Towards AI

AUGUST 16, 2023

In fact, AI/ML graduate textbooks do not provide a clear and consistent description of the AI software engineering process. Therefore, I thought it would be helpful to give a complete description of the AI engineering process or AI Process, which is described in most AI/ML textbooks [5][6]. 85% or more of AI projects fail [1][2].

AI 98

AI AI Machine Learning Machine Learning 98

Towards AI

JULY 19, 2023

Please refer to Part 1– to understand what is Sales Prediction/Forecasting, the Basic concepts of Time series modeling, and EDA I’m working on Part 3 where I will be implementing Deep Learning and Part 4 where I will be implementing a supervised ML model.

Cross Validation 98

Cross Validation Clustering EDA Data Preparation 98

Ocean Protocol

SEPTEMBER 29, 2023

AI / ML offers tools to give a competitive edge in predictive analytics, business intelligence, and performance metrics. By leveraging cross-validation, we ensured the model’s assessment wasn’t reliant on a singular data split.

Cross Validation 59

Cross Validation Predictive Analytics Exploratory Data Analysis EDA 59

Pickl AI

JANUARY 3, 2024

The growing application of Machine Learning also draws interest towards its subsets that add power to ML models. Key takeaways Feature engineering transforms raw data for ML, enhancing model performance and significance. 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

Mlearning.ai

NOVEMBER 23, 2023

But deep down, we know we could achieve better results with a different approach, after all in ML, there’s no one-size-fits-all solution. You may need to import more libraries for EDA, preprocessing, and so on depending on the dataset you’re dealing with. Cross-Validation: Perform cross-validation to ensure the models generalize well.

Supervised Learning 52

Supervised Learning Cross Validation EDA Machine Learning 52

Ocean Protocol

FEBRUARY 1, 2024

Challenge Overview Objective : Building upon the insights gained from Exploratory Data Analysis (EDA), participants in this data science competition will venture into hands-on, real-world artificial intelligence (AI) & machine learning (ML). You can download the dataset directly through Desights.

Exploratory Data Analysis 52

Exploratory Data Analysis Data Science Cross Validation Machine Learning 52

DrivenData Labs

APRIL 10, 2024

Michal Wierzbinski ¶ Place: 2nd Place Prize: $3,000 Hometown: Rabka-Zdroj (near the city of Cracow), Poland Username: xultaeculcis Social Media: GitHub , LinkedIn Background: ML Engineer specializing in building Deep Learning solutions for Geospatial industry in a cloud native fashion. What motivated you to compete in this challenge?

Deep Learning 130

Deep Learning Deep Learning Machine Learning Machine Learning 130