You will get your Data Science and Machine Learning tasks done using Python
Project details
Gig Includes services like:
✅ Data Visualization, Data analysis, and Data Cleaning (Missing Values, Outliers detection...)
✅ Machine Learning model building
✅ Hyperparameters Tuning
✅ Model Deployment
✅ Data Visualization, Data analysis, and Data Cleaning (Missing Values, Outliers detection...)
✅ Machine Learning model building
✅ Hyperparameters Tuning
✅ Model Deployment
What's included $200
These options are included with the project scope.
$200
- Delivery Time 5 days
- Number of Revisions 2
- Number of Model Variations 2
- Number of Scenarios 2
- Number of Graphs/Charts 10
- Model Validation/Testing
- Source Code
Frequently asked questions
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AM
Alina M.
Feb 4, 2021
Machine Learning - Computer Vision Engineer
Thank you for completing our initial task!
About Fares
Data Scientist | Machine Learning Engineer
Setif, Algeria - 7:58 am local time
I have a lot of experience with Machine Learning, Data Analysis, and Data Visualization. I have programming knowledge in Python, Rust, and SQL.
As a Data Science competitor at Kaggle, I participate in competitions (Computer Vision, Natural Language Processing, Time Series Analysis, and Predictive Modeling) based on real-world problems.
✅ My services:
➼ Data cleaning, Data analysis, and Data visualization.
➼ Supervised learning (Classification / Regression)
➼ Time series analysis
➼ Natural Language Processing (NLP)
➼ Computer Vision (CV)
➼ Machine Learning Operations (MLOps)
✅ Machine Learning Algorithms:
➼ Linear/Logistic Regression
➼ K-Nearest Neighbours (kNN) | Decision Trees | Support Vector Machine (SVM)
➼ Ensemble methods (Random Forest, XGBoost, CatBoost, AdaBoost, LightGBM, etc)
➼ Neural Network (ANN, RNN, CNN, DNN, etc)
➼ Transformers
✅ Tools:
➼ Python
➼ Matplotlib | Seaborn | Pandas | Numpy | Dask
➼ SpaCy | Gensim | HuggingFaces | NLTK
➼ Scikit-Learn | Tensorflow | Keras
➼ Docker
Steps for completing your project
After purchasing the project, send requirements so Fares can start the project.
Delivery time starts when Fares receives requirements from you.
Fares works on your project following the steps below.
Revisions may occur after the delivery date.
Gather requirements
- Determine the project goal and the right metric for determining success. - Collect project data with a description of the data. - Determine the timelines and work plan for the project.
Deliver first draft
Deliver the first version to the client. An MVP (Minimum Valuable Product) -in our case a Basic Machine Learning Model- will be delivered to the client to: see if we are on the right track, get early feedback, and test the product on production.