I will do your data science and machine learning projects in python
I will do your data science and machine learning projects in python
In this post, you will complete your first machine learning project using Python. In this step-by-step tutorial you will: Download and install Python SciPy
Get data science and machine learning projects in python
There are many data science and machine learning projects that you can do in Python. Here are some examples of projects that you can work on:
Ultrasound Nerve Segmentation - This project involves segmenting ultrasound images of the neck to identify the location of the nerve.
Retail Price Optimization - This project involves using machine learning algorithms to optimize retail prices.
Demand prediction of driver availability using multistep Time Series Analysis - This project involves predicting driver availability using time series analysis.
Customer Market Basket Analysis using Apriori and FP-growth algorithms - This project involves analyzing customer purchase patterns using association rule mining algorithms.
E-commerce product reviews – Pairwise ranking and sentiment analysis - This project involves analyzing product reviews to identify the sentiment of the review.
You can find more projects in this article 1. You can also check out Kaggle 2 and ProjectPro 3 for more data science and machine learning projects.
Ultrasound Nerve Segmentation is a project that involves segmenting ultrasound images of the neck to identify the location of the nerve.
The goal of this project is to build an algorithm for automatic segmentation of nerves in ultrasound images of the neck. Deep convolutional neural networks have demonstrated incomparable performance for various tasks such as image classification and object detection 1.
You can find more information about this project on Github 1 and ProjectPro 2.
A convolutional neural network (CNN) is a deep learning neural network designed for processing structured arrays of data such as images.
Convolutional neural networks are widely used in computer vision and have become the state of the art for many visual applications such as image classification 1.
You can find more information about CNNs on DeepAI 1, Wikipedia 2, and IBM 3.
Deep learning is a subset of machine learning that uses neural networks with three or more layers.
These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data 1.
You can find more information about deep learning on IBM 1, Wikipedia 2, and Glints 3.
Machine learning is a field devoted to understanding and building methods that let machines “learn” – that is, methods that leverage data to improve computer performance on some set of tasks 1.
Machine learning models fall into three primary categories: supervised machine learning, unsupervised machine learning, and reinforcement learning 2.
You can find more information about machine learning on IBM 2, Wikipedia 1, and MIT Sloan 3.
Supervised learning is a machine learning paradigm for problems where the available data consists of labeled examples, meaning that each data point contains features (covariates) and an associated label.
The goal of supervised learning algorithms is learning a function that maps feature vectors (inputs) to labels (output), based on input-output pairs 1.
You can find more information about supervised machine learning on IBM 2, Wikipedia 1, and Google Developers 3.
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