Ahli semenjak 13 Januari 2013

Dalam Talian Luar Talian
I'm a experienced Data Scientist ready to do your work in an efficient way with strong analytical and logical reasoning skills with insatiable interest in Data Science and Big Data Analytics. Data analysis- Data Mining-Forecasting using R, SPSS, Excel and SAS Enterprise Miner. Machine learning- KNN, SVM, NB, PCA, LDA, RF, K-means/Hierarchical clustering, Supervised and Unsupervised Learning etc. Deep Learning - RNN, CNN, RCNN, RNTN etc. Programming Languages - R, Python, Java, C++, C, C# etc. Databases: Teradata-SQL, MySQL
\$25 USD/hr
15 ulasan
4.4
• 100%Kerja Diselesai
• 79%Mengikut Bajet

## Pengalaman

### Data Scientist

Feb 2017

Role: Data Scientist, Data Visualizer and Prediction Modeling Engineer â€¢ Prediction of the Crime at particular locations and various shifts by using Intelligent ML techniques. â€¢ Data Visualizations of the crimes at various localities and instances using [login to view URL] â€¢ Automated Number Plates Recognition from the live streams â€¢ Object Recognition of the potential suspects from CCTV live streams â€¢ Front-end design of the product

## Pendidikan

### Bachelor of Computer Engineering

2011 - 2015 (4 years)

### Master of Computer Engineering

2015 - 2017 (2 years)

## Kelayakkan

### Correlation and Regression (2017)

DataCamp

Ultimately, data analysis is about understanding relationships among variables. Exploring data with multiple variables requires new, more complex tools, but enables a richer set of comparisons. In this course, you will learn how to describe relationships between two numerical quantities. You will characterize these relationships graphically, in the form of summary statistics, and through simple linear regression models.

### Credit Risk Modeling in R (2017)

DataCamp

This hands-on-course with real-life credit data taught me how to model credit risk by using logistic regression and decision trees in R. Modeling credit risk for both personal and company loans is of major importance for banks. The probability that a debtor will default is a key component in getting to a measure for credit risk. Focused on two model types that are often used in the credit scoring context; logistic regression and decision trees.

### Deep Learning in Python (2017)

DataCamp

Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition and artificial intelligence (including the famous AlphaGo). In this course, I gained hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting edge library for deep learning in Python.

### Supervised Learning with scikit-learn (2017)

Datacamp

In this course, I learnt how to use Python to perform supervised learning, an essential component of Machine Learning. I learnt how to build predictive models, how to tune their parameters and how to tell how well they will perform on unseen data, all the while using real world datasets. I did so using scikit-learn, one of the most popular and user-friendly machine learning libraries for Python.

### Text Mining: Bag of Words (2017)

DataCamp

It is estimated that over 70% of potentially useable business information is unstructured, often in the form of text data. Text mining provides a collection of techniques that allow to derive actionable insights from these data. In this course, we explore the basics of text mining using the bag of words method. Essential topics for analyzing and visualizing text data. Applying everything learnt in a real-world case study to extract insights from employee reviews of two major tech companies.

### Unsupervised Learning in Python (2017)

DataCamp

In this course, I learnt the fundamentals of unsupervised learning and implement the essential algorithms using scikit-learn and scipy. I also learnt how to cluster, transform, visualize, and extract insights from unlabeled datasets, and end the course by building a recommender system to recommend popular musical artists.

### Unsupervised Learning in R (2017)

DataCamp

Many times in machine learning, the goal is to find patterns in data without trying to make predictions. This is called unsupervised learning. One common use case of unsupervised learning is grouping consumers based on demographics and purchasing history to deploy targeted marketing campaigns. This course provided a basic introduction to clustering and dimensionality reduction in R from a machine learning perspective, so that I could get from data to insights as quickly as possible.

### Exploratory Data Analysis (2017)

DataCamp

In this course, I learnt how to use graphical and numerical techniques to begin uncovering the structure of data. Which variables suggest interesting relationships? Which observations are unusual? By the end of the course, you'll be able to answer these questions and more, while generating graphics that are both insightful and beautiful.

### Machine Learning Toolbox (2017)

DataCamp

In this course I learnt the big ideas in machine learning: how to build and evaluate predictive models, how to tune them for optimal performance, how to preprocess data for better results, and much more. The popular caret R package, which provides a consistent interface to all of R's most powerful machine learning facilities, is used throughout the course.

### Data Analysis and Statistical Inference (2017)

DataCamp

This interactive DataCamp course complements the Coursera course Data Analysis and Statistical Inference by Mine Ã‡etinkaya-Rundel. In this course, I learnt Introduction to R, Data, Probability, Foundations for inference: Sampling distributions, Confidence intervals, Inference for numerical data, Inference for categorical data, Introduction to linear regression and Multiple linear regression.

### Statistical Modeling in R (2017)

DataCamp

I learnt what modeling is and what it's used for, R tools for constructing models, using models for prediction (and using prediction to test models), and how to account for the combined influences of multiple variables. By using computing and concepts from machine learning, It made me able to leapfrog many of the marginal and esoteric topics encountered in traditional 'regression' courses.

## Penerbitan

### Laser Marks Detection from Fundus Images

Eye diseases such as diabetic retinopathy may cause blindness. At the advanced stages of diabetic retinopathy, further disease progression is stopped using laser treatment. These laser marks hinders the further analysis of the retinal images so it is desirable to detect laser marks & remove them to avoid any unnecessary processing. This paper presents a method to automatically detect laser marks from the retinal image and present some results based on the performance evaluation.

### Detection of Laser Marks from Retinal Images for Improved Diagnosis of Diabetic Retinopathy

Proposed a method that uses techniques from image processing and machine learning to help segment out the laser marks from the retinal images. The system uses Minimum distance classifier using a feature set extracted from the laser marks in retinal images. The evaluation of the proposed system is done on a locally gathered data set of patients suffering from different Retinal diseases. Results of the system are based on various parameters like accuracy, specificity and sensitivity.

### Automated Computer Aided Detection of Cataract

CAD systems with their mobility of usage in low resource settings can be very useful for the detection of cataract. For the detection of cataract through CAD system, the picture of the patient with retina in focus, is accessed and our proposed method localizes the iris and pupil of the eye. Texture analysis of the obtained image is performed enabling us to tell whether the eye is normal or it has cataract. The system is developed and tested using locally gathered dataset of cataract.

## Sijil

• US English Level 1
77%

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