AWS Machine Learning Blog

MAY 30, 2023

For ML model development, the size of a SageMaker notebook instance depends on the amount of data you need to load in-memory for meaningful exploratory data analyses (EDA) and the amount of computation required. She has worked with a wide range of organizations on various deep learning use cases involving NLP and computer vision.

AWS 98

AWS ML ML EDA 98

Pickl AI

JULY 25, 2023

Exploratory Data Analysis (EDA) EDA is a crucial step where Data Scientists visually explore and analyze the data to identify patterns, trends, and potential correlations. Model Development Data Scientists develop sophisticated machine-learning models to derive valuable insights and predictions from the data.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Heartbeat

SEPTEMBER 28, 2023

Load and Explore Data We load the Telco Customer Churn dataset and perform exploratory data analysis (EDA). EDA is essential for gaining insights into the dataset’s characteristics and identifying any data preprocessing requirements. Are there clusters of customers with different spending patterns? #3.

Support Vector Machines 52

Support Vector Machines Machine Learning Machine Learning ML 52

Pickl AI

FEBRUARY 12, 2024

Unsupervised learning algorithms, on the other hand, operate on unlabeled data and identify patterns and relationships without explicit supervision. Clustering algorithms such as K-means and hierarchical clustering are examples of unsupervised learning techniques. How do you handle missing values in a dataset?

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Heartbeat

JANUARY 25, 2023

Then they use these patterns to understand the public’s behavior and predict the election results, thus making more informed political strategies based on population clusters. The model-building process involves Natural Language Processing, Deep Learning techniques, and Python libraries. .

Python 52

Python Deep Learning Deep Learning ML 52

Pickl AI

SEPTEMBER 12, 2024

Boosting: An ensemble learning technique that combines multiple weak models to create a strong predictive model. C Classification: A supervised Machine Learning task that assigns data points to predefined categories or classes based on their characteristics.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

JULY 20, 2023

Kaggle datasets) and use Python’s Pandas library to perform data cleaning, data wrangling, and exploratory data analysis (EDA). Image Recognition with Deep Learning: Use Python with TensorFlow or PyTorch to build an image recognition model (e.g., Analyzing Large Datasets: Choose a large dataset from public sources (e.g.,

Analytics 40

Analytics Analytics Big Data Big Data 40

DrivenData Labs

DECEMBER 7, 2023

Solvers submitted a wide range of methodologies to this end, including using open-source and third party LLMs (GPT, LLaMA), clustering (DBSCAN, K-Means), dimensionality reduction (PCA), topic modeling (LDA, BERT), sentence transformers, semantic search, named entity recognition, and more. and DistilBERT.

Natural Language Processing 130

Natural Language Processing Clustering Data Science Data Analysis 130