santoshmaher Goto Github PK
Type: User
Company: Needyin.com
Bio: Machine Learning Engineer | NLP | Chatbot |
Location: Hyderabad,india
Blog: http://www.needyin.com
Type: User
Company: Needyin.com
Bio: Machine Learning Engineer | NLP | Chatbot |
Location: Hyderabad,india
Blog: http://www.needyin.com
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I got some Data Science and AI/ML figures on the internet for better understanding.
Heart rate variability which find out the deference between two adjacent distances
All Machine learning Algorithms implemention
Using Machine Learning algorithms solve using Dataset is downloaded from Kaggle.
Types of Machine Learning Algorithms There are 3 types of machine learning (ML) algorithms: Supervised Learning Algorithms: Supervised learning uses labeled training data to learn the mapping function that turns input variables (X) into the output variable (Y). In other words, it solves for f in the following equation: Y = f (X) This allows us to accurately generate outputs when given new inputs. We’ll talk about two types of supervised learning: classification and regression. Classification is used to predict the outcome of a given sample when the output variable is in the form of categories. A classification model might look at the input data and try to predict labels like “sick” or “healthy.” Regression is used to predict the outcome of a given sample when the output variable is in the form of real values. For example, a regression model might process input data to predict the amount of rainfall, the height of a person, etc. The first 5 algorithms that we cover in this blog – Linear Regression, Logistic Regression, CART, Naïve-Bayes, and K-Nearest Neighbors (KNN) — are examples of supervised learning. Ensembling is another type of supervised learning. It means combining the predictions of multiple machine learning models that are individually weak to produce a more accurate prediction on a new sample. Algorithms 9 and 10 of this article — Bagging with Random Forests, Boosting with XGBoost — are examples of ensemble techniques. Unsupervised Learning Algorithms: Unsupervised learning models are used when we only have the input variables (X) and no corresponding output variables. They use unlabeled training data to model the underlying structure of the data. We’ll talk about three types of unsupervised learning: Association is used to discover the probability of the co-occurrence of items in a collection. It is extensively used in market-basket analysis. For example, an association model might be used to discover that if a customer purchases bread, s/he is 80% likely to also purchase eggs. Clustering is used to group samples such that objects within the same cluster are more similar to each other than to the objects from another cluster. Dimensionality Reduction is used to reduce the number of variables of a data set while ensuring that important information is still conveyed. Dimensionality Reduction can be done using Feature Extraction methods and Feature Selection methods. Feature Selection selects a subset of the original variables. Feature Extraction performs data transformation from a high-dimensional space to a low-dimensional space. Example: PCA algorithm is a Feature Extraction approach. Algorithms 6-8 that we cover here — Apriori, K-means, PCA — are examples of unsupervised learning. Reinforcement learning: Reinforcement learning is a type of machine learning algorithm that allows an agent to decide the best next action based on its current state by learning behaviors that will maximize a reward. Reinforcement algorithms usually learn optimal actions through trial and error. Imagine, for example, a video game in which the player needs to move to certain places at certain times to earn points. A reinforcement algorithm playing that game would start by moving randomly but, over time through trial and error, it would learn where and when it needed to move the in-game character to maximize its point total.
Node.js JavaScript runtime :sparkles::turtle::rocket::sparkles:
Sentiment analysis Using BERT on Customer Complaint
Extract the sentiment score (-1 for negative, 0 for neutral and 1 for positive) from any given text using the vaderSentiment library. VADER stands for Valence Aware Dictionary and sEntiment Reasoner, which is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on text from other domains. Installing the requirements for this tutorial: pip install vaderSentiment
# Text Analysis and prediction model using LSTM prediction next world based on training model LSTM store previous sentence and predicting new sentence or word. in the prediction model, we use LSTM, kears, and NLTK.
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User Engagement Codelab - https://codelabs.developers.google.com/codelabs/actions-user-engagement
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.