IBM Journey to AI blog

FEBRUARY 23, 2024

Predictive analytics integrates with NLP, ML and DL to enhance decision-making capabilities, extract insights, and use historical data to forecast future behavior, preferences and trends. ML and DL lie at the core of predictive analytics, enabling models to learn from data, identify patterns and make predictions about future events.

AI 116

AI AI ML ML 116

Pickl AI

SEPTEMBER 12, 2024

Data cleaning identifies and addresses these issues to ensure data quality and integrity. Data Analysis: This step involves applying statistical and Machine Learning techniques to analyse the cleaned data and uncover patterns, trends, and relationships.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Mlearning.ai

MAY 23, 2023

The following figure represents the life cycle of data science. It starts with gathering the business requirements and relevant data. Once the data is acquired, it is maintained by performing data cleaning, data warehousing, data staging, and data architecture. character) is underlined or not.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Pickl AI

OCTOBER 18, 2023

It defines roles, responsibilities, and processes for data management. 6 Elements of Data Quality Accuracy Data accuracy measures how well the data reflects the real-world entities or events it represents. Accurate data is free from errors, inconsistencies, or discrepancies.

Data Quality 52

Data Quality ML ML Machine Learning 52

Pickl AI

APRIL 3, 2025

Collect Data: Gather customer demographics, purchase history, website interaction logs, customer support tickets, and subscription status. Clean Data: Handle missing addresses, standardize purchase dates, remove test accounts. EDA: Calculate overall churn rate.

Data Analysis 52

Data Analysis Data Analysis Data Visualization EDA 52

Heartbeat

MAY 29, 2023

This step involves several tasks, including data cleaning, feature selection, feature engineering, and data normalization. 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.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78