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Comprehensive Introduction to Data Science

About Us » SupportVectors

Who is Comprehensive Introduction to Data Science?

All of the courses at SupportVectors focus on the areas of machine learning, deep learning/AI, large-scale data, data visualization, wrangling with data, and AI application development in the cloud. NLP with Transformers & Large Language Models Starts on August 12th, 2023. Saturday, July. 15, 2023 and Sunday July 16, 2023 10:00 am to 5 pm. Saturday, Feb. 25, 2023 and Sunday Feb 26, 2023 10:00 am to 5 pm. SupportVectors AI lab and training institution are interested in cultivating an environment where engineering professionals can come together and learn in a creative environment.

This company is:

Educational institution

Fremont, United States

11-50 Employees

Founding year: 2015



Products & services of Comprehensive Introduction to Data Science

Product Course: Automated Machine Learning (AutoML) » SupportVectors image
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Verified

Course: Automated Machine Learning (AutoML) » SupportVectors

This is a hands-on, case-studies-based short course on automated machine learning (AutoML). Here, we will learn the following: What is automated machine learning? Bayesian inference and how it enables hyperparameter optimization Neural architecture search Examples and exercises with various auto-ml libraries Case studies Automl in GCP A fun (group) project with AutoML

Product Intermediate Machine Learning » SupportVectors image
Product

Verified

Intermediate Machine Learning » SupportVectors

Intermediate Machine Learning (ML-200) is a continuation of "Introduction to Machine Learning (ML-100)". In this course, we cover extensively some of the powerful methods widely used in the current industry and research for data analyses. More details are available at Intermediate Machine Learning

Product Free Event: Wrangling with data » SupportVectors image
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Free Event: Wrangling with data » SupportVectors

Learn powerful techniques of data manipulation, cleanup, missing values handling, and web scraping. In this course, you will cultivate a solid foundation in data wrangling, including transformations, cleanup, missing-values handling, and web-scaping. Extraction and standardizing data into a tidy format is a crucial prerequisite to meaningful data exploration, machine-learning models, and the derivation of insights from the data.  The teaching faculty for this workshop comprises the instructor, a supportive staff of teaching assistants, and a workshop coordinator. You will work closely with their guidance and help in 1-1 sessions. You can attend the workshop in person or remotely. State-of-the-art facilities and instructional equipment ensure that the learning experience is invariant of either choice. Of course, you can also mix the two modes: attend the workshop in person when you can, and attend it remotely when you cannot. All sessions are live-streamed and recorded and available on the workshop portal. See more details on the course page:  Event: Wrangling with data

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Keywords

AI
Data Science
Machine Learning
Cloud AI
GCP
Training in AI
Training in Data Science
Natural Language Processing
Data Visualization
Computer Vision
Time Series
Anomaly and Fraud Detection
Hybrid Learning
Instructor-led hands-on workshops
Silicon Valley leaders in AI training
Pytorch
Tensorflow
AI Pipelines
Kubeflow
Kubernetes
Big Data
BigQuery
BigTable
Spark
Tidy Data
E-learning

Industries

Education

Contact of Comprehensive Introduction to Data Science

City: Fremont

State: California

Country: United States



Frequently asked questions (FAQ) about Comprehensive Introduction to Data Science

The company Comprehensive Introduction to Data Science is located in Fremont, California, United States. It's worth noting that the company may has more corporate locations

As of the latest available information Comprehensive Introduction to Data Science has around 11-50 employees worldwide.

Comprehensive Introduction to Data Science was founded in 2015

The company Comprehensive Introduction to Data Science has it's main focus in the industries of Education

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