Machine Learning, Deep Learning, Natural Language Process, Python

D VEERA VENKATESWARLU

******************@*****.*** +91-955*******.

https://github.com/venkateshdudipalli Thripuranthakam,

www.linkedin.com/in/venkatesh-dudipalli-955******* Prakasham district,

https://medium.com/@venkateshdudipalli Andra Pradesh.

To obtain an entry level position at an organization that gives me an opportunity to learn and implement new skills and technologies for the betterment of the organization.

CAREER SNAPSHOT

Have strong knowledge in Machine Learning, Natural Language Process with Python and Deep Learning with Tensorflow

Good knowledge of K-Nearest Neighbor, Naïve Bayes, Logistic Regression, Linear Regression, SVM, Decision Tree, Random Forest, PCA, t-SNE, and Data Mining algorithms with python.

Proficient knowledge in Linear Algebra, Probability & Statistics and Calculus.

Time series analysis, Recommended Systems Machine learning with python and also Data Science with Pandas in machine learning with python.

Identifying suitable large datasets for different Machine Learning algorithms.

Performing data analysis, visualization, feature extraction, feature selection and feature engineering using python pandas, NumPy.

Involving in Training and Testing the Machine Learning Supervised and Unsupervised models.

Implementing Classification using supervised algorithms like Logistic Regression, Decision trees, KNN, Naive Bayes..

EDUCATION

B.Tech in Machanical from DJR College of Engineering and Technology (JNTUK- 2018) Vijayawada, Andhra Pradesh.

Inter(M.P.C) from GVR & S Co-Operative Jr.College (2014), Guntur, Andhra Pradesh.

S.S.C from Sri Vani and Rama High School (2012), Medapi, Andhra Pradesh.

KEY AREAS OF KNOWLEDGE

Python, Machine Learning, Deep Learning, Natural Language Process, TensorFlow, Keras, NumPy, SciPy, Pandas, Seaborn, Matplotlib, Plotly, Scikitlearn, NLTK, OpenCV, Spacy, Gensim, MySQL.

CERTIFICATION

Applied Machine Learning Course from Applied AI Course.

https://drive.google.com/file/d/1mm_SpJtdw5wB9TjsCRDwORHXcPqPfffs/view?usp=sharing

PROJECT DEATAILS

AMAZON FINE FOOD REVIEWS ANALYSIS

Machine learnig & Deep learning –Binary Class Classification Task / Dec 2018 - Jan 2019

Build the models to determine given a review whether the review is positive (rating of 4 or 5) or negative (rating of 1 or 2) using Machine learning algorithms like t-SNE, KNN, Naive Bayes, Logistic Regression, Linear SVM, Decision Tree, Random Forest, XGboost, Clustering, Truncated SVD,and Deep learning algorithm LSTM

PERSONALIZED CANCER DIAGNOSIS

Machine learning – Multi Class Classification Task / Feb 2019 - Feb 2019

Build models to successfully predict the class of cancer, given the 'Gene', 'Variation' and the corresponding text in the literature

N EWYORK TAXI DEMAND PREDICATION

Machine learning-Regression Task / Feb 2019 - Mar 2019

Scrapped, Cleaned and assimilated New York Taxi Data to predict the number of pickups in a given location Using a Regression model.

Visualized Clustering, Time-binning, Time-series data, Fourier Transform and other factors in New York Taxi Data to Build Linear, Random Forest and XGBoost Regression Algorithms and Achieved an Error Metric Less than 12%.

M ICROSOFT MALWARE DETECTION

Machine learning – Multi Class Classification Task / Mar 2019 - Mar 2019

There are nine different classes of malware that we need to build a model classify a given a data point belongs to which class

N ETFLIX MOVIE RECOMMENDATION SYSTEM

Machine learning-Regression Task / Mar 2019 – Apr 2019

Designed a Machine Learning Algorithm which can predict the rating that a user would give to a movie that he has not yet rated by minimizing the difference between predicted and actual rating and achieved Root Mean Square Error of 1.08.

S TACK OVERFLOW: TAG PREDICTION

Machine learning – Multi Label Classification Task / Apr 2019 - Apr 2019

Predicted Tags by using Bag of Words, Tt-idf Vectorizer and Classified the Tags Using Logistic, linear regression models and achieved an Accuracy of 0.22.

Q UORA QUESTION PAIRS

Machine learning – Binary Class Classification Task / Apr 2019 - May 2019

There are nine different classes of malware that we need to build a model classify a given a data point belongs to which classc

H UMAN ACTIVITY RECOGNITION

Deep learning – Multi Class Classification Task / May 2019 - May 2019

Built a model that predicts the human activities such as Walking, Walking Upstairs, Walking Downstairs, Sitting, Standing or Laying and implemented ‘LSTM’Layered model with dropout and regularization on the dataset, which is gathered from gyroscope and accelerometer.

SOCIAL NETWORK GRAPH LINK PREDICTION - FACEBOOK CHALLENGE

Machine learning –Binary Class Classification Task / May 2019 – Jun 2019

Build a model given a directed social graph, have to predict missing links to recommend users (Link Prediction in graph)

A MAZON APPAREL RECOMMENDATION

Machine learning – Multi Class Classification Task / Jun 2019 - Jul 2019

Build a model Recommending similar products (apparel) to the given product (apparel) in any e-commerce websites.

S ELF DRIVING CAR

Deep learning-Regression Task / Jun 2019 – Jul 2019

Built an End to End Deep learning Network to Predict the steering angle with the help of 25 minutes of video footage.

Used Convolutional Neural Networks (CNNs) to map the raw pixels from a front-facing camera to the steering commands for a self-driving car.

DONORS CHOOSE

Deep learning – Binary Class Classification Task / Jul 2019 - Aug 2019

Build a model is to predict whether or not a DonorsChoose.org project proposal submitted by a teacher will be approved, using the text of project descriptions as well as additional metadata about the project, teacher, and school. DonorsChoose.org can then use this information to identify projects most likely to need further review before approval.

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