ODSC - Open Data Science

AUGUST 15, 2023

Build Classification and Regression Models with Spark on AWS Suman Debnath | Principal Developer Advocate, Data Engineering | Amazon Web Services This immersive session will cover optimizing PySpark and best practices for Spark MLlib. Finally, you’ll explore how to handle missing values and training and validating your models using PySpark.

Machine Learning 52

Machine Learning Machine Learning Data Science Data Scientist 52

Pickl AI

FEBRUARY 12, 2024

Machine Learning Algorithms Candidates should demonstrate proficiency in a variety of Machine Learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks. Handling missing values is a critical aspect of data preprocessing.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Pickl AI

JULY 18, 2023

Gain hands-on experience in implementing algorithms and working with AI frameworks such as TensorFlow , PyTorch, or scikit-learn. Learn Machine Learning and Deep Learning Deepen your understanding of machine learning algorithms, statistical modelling, and deep learning architectures.

AI 52

AI AI Machine Learning Machine Learning 52

Pickl AI

JULY 18, 2023

Common libraries in Python, such as pandas and NumPy, are essential for data cleaning, preprocessing, and transformation. Gain experience in working with datasets, data wrangling, and data visualization. Study machine learning: Understand the principles and algorithms of machine learning.

Computer Science 52

Computer Science Computer Science Data Science Machine Learning 52

ODSC - Open Data Science

JANUARY 29, 2024

These outputs, stored in vector databases like Weaviate, allow Prompt Enginers to directly access these embeddings for tasks like semantic search, similarity analysis, or clustering. Additionally, prompt engineering relies heavily on machine learning tasks like fine-tuning, bias detection, and performance evaluation.

Machine Learning 52

Machine Learning Machine Learning Data Science Natural Language Processing 52

Pickl AI

JULY 20, 2023

Here are some project ideas suitable for students interested in big data analytics with Python: 1. Kaggle datasets) and use Python’s Pandas library to perform data cleaning, data wrangling, and exploratory data analysis (EDA). Analyzing Large Datasets: Choose a large dataset from public sources (e.g.,

Analytics 40

Analytics Analytics Big Data Big Data 40

Dataconomy

MAY 16, 2023

Mathematical and statistical knowledge: A solid foundation in mathematical concepts, linear algebra, calculus, and statistics is necessary to understand the underlying principles of machine learning algorithms.

Data Scientist 156

Data Scientist ML ML Machine Learning 156