Pickl AI

JANUARY 29, 2024

ML focuses on enabling computers to learn from data and improve performance over time without explicit programming. Key Components In Data Science, key components include data cleaning, Exploratory Data Analysis, and model building using statistical techniques. billion in 2022 to a remarkable USD 484.17

Data Science 52

Data Science ML ML Machine Learning 52

Mlearning.ai

FEBRUARY 2, 2023

Common causes of data leakage include using test data in the training process, using data from future time points, and using data that is not connected to the problem at hand. Data Leakage — Not using the appropriate test set  — Test set measures the generality of the model.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

JULY 12, 2024

Scikit-learn: A simple and efficient tool for data mining and data analysis, particularly for building and evaluating machine learning models. Data Normalization and Standardization: Scaling numerical data to a standard range to ensure fairness in model training.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

JULY 10, 2023

As a programming language it provides objects, operators and functions allowing you to explore, model and visualise data. The programming language can handle Big Data and perform effective data analysis and statistical modelling. R’s workflow support enhances productivity and collaboration among data scientists.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Data Cleaning: Raw data often contains errors, inconsistencies, and missing values. Data cleaning identifies and addresses these issues to ensure data quality and integrity. Data Visualisation: Effective communication of insights is crucial in Data Science.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 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 91

Deep Learning Deep Learning Algorithm Decision Trees 91

Pickl AI

FEBRUARY 12, 2024

Statistical Concepts A strong understanding of statistical concepts, including probability, hypothesis testing, regression analysis, and experimental design, is paramount in Data Science roles. However, there are a few fundamental principles that remain the same throughout. Here is a brief description of the same.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Towards AI

JULY 19, 2023

That post was dedicated to an exploratory data analysis while this post is geared towards building prediction models. Feel free to try other algorithms such as Random Forests, Decision Trees, Neural Networks, etc., among supervised models and k-nearest neighbors, DBSCAN, etc., among unsupervised models.

Machine Learning 40

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 40

PyImageSearch

DECEMBER 12, 2022

We went through the core essentials required to understand XGBoost, namely decision trees and ensemble learners. Since we have been dealing with trees, we will assume that our adaptive boosting technique is being applied to decision trees. For now, since we have 7 data samples, we will assign 1/7 to each sample.

Decision Trees 52

Decision Trees Deep Learning Deep Learning Exploratory Data Analysis 52

Heartbeat

SEPTEMBER 28, 2023

In a typical MLOps project, similar scheduling is essential to handle new data and track model performance continuously. Load and Explore Data We load the Telco Customer Churn dataset and perform exploratory data analysis (EDA). Random Forest Classifier (rf): Ensemble method combining multiple decision trees.

Support Vector Machines 52

Support Vector Machines Machine Learning Machine Learning ML 52

DagsHub

SEPTEMBER 16, 2024

LIME can help improve model transparency, build trust, and ensure that models make fair and unbiased decisions by identifying the key features that are more relevant in prediction-making. LIME provides explanations for individual predictions by approximating the model locally with an interpretable model like a decision tree.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Heartbeat

MAY 29, 2023

It is therefore important to carefully plan and execute data preparation tasks to ensure the best possible performance of the machine learning model. It is also essential to evaluate the quality of the dataset by conducting exploratory data analysis (EDA), which involves analyzing the dataset’s distribution, frequency, and diversity of text.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

Mlearning.ai

JANUARY 29, 2024

Data Science Project — Build a Decision Tree Model with Healthcare Data Using Decision Trees to Categorize Adverse Drug Reactions from Mild to Severe Photo by Maksim Goncharenok Decision trees are a powerful and popular machine learning technique for classification tasks.

Decision Trees 52

Decision Trees Data Science Exploratory Data Analysis Data Analysis 52