Pickl AI

JANUARY 3, 2024

Let’s delve into the intricacies of Feature Engineering and discover its pivotal role in the realm of artificial intelligence. EDA, imputation, encoding, scaling, extraction, outlier handling, and cross-validation ensure robust models.

Machine Learning 52

Machine Learning Machine Learning Exploratory Data Analysis Cross Validation 52

Pickl AI

NOVEMBER 18, 2024

Basics of Machine Learning Machine Learning is a subset of Artificial Intelligence (AI) that allows systems to learn from data, improve from experience, and make predictions or decisions without being explicitly programmed. Decision trees are easy to interpret but prone to overfitting.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

PyImageSearch

JANUARY 23, 2023

The reasoning behind that is simple; whatever we have learned till now, be it adaptive boosting, decision trees, or gradient boosting, have very distinct statistical foundations which require you to get your hands dirty with the math behind them. , you already know that our approach in this series is math-heavy instead of code-heavy.

Deep Learning 96

Deep Learning Deep Learning Algorithm Decision Trees 96

Mlearning.ai

MAY 23, 2023

Decision trees are more prone to overfitting. Some algorithms that have low bias are Decision Trees, SVM, etc. Hence, we have various classification algorithms in machine learning like logistic regression, support vector machine, decision trees, Naive Bayes classifier, etc. character) is underlined or not.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Pickl AI

DECEMBER 12, 2024

Introduction Boosting is a powerful Machine Learning ensemble technique that combines multiple weak learners, typically decision trees, to form a strong predictive model. Lets explore the mathematical foundation, unique enhancements, and tree-pruning strategies that make XGBoost a standout algorithm. Lower values (e.g.,

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Pickl AI

MAY 24, 2023

Its modified feature includes the cross-validation that allowing it to use more than one metric. It was mostly developed by Facebook’s artificial intelligence research lab, and it serves as the basis for Uber’s “Pyro” technology for probability programming.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

Pickl AI

AUGUST 9, 2024

Key topics include: Supervised Learning Understanding algorithms such as linear regression, decision trees, and support vector machines, and their applications in Big Data. Model Evaluation Techniques for evaluating machine learning models, including cross-validation, confusion matrix, and performance metrics.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

NOVEMBER 2, 2023

linear regression, decision trees , SVM) – Understanding about the perfect fit for using each algorithm – Parameters and hyperparameters to tune Click here to access -> Cheat sheet for Key Machine Learning Algorithms Deep Learning Concepts and Neural Network Architectures – Neural network components and their functions (e.g.,

Data Scientist 52

Data Scientist Data Science Data Visualization Machine Learning 52

Pickl AI

APRIL 26, 2024

Machine learning is a subset of artificial intelligence that enables computers to learn from data and improve over time without being explicitly programmed. Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. What are the advantages and disadvantages of decision trees ?

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Heartbeat

MAY 29, 2023

The weak models can be trained using techniques such as decision trees or neural networks, and the outputs are combined using techniques such as weighted averaging or gradient boosting. Use a representative and diverse validation dataset to ensure that the model is not overfitting to the training data.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78