Pickl AI

DECEMBER 16, 2024

This step includes: Identifying Data Sources: Determine where data will be sourced from (e.g., Ensuring Time Consistency: Ensure that the data is organized chronologically, as time order is crucial for time series analysis. databases, APIs, CSV files).

AI 52

AI AI Machine Learning Machine Learning 52

Pickl AI

AUGUST 9, 2024

Data Cleaning and Transformation Techniques for preprocessing data to ensure quality and consistency, including handling missing values, outliers, and data type conversions. Students should learn about data wrangling and the importance of data quality.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Heartbeat

MAY 29, 2023

Hyperparameters are the configuration variables of a machine learning algorithm that are set prior to training, such as learning rate, number of hidden layers, number of neurons per layer, regularization parameter, and batch size, among others.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

DataRobot Blog

DECEMBER 20, 2022

You can understand the data and model’s behavior at any time. Once you use a training dataset, and after the Exploratory Data Analysis, DataRobot flags any data quality issues and, if significant issues are spotlighted, will automatically handle them in the modeling stage. Rapid Modeling with DataRobot AutoML.

AI 59

AI AI Cross Validation Machine Learning 59

Mlearning.ai

NOVEMBER 29, 2023

Scientific studies forecasting  — Machine Learning and deep learning for time series forecasting accelerate the rates of polishing up and introducing scientific innovations dramatically. 19 Time Series Forecasting Machine Learning Methods How exactly does time series forecasting machine learning work in practice?

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

SEPTEMBER 12, 2024

Key Components of Data Science Data Science consists of several key components that work together to extract meaningful insights from data: Data Collection: This involves gathering relevant data from various sources, such as databases, APIs, and web scraping.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

APRIL 26, 2024

Overfitting occurs when a model learns the training data too well, including noise and irrelevant patterns, leading to poor performance on unseen data. Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. In my previous role, we had a project with a tight deadline.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

The MLOps Blog

APRIL 20, 2023

Regularization techniques: experiment with weight decay, dropout, and data augmentation to improve model generalization. These techniques can help prevent overfitting and improve the model’s performance on the validation set.

ML 64

ML ML Data Quality Cross Validation 64