DrivenData Labs

APRIL 10, 2024

Combining deep and practical understanding of technology, computer vision and AI with experience in big data architectures. A data geek by heart. What motivated you to compete in this challenge?

Deep Learning 130

Deep Learning Deep Learning Machine Learning Machine Learning 130

Heartbeat

OCTOBER 23, 2023

Image from "Big Data Analytics Methods" by Peter Ghavami Here are some critical contributions of data scientists and machine learning engineers in health informatics: Data Analysis and Visualization: Data scientists and machine learning engineers are skilled in analyzing large, complex healthcare datasets.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Big Data Analytics 52

AWS Machine Learning Blog

FEBRUARY 10, 2023

Feature engineering Game tracking data is captured at 10 frames per second, including the player location, speed, acceleration, and orientation. and Big Data Bowl Kaggle Zoo solution ( Gordeev et al. ). Our feature engineering constructs sequences of play features as the input for model digestion.

ML 84

ML ML Machine Learning Machine Learning 84

Pickl AI

SEPTEMBER 17, 2024

Read More: Big Data and Artificial Intelligence: How They Work Together? The goal in Machine Learning is to find a balance between bias and variance by choosing an appropriate model complexity and using techniques such as regularisation and cross-validation. What Is the Role of Explainable AI (XAI) In Machine Learning?

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Machine Learning Machine Learning 52

Pickl AI

FEBRUARY 22, 2024

MicroMasters Program in Statistics and Data Science MIT – edX 1 year 2 months (INR 1,11,739) This program integrates Data Science, Statistics, and Machine Learning basics. It emphasises probabilistic modeling and Statistical inference for analysing big data and extracting information.

Data Science 52

Data Science Hypothesis Testing Data Analysis Data Analysis 52

Pickl AI

DECEMBER 12, 2024

This scalability ensures that the algorithm remains reliable whether youre working on a single machine or a large-scale distributed system, making it suitable for real-world big data applications. Its design and implementation make it a go-to choice for beginners and seasoned Data Scientists.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Pickl AI

SEPTEMBER 12, 2024

B Big Data : Large datasets characterised by high volume, velocity, variety, and veracity, requiring specialised techniques and technologies for analysis. Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Mlearning.ai

MAY 23, 2023

This data can be used to pass as an input to the neural network maintaining a small batch size. The steps for SVM are given below: For SVM, small data sets can be obtained. This can be done by dividing the big data set. The subset of the data set can be obtained as an input if using the partial fit function.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 19, 2024

Consider incorporating techniques like cross-validation to assess the model’s generalisation ability. Read More: Big Data and Artificial Intelligence: How They Work Together? Solution: To prevent overfitting, balance your prompt tuning with various examples and scenarios.

AI 52

AI AI Machine Learning Machine Learning 52

Mlearning.ai

NOVEMBER 29, 2023

Modeling Stage Forecasting models evaluation — based on all the preliminary research and prep data, different forecasting models are tested and evaluated to pick the most efficient one(s). Testing Stage Forecasting models run on testing data with known results — a step necessary for making sure the picked algorithms do their work properly.

Machine Learning 52

Machine Learning Machine Learning ML ML 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

AWS Machine Learning Blog

JANUARY 26, 2023

The data science team must sometimes work with limited training data in the order of tens of thousands of records given the nature of their use cases. To reduce variance, Best Egg uses k-fold cross validation as part of their custom container to evaluate the trained model.

ML 94

ML ML Data Scientist AWS 94