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Feature-Engineering
All Techniques of Feature Engineering.Machine-Learning-from-scratch
Ensembling-Blending-and-Stacking
Ive shown each and everyway how to blend and stack with bunch of algortihms then its all up to you. how you use them.Tweets-Analysis
Using bert for tweet analysisRajputJay41
mlops
Data-Structure-and-Algorithms
Covering all the interviews Problem solving the leet code problems.Wine-Quality-
Deep-learning-from-scratch
Keras-Tuner
Using Keras Tuner for knowing Best Hyper ParametersUsing-bert-and-pytorch-for-IMDB
Malenoma--classification
Mechanisms-of-action-kaggle-comp-
Need to download the dataset from the kaagle.Data-Science
A Beginners Guide to Data Science. A Respository to get you job ready as a Data Science fresherweb
MY-sql-with-Python
portfo-withh-flask
Computer-Vision-Projects
Lets make our hand dirty with CV.Data-Visualization-and-Analysis
Pytorch-Trainer
Just a Model.py file created so dont need to do much coding, just edit this and have fun.P_ython
python-for-finance
Its all about python. programs and projects and Frameworks.House-Price-Prediction
Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home. Practice Skills Creative feature engineering Advanced regression techniques like random forest and gradient boostingJulia-for-Machine-Learning
Julia provides powerful tools for deep learning (Flux.jl and Knet.jl), machine learning and AI. Julia’s mathematical syntax makes it an ideal way to express algorithms just as they are written in papers, build trainable models with automatic differentiation, GPU acceleration and support for terabytes of data with JuliaDB. Julia's rich machine learning and statistics ecosystem includes capabilities for generalized linear models, decision trees, and clustering. You can also find packages for Bayesian Networks and Markov Chain Monte Carlo.Hyperparameter-Tuning-with-the-HParams-Dashboard
When building machine learning models, you need to choose various hyperparameters, such as the dropout rate in a layer or the learning rate. These decisions impact model metrics, such as accuracy. Therefore, an important step in the machine learning workflow is to identify the best hyperparameters for your problem, which often involves experimentation. This process is known as "Hyperparameter Optimization" or "Hyperparameter Tuning". The HParams dashboard in TensorBoard provides several tools to help with this process of identifying the best experiment or most promising sets of hyperparameters.Malenoma-Classification
Search Results Featured snippet from the web Melanoma, also known as malignant melanoma, is a type of skin cancer that develops from the pigment-producing cells known as melanocytes. Melanomas typically occur in the skin but may rarely occur in the mouth, intestines or eye (uveal melanoma).Love Open Source and this site? Check out how you can help us