMachineLearning Learned about Decision Trees Practiced building a Decision Tree Looked into GridSearchCV Checked pruning in DT #MachineLearning #DailyMLChallenge

#Day17 of #60daysofMachineLearning -Univariate and Bivariate -Pandas profiling -Standardization and normalization techniques #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.

#Day23 of #60DaysOfMachineLearning Learned about Eigenvalues & Eigenvectors Understood how PCA works. Use PCA in scikit learn and from scratch #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

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 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).