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$13 USD / heure
Drapeau de RUSSIAN FEDERATION
industrialnyy rayon, russian federation
$13 USD / heure
Il est actuellement 12:16 AM ici
Membre depuis le mai 16, 2022
0 Recommandations

Alex C.

@alekscoch

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$13 USD / heure
Drapeau de RUSSIAN FEDERATION
industrialnyy rayon, russian federation
$13 USD / heure
N/A
Travaux complétés
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Suivant le budget
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Taux de réembauche

ML developer

I create the best Machine Learning algorithms to generate value for your business. I am going to offer you my experience in Data Science, plus a master’s degree, to solve your problems from scratch and bring value to your business. I am interested in developing machine learning and deep learning techniques to solve real-world issues. I have great experience in query optimization, application profiling, and troubleshooting. My area of expertise includes: - Python (e.g., web scraping, pandas, Numpy, matplotlib, scikit-learn, TensorFlow, Keras) - SQL - Data Visualization (Plotly, Matplotlib, Seaborn) - Docker, Github, Linux/Bash - Google Colab, Jupyter - C/C++ - Matlab - JSON, Rest API - Cloud Computing - Tableau - In-depth data analysis (descriptive, inferential statistics and multivariate analyses, such as linear regression, ANOVA, t-test, Mann-Whitney, Kruskal-Wallis...) - Computer Vision: Object detection, Classification, Segmentation, YOLO, OpenCV - Supervised Machine Learning (e.g., Neural networks, Linear regression, Logistic regression, Support vector machine (SVM), K-nearest neighbor (kNN), Random forest) - Unsupervised Machine Learning (k-Means) - Customer Segmentation - Image classification - NoSql Database like MongoDB - Deep Learning Skills • Analytics • Python • NumPy • PyCharm • Data Cleansing • Data Visualization • Data Analytics • Data Science • SQL • C/C++ • PHP • Docker • Cloud Computing • Machine Learning • Deep Learning • Supervised Learning • Unsupervised Learning • MongoDB • Computer Vision • TensorFlow • Keras • OpenCV • Deep Neural Networks
Freelancer Python Developers Russian Federation

Contactez Alex C. concernant votre emploi

Connectez-vous pour discuter des détails via la messagerie.

Éléments du portfolio

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Analyzing and visualizing trends over time
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Analyzing and visualizing trends over time
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Analyzing and visualizing trends over time
Pandas python
Jypyter Notebook
Python Notebook
Google Colab

Tasks
detect NaN (not a number) values and clean your data
sort data
group data by category
Analysis of the Post-University Salaries of Graduates by Maj
Pandas python
Jypyter Notebook
Python Notebook
Google Colab

Tasks
detect NaN (not a number) values and clean your data
sort data
group data by category
Analysis of the Post-University Salaries of Graduates by Maj
Pandas python
Jypyter Notebook
Python Notebook
Google Colab

Tasks
detect NaN (not a number) values and clean your data
sort data
group data by category
Analysis of the Post-University Salaries of Graduates by Maj
Analyzing the Popularity of Different Programming Languages over Time
Tasks:
•	Visualizing data and creating charts with Matplotlib;
•	Pivoting, grouping and manipulating data with Pandas to get it into the convenient format;
•	Working with timestamps and time-series data;
•	Styling and customizing a line chart.
Data Visualisation
Analyzing the Popularity of Different Programming Languages over Time
Tasks:
•	Visualizing data and creating charts with Matplotlib;
•	Pivoting, grouping and manipulating data with Pandas to get it into the convenient format;
•	Working with timestamps and time-series data;
•	Styling and customizing a line chart.
Data Visualisation
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Aggregating & Merging Data
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Aggregating & Merging Data
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Aggregating & Merging Data
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Aggregating & Merging Data
Tasks:
-	making time-series data comparable by resampling and converting to the same periodicity
-	Fine-tuning the styling of Matplotlib charts by using limits, labels, linestyles, markers, colours, and the chart's resolution
-	Using grids to help visually identify seasonality in a time series
-	Finding the number of missing and NaN values and locating NaN values in a DataFrame
-	working with Locators to better style the time axis on a chart
Resampling and Visualizing Time Series
Tasks:
-	making time-series data comparable by resampling and converting to the same periodicity
-	Fine-tuning the styling of Matplotlib charts by using limits, labels, linestyles, markers, colours, and the chart's resolution
-	Using grids to help visually identify seasonality in a time series
-	Finding the number of missing and NaN values and locating NaN values in a DataFrame
-	working with Locators to better style the time axis on a chart
Resampling and Visualizing Time Series
Tasks:
-	making time-series data comparable by resampling and converting to the same periodicity
-	Fine-tuning the styling of Matplotlib charts by using limits, labels, linestyles, markers, colours, and the chart's resolution
-	Using grids to help visually identify seasonality in a time series
-	Finding the number of missing and NaN values and locating NaN values in a DataFrame
-	working with Locators to better style the time axis on a chart
Resampling and Visualizing Time Series
Tasks:
-	making time-series data comparable by resampling and converting to the same periodicity
-	Fine-tuning the styling of Matplotlib charts by using limits, labels, linestyles, markers, colours, and the chart's resolution
-	Using grids to help visually identify seasonality in a time series
-	Finding the number of missing and NaN values and locating NaN values in a DataFrame
-	working with Locators to better style the time axis on a chart
Resampling and Visualizing Time Series
Tasks:
•	Making a judgement if our regression is good or bad based on how well the model fits our data and the r-squared metric
•	Running regressions with scikit-learn and calculate the coefficients.
Linear Regression and Data Visualisation
Tasks:
•	Making a judgement if our regression is good or bad based on how well the model fits our data and the r-squared metric
•	Running regressions with scikit-learn and calculate the coefficients.
Linear Regression and Data Visualisation

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Expérience

Computer Vision Engineer

SkyScout Group, Slovenia
mars 2022 - Jusqu'à présent
• Proposing and implementing creative, efficient solutions for vision problems • Developing novel state-of-the-art vision algorithms, leveraging various deep learning techniques • Developing and evaluating computer vision algorithms for real-time control of Sports events

Machine learning Engineer

TDI-India
déc. 2021 - Jusqu'à présent
• Implementing solutions using Machine Learning techniques in Computer vision • Researching appropriate ML algorithms and tools • Prototyping ML systems and run tests to evaluate performance on real-world data

Éducation

Master Degree

Iževskij Gosudarstvennyj Tehniceskij Universitet, Russian Federation 1995 - 2000
(5 ans)

Qualifications

Lean Six Sigma Green Belt

IASSC
2021
The IASSC Certified Lean Six Sigma Green Belt™ is a professional who is well versed in the core to advanced elements of Lean Six Sigma Methodology.

Contactez Alex C. concernant votre emploi

Connectez-vous pour discuter des détails via la messagerie.

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Certifications

preferredfreelancer-1.png Preferred Freelancer Program SLA 1 94%

Meilleures compétences

Python Machine Learning (ML) Data Science Artificial Intelligence Data Analytics

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