Mlearning.ai

NOVEMBER 30, 2023

Mastering Tree-Based Models in Machine Learning: A Practical Guide to Decision Trees, Random Forests, and GBMs Image created by the author on Canva Ever wondered how machines make complex decisions? Just like a tree branches out, tree-based models in machine learning do something similar.

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ODSC - Open Data Science

JANUARY 7, 2025

Heres what we noticed from analyzing this data, highlighting whats remained the same over the years, and what additions help make the modern data scientist in2025. Data Science Of course, a data scientist should know data science! Joking aside, this does infer particular skills.

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IBM Journey to AI blog

DECEMBER 20, 2023

Supervised machine learning Supervised machine learning is a type of machine learning where the model is trained on a labeled dataset (i.e., Classification algorithms include logistic regression, k-nearest neighbors and support vector machines (SVMs), among others. the target or outcome variable is known).

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Pickl AI

SEPTEMBER 16, 2024

Summary: This blog highlights ten crucial Machine Learning algorithms to know in 2024, including linear regression, decision trees, and reinforcement learning. Introduction Machine Learning (ML) has rapidly evolved over the past few years, becoming an integral part of various industries, from healthcare to finance.

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Mlearning.ai

MARCH 13, 2023

Photo by Andy Kelly on Unsplash Choosing a machine learning (ML) or deep learning (DL) algorithm for application is one of the major issues for artificial intelligence (AI) engineers and also data scientists. Experiment and evaluate: Implement the algorithms you have selected and evaluate their performance on your data.

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Pickl AI

MAY 10, 2023

K-Nearest Neighbours (kNN) In order to calculate the distance between one data point and every other accomplished parameter through using the metrics of distance like Euclidean distance, Manhattan distance and others. Decision Trees Decision Trees are non-linear model unlike the logistic regression which is a linear model.

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Pickl AI

DECEMBER 9, 2024

Summary: Inductive bias in Machine Learning refers to the assumptions guiding models in generalising from limited data. By managing inductive bias effectively, data scientists can improve predictions, ensuring models are robust and well-suited for real-world applications.

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Pickl AI

JANUARY 29, 2024

Key Components In Data Science, key components include data cleaning, Exploratory Data Analysis, and model building using statistical techniques. ML focuses on algorithms like decision trees, neural networks, and support vector machines for pattern recognition. over the specified period.

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Pickl AI

MAY 31, 2023

Explore Machine Learning with Python: Become familiar with prominent Python artificial intelligence libraries such as sci-kit-learn and TensorFlow. Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and support vector machines.

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IBM Journey to AI blog

DECEMBER 19, 2023

These powerful tools can find patterns from input data and make assumptions about what data is perceived as normal. These techniques can go a long way in discovering unknown anomalies and reducing the work of manually sifting through large data sets.

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IBM Journey to AI blog

JULY 6, 2023

It processes enormous amounts of data a human wouldn’t be able to work through in a lifetime and evolves as more data is processed. Challenges of data science Across most companies, finding, cleaning and preparing the proper data for analysis can take up to 80% of a data scientist’s day.

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Heartbeat

SEPTEMBER 28, 2023

Hands-on Project Why customer churn matters and how to predict it with machine learning, explained step-by-step Photo by Gabrielle Ribeiro on Unsplash Introduction In today’s competitive business environment, retaining customers is essential to a company’s success. Captures complex relationships in data.

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Pickl AI

JULY 12, 2024

Decision Trees Decision trees recursively partition data into subsets based on the most significant attribute values. Python’s Scikit-learn provides easy-to-use interfaces for constructing decision tree classifiers and regressors, enabling intuitive model visualisation and interpretation.

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Pickl AI

FEBRUARY 12, 2024

Data Science interviews are pivotal moments in the career trajectory of any aspiring data scientist. Having the knowledge about the data science interview questions will help you crack the interview. Supervised learning algorithms learn from labelled data, where each input is associated with a corresponding output label.

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ODSC - Open Data Science

JANUARY 5, 2024

Scikit-learn A machine learning powerhouse, Scikit-learn provides a vast collection of algorithms and tools, making it a go-to library for many data scientists. It is easy to use, with a well-documented API and a wide range of tutorials and examples available.

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Heartbeat

APRIL 17, 2023

NRE is a complex task that involves multiple steps and requires sophisticated machine learning algorithms like Hidden Markov Models (HMMs) , Conditional Random Fields (CRFs), and Support Vector Machines (SVMs) be present. We’re committed to supporting and inspiring developers and engineers from all walks of life.

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Pickl AI

SEPTEMBER 12, 2024

Data Science is the art and science of extracting valuable information from data. It encompasses data collection, cleaning, analysis, and interpretation to uncover patterns, trends, and insights that can drive decision-making and innovation.

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Mlearning.ai

MAY 23, 2023

Hey guys, in this blog we will see some of the most asked Data Science Interview Questions by interviewers in [year]. Data science has become an integral part of many industries, and as a result, the demand for skilled data scientists is soaring. Overfitting: The model performs well only for the sample training data.

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Pickl AI

NOVEMBER 18, 2024

Selecting an Algorithm Choosing the correct Machine Learning algorithm is vital to the success of your model. For example, linear regression is typically used to predict continuous variables, while decision trees are great for classification and regression tasks. Decision trees are easy to interpret but prone to overfitting.

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Pickl AI

JULY 14, 2023

Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Techniques such as decision trees, support vector machines, and neural networks gained popularity.

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Pickl AI

JULY 26, 2023

The concepts of bias and variance in Machine Learning are two crucial aspects in the realm of statistical modelling and machine learning. Understanding these concepts is paramount for any data scientist, machine learning engineer, or researcher striving to build robust and accurate models.

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Heartbeat

FEBRUARY 22, 2023

Background Information Decision trees, random forests, and linear regression are just a few examples of classic machine-learning models that have been used extensively in business for years. The n_estimators argument is set to 100, meaning that 100 decision trees will be used in the forest.

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Pickl AI

AUGUST 9, 2024

Students should learn how to leverage Machine Learning algorithms to extract insights from large datasets. Key topics include: Supervised Learning Understanding algorithms such as linear regression, decision trees, and support vector machines, and their applications in Big Data.

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Heartbeat

OCTOBER 11, 2023

Model Complexity Machine Learning : Traditional machine learning models have fewer parameters and a simpler structure than deep learning models. They typically rely on simpler algorithms like decision trees, support vector machines, or linear regression.

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DagsHub

APRIL 21, 2024

Although MLOps is an abbreviation for ML and operations, don’t let it confuse you as it can allow collaborations among data scientists, DevOps engineers, and IT teams. Model Training Frameworks This stage involves the process of creating and optimizing the predictive models with labeled and unlabeled data.

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Pickl AI

JANUARY 8, 2025

RFE works effectively with algorithms like Support Vector Machines (SVMs) and linear regression. Tree-Based Methods Decision trees and ensemble methods like Random Forest and Gradient Boosting inherently perform feature selection. For tree-based models, importance scores are derived from decision splits.

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Pickl AI

SEPTEMBER 30, 2024

They process data, identify patterns, and adjust the model accordingly. Common algorithms include decision trees, neural networks, and support vector machines. Data : Data serves as the foundation for ML.

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Pickl AI

NOVEMBER 28, 2024

Decision Trees These trees split data into branches based on feature values, providing clear decision rules. Support Vector Machines (SVM) SVMs are powerful classifiers that separate data into distinct categories by finding an optimal hyperplane.

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Pickl AI

SEPTEMBER 23, 2024

Hypothesis testing and regression analysis are crucial for making predictions and understanding data relationships. Machine Learning Supervised Learning includes algorithms like linear regression, decision trees, and support vector machines.

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Dataconomy

SEPTEMBER 1, 2023

Hybrid machine learning techniques excel in model selection by amalgamating the strengths of multiple models. By combining, for example, a decision tree with a support vector machine (SVM), these hybrid models leverage the interpretability of decision trees and the robustness of SVMs to yield superior predictions in medicine.

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Dataconomy

MARCH 28, 2025

Decision trees: They segment data into branches based on sequential questioning. Unsupervised algorithms In contrast, unsupervised algorithms analyze data without pre-existing labels, identifying inherent structures and patterns. Random forest: Combines multiple decision trees to strengthen predictive capabilities.

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How to Learn Machine Learning

MARCH 25, 2025

Some common supervised learning algorithms include decision trees, random forests, support vector machines, and linear regression. These algorithms help businesses make decisions when there is clear historical data available. Preparing labeled data takes time and effort.

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