You will get exceptional Deep Learning and machine learning expertise for your business.
Project details
My projects encapsulate the essence of a professional data scientist who thrives on pushing boundaries and delivering impactful results. With my comprehensive skill set, ability to bridge the gap between data and business, and relentless commitment to staying ahead of the curve, I am poised to drive transformation and empower organizations to make data-driven decisions in an increasingly competitive landscape.
Machine Learning Tools
Apache Spark MLlib, Azure Machine Learning, BERT, Databricks Platform, Databricks MLflow, Keras, KNIME, Microsoft Power BI, MLflow, NLTK, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, R, SciPy, Scrapy, SQL, TensorFlow, TextBlob, Word2vec, XGBoostWhat's included
| Service Tiers |
Starter
$30
|
Standard
$50
|
Advanced
$100
|
|---|---|---|---|
| Delivery Time | 3 days | 4 days | 10 days |
Number of Revisions | Unlimited | Unlimited | Unlimited |
Number of Model Variations | 3 | 5 | 10 |
Number of Scenarios | 5 | 8 | 14 |
Number of Graphs/Charts | 5 | 10 | 15 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | |||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$10 - $30
Additional Model Variation
+$5
Additional Graph/Chart
+$5About Chukwudi
Data Scientist | Data Engineer | Machine Learning Expert
Stavanger, Norway - 6:34 pm local time
Skills and Tools
• Statistical analysis
• Programming
• Data visualization
• Exploratory data analysis (EDA)
• Data mining
• Feature engineering
• Predictive modeling
• Data storytelling
• Deep learning
• Python
• R
• SQL
• TensorFlow
• Jupyter Notebook
• Scikit-Learn
• Azure
• Databricks
• Git
Steps for completing your project
After purchasing the project, send requirements so Chukwudi can start the project.
Delivery time starts when Chukwudi receives requirements from you.
Chukwudi works on your project following the steps below.
Revisions may occur after the delivery date.
Understand the Project Requirements
A clear understanding of project expectations, goals, and deliverables.
Data Collection and Preparation
Gather relevant data from diverse sources, and ensure its integrity and accuracy. Clean and handle missing values, and address outliers to ensure high-quality datasets. Perform feature engineering to extract meaningful insights, via domain knowledge.