RUSHIKESH AGGADI - ML Engineer Resume Summary

RUSHIKESH AGGADI

Mobile: +91-738******* Email: ****************@*****.***

LinkedIn: https://www.linkedin.com/in/aggadi-rushikesh GitHub: https://github.com/rushikesh01aiml

PROFILE SUMMARY:

I am a motivated fresher skilled in machine learning with a solid grasp of machine learning algorithms, supervised learning, unsupervised learning, and deep learning. Strong analytical and problem-solving abilities, coupled with effective communication skills. Knowledgeable in machine learning model development. I am eager to leverage my skills as a machine learning engineer to deliver innovative and high-quality solutions.

EDUCATION:

National Institute for Micro, Small, and Medium Enterprises

(ni-msme)-Hyderabad 2023-2024

Artificial Intelligence and Machine Learning Jr. Data Analyst (AI Data Science) Vivekananda Institute of Technology & Science (N6-V VITS)-Karimnagar 2017-2020 B. Tech (Civil Engineering) CGPA: 7.79

Vaageswari College of Engineering-Karimnagar 2014-2017 Diploma (Civil Engineering) % of marks: 85.06

PROJECTS:

Gold Price Prediction Machine learning model development, Google Colab, Python, Scikit library, Random Forest

- The "Gold Price Prediction" project focuses on predicting the prices of gold using machine learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn (sklearn), Matplotlib, Seaborn, Random Forest Regressor, and others, this project provides a comprehensive solution for accurate price estimation.

Convolution Neural Network Deep learning model development, Google Colab, Python, libraries, algorithm, TensorFlow, Keras

- The dataset in the project includes the pixel values of images of objects, and labels include numbers. Each object is represented by a number. We will train the network first and check its accuracy using the test dataset. There is a lot more mathematics involved. Especially for the parameter update. But luckily, we have TensorFlow.

SKILLS:

Technical Skills:

• Programming languages: Python

• Mathematics and Statistics: linear algebra, calculus, and statistics.

• Machine Learning Algorithms: supervised learning (e.g., regression, classification), unsupervised learning (e.g., clustering, association, dimensionality reduction), and deep learning.

• Data Preprocessing: Cleanse, preprocess, and convert raw data into an appropriate format for machine learning.

• Data Visualization: Libraries: Matplotlib, Seaborn, or Ploty.

• Machine Learning Framework: Machine learning libraries and frameworks like scikit-learn, Tensorflow, and Keras.

• Feature Engineering: Features from raw data to improve model performance.

• Deep Learning: Deep learning architectures and frameworks tasks: neural networks, CNN, and reinforcement.

• Natural Language Processing: Sentiment Analysis, Bag of Words, and TFIDF.

• Machine Learning Model Pipeline.

• Developer Tools: Google Colab, Jupyter Notebook, VS Code, Soft skills: flexibility, problem-solving, patience, adaptability, communication, time management, and leadership.

CERTIFICATIONS:

Entrepreneurship and Skill Development Programme (ESDP) on Artificial Intelligence and Machine Learning Jr. Data Analyst (AI Data Science), 2023–24 Sponsored by the Ministry of MSME, Government of India.

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