Tips and Tricks.
Data science, which should not be mistaken for information science, is a field of study that uses scientific processes, methods, systems, and algorithms to extract insights and knowledge from various forms of data, be it structured or unstructured. It is similar to data mining and is a conception to unify machine learning, data analysis, statistics, and their related methods so as to analyze and understand the actual facts about data. Data science employs theories and techniques taken from many disciplines such as computer science, information science, statistics, and mathematics.
In a nutshell, data science can be said to be a multidisciplinary blend of algorithm development, data inference, and technology in a way to find solution to analytically complex problems. Data science is useful to anyone who wants to be a data miner.Hire Data Science Experts
you are provided with a dataset containing mobility traces of ~500 taxi cabs in San Francisco collected over ~30 days. The format of each mobility trace file is the following - each line contains [latitude, longitude, occupancy, time], e.g.: [37.75134 -122.39488 0 1213084687], where latitude and longitude are in decimal degrees, occupancy shows if a cab has a fare (1 = occupied, 0 = free) and time is in UNIX epoch format. The goal of this data science challenge is twofold: 1. To calculate the potential for a yearly reduction in CO2 emissions, caused by the taxi cabs roaming without passengers. In your calculation please assume that the taxicab fleet is changing at the rate of 15% per month (from combustion engine-powered vehicles to electric vehicles). Assume also that the average pas...
need to link my tradingview webhook alerts with TV-HUB to Binance
We have an exciting fashion client based in Montreal, Canada that requires some category research, with a follow-on deep dive specifically into 3 leading brands. We're looking to understanding how the category promotes itself through advertising, social media, press, and retail activation.
I already have a model that is built using TensorFlow Decision Forests. I have to convert some of the preprocessing steps from pandas to tensorflow. And model building steps. Bid only if you have experience in tensorflow and is available to do it right now.
The project will require you to perform data pre-processing that is needed for each dataset that will be used in this project. Thereafter, you will be required to build models for each of the two classifiers and optimize them for accuracy. Finally, you will experiment with different data distributions and then compare the two classifiers in terms of stability of their model accuracy.
I am starting out in data science and machine learning be I have a few questions that will help me progress in my own projects. I want to develop model that recognises certain stock chart patterns like a triangle, bullish/bearish flag etc. What I am looking for at the moment is not someone who will deliver a complete project but I need consultancy where people can answer some of the questions I have that is why I want to pay hourly. What I am struggling with at the moment is preparing the complete dataset for machine learning. I have a collection of many samples of the chart pattern (some are attached) But I don’t know how to compile the master dataset with a large set of stocks each having a varying amount of time periods showing open, high , low, close and volume. To make sure ...
make deep neural network for regression supervised problem
Our organization is seeking for a freelancer to work on a deep learning image classification project. The project's goal is to select informative data to train a CNN using semi-supervised active learning. You should be proficient in deep learning, particularly image classification, and have a degree, although a master's or doctorate degree is preferred.
This assignment covers linear regression and logistic regression using disciminative meth- ods. 1 The Iris Dataset The Iris flower data set () was orga- nized by Ronald Fisher in 1936. It is a commonly used dataset for introductory machine learning concepts. You will use this dataset for use with a classification AND regression task. 1.1 Preparing the Data To begin, load the data using scikit-learn. As we saw during class, the setosa samples are very clearly linearly separable given any combination of two features. However, the versicolor and virginica usually have some overlap. In order to verify the models that you will create in the following two sections, you will need to take some portion of the dataset and reserve it for testing. Randomly select 10% of the dataset, ensuring an eve...
We need to design a pdf information brochure of Data science course in 5-6 pages.
Hello Everyone, I am Vihar, Our company is having a requirement of Data-Science trainer. Job Detail: This course is ideal for you if you are working in a role that works with data, such as advertising, journalism, design, computing, etc., or you are a complete fresher (no experience) who wants to learn more about getting value from data and make a career as a Data Scientist. Training: 2-3 hrs/day Experience: 5+ years Education: BE/ME Kindly find all other things in the attached document [ NOTE: Those who are interested only that people can bid ]
COMMAND DASHBOARD INTEGRATING NEXT-GEN TECHNOLOGY (CommanDING Tech) CHALLENGE: PHASE 1 For more information about the challenge, see the official rules at : Challenge Timeline: June 06, 2022 - Early March 2023 Total Prizes: $1,000,000 USD Phase 1 Start: June 06, 2022 Phase 1 Submission Deadline: July 31, 2022 at 5:00pm ET This challenge is run under the America COMPETES Act. —------------------------------------------------------------------------------------------------------------------ IMPORTANT LINKS 1. Visit the Challenge Repository that contains the challenge resources that will help guide your participation in this challenge: 2. Submit the Registration Form to be eligible to participate in the challenge: 3. On Thursday, June 16 at 10am PT/1pm ET Free...