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Stars4
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Rank 3,293,820 (Top 66 %)
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LanguagePython
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Created about 4 years ago
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Updated about 4 years ago
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Feature-Engineering
All Techniques of Feature Engineering.Jupyter Notebook
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Machine-Learning-from-scratch
Jupyter Notebook
6
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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.Python
4
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Tweets-Analysis
Using bert for tweet analysisPython
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RajputJay41
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Data-Structure-and-Algorithms
Covering all the interviews Problem solving the leet code problems.C++
3
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Wine-Quality-
Python
3
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Deep-learning-from-scratch
3
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Keras-Tuner
Using Keras Tuner for knowing Best Hyper ParametersJupyter Notebook
3
10
Using-bert-and-pytorch-for-IMDB
Python
3
11
Malenoma--classification
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Mechanisms-of-action-kaggle-comp-
Need to download the dataset from the kaagle.Python
3
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Data-Science
A Beginners Guide to Data Science. A Respository to get you job ready as a Data Science fresher
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web
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MY-sql-with-Python
Jupyter Notebook
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portfo-withh-flask
HTML
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Computer-Vision-Projects
Lets make our hand dirty with CV.Python
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Data-Visualization-and-Analysis
Jupyter Notebook
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19
Pytorch-Trainer
Just a Model.py file created so dont need to do much coding, just edit this and have fun.Python
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P_ython
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python-for-finance
Its all about python. programs and projects and Frameworks.Python
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22
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 boostingJupyter Notebook
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Julia-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.
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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.Jupyter Notebook
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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).
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Scala-from-scratch-for-DataScience
When it comes to DSE, Apache Spark is the widely used tool in the industry which is written using Scala programming language. Spark is an extension for Hadoop which does batch processing as well as real-time processing. Compared to Hadoop, Spark is more efficient due to many reasons. Find more information on Spark from here. Scala is an extension of java language that is inter-operable with java as it runs on JVM. It has many different features compared to java and a language more focused on Functional Programming. Since Spark is written using Scala, for Spark users Scala will be more appropriate compared to other languages. So using Scala we can get the maximum out of the Spark framework without any restrictions. There are many features in Scala a Data Science Engineer should be familiar with, such as val, Higher Order Functions, Partial Functions, Pattern Matching & Case Classes, Collections, Currying and Implicit.
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