Machine learning (ML) is the study and the use of computer algorithms that can improve itself through experience and by the use of data. It is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It involves computers discovering how they can perform various tasks. It involves computers learning from data provided so that they carry out certain tasks. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand. For the advanced tasks, it can be challenging to manually create the needed algorithms. In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than having programmers specify every needed step.
Automation & Bots 🤖
Relevant Examples:
- Sending email using python
- Linkedin Bots
Data Visualisation 📊
Relevant Examples:
- Covid data visualisation
Naive Bayes Classifier 👦
Relevant Examples:
- Given the weather conditions, each tuple classifies the conditions as fit(“Yes”) or unfit(“No”) for plaing golf.
Linear regression📈
Relevant Examples:
- Air Quality Prediction
Logistic regression↕️
Relevant Examples:
- Diabeties Classification using Logistic regression
K Nearest Neighbours 🤝
Relevant Examples:
- Face recognition using haarcascades and KNN
Support Vector Machine 🧮
Relevant Examples:
- SVM for identifying the classification of genes given genes dataset
Decision trees 🌲
Relevant Examples:
- Predict the surviours left and evaluation metrics using the Titanic dataset.
Random Forest 🪵
Relevant Examples:
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Predict the surviours left and evaluation metrics using the Titanic dataset.
K Means Clustering 👥
Relevant Examples:
-
Dominant color detection
Recommender Systems ☁️
Relevant Examples:
- Recommend movies based on collaborative filtering
Artificial Neural Networks 🧠
Relevant Examples:
- Predict the label of the input image of MNIST Digits and show the evaluation metrics of the model used.
Convlutional Neural Networks 🙈
Relevant Examples:
- Predict the label of the input image of Fashion MNIST and show the evaluation metrics of the model used
- Cat vs Dog Classifier
- Hand Gesture Recognition
Recurrent Neural Networks 💹
Relevant Examples:
-
Time Series Predictions using RNN.
OpenCV 📸
Relevant Examples:
- Mask Detection using Haarcascades and opencv.
Natural Language Processing 🔤
Relevant Examples:
- Text rank algortihm to find the most important keywords in a paragraph
Computer Vision 👁
Relevant Examples:
- Optical Character Recognition
- Avail your Github Student Pack.
- Sign in / Sign up on Azure.
- In VS Code download the Azure Extension.
- Now sign into Azure from VS Code.
- Then open this tab
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If it asks you to create a new workspace, click on yes and create a new workspace.
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Click on the plus icon to create a new app service
- Enter a globally unique name for your app service.
- Select runtime stack is python 3.8 (or any other version according to your API )
- Right-click on the app service you created.
- Click on Deploy to the Web App.
- Check the deployment logs if there are any errors.
- After the API gets deployed test it using Postman (or any other API testing application).
- Make a collection on postman for the required routes.
- Now create an Issue and send a Pull Request(attach the postman collection).
With ❤️ by ISTE-VIT