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venkatlovenkatesh/README.md

Venkatesh

  • As an Artificial Intelligence Enthusiast,Through a combination of academic courses and self-driven projects, I have established a solid skill set in machine learning, deep learning, generative AI, and data science. My educational background has given me a strong theoretical basis, and I've been able to use these ideas in a variety of practical contexts through side projects. I now have practical expertise with a variety of AI models and approaches for jobs like computer vision, natural language processing, and the creation of original content. I have developed competence in data science approaches, such as data collecting, preprocessing, exploratory analysis, and predictive modeling, in addition to my focus in artificial intelligence. I regularly participate in AI communities and attempt to contribute to the field since I am driven by an insatiable curiosity.

Contact Info / Profiles:

Skills

Python:

  • Proficient in Python programming language with expertise in object-oriented programming (OOP) principles and modular programming.

Data Structures and Algorithms:

  • Experienced in implementing and analyzing efficient data structures and algorithms for solving complex problems.

Machine Learning:

  • Skilled in developing and deploying both supervised and unsupervised learning algorithms, including principal component analysis (PCA), and well-versed in probability and statistics concepts.

Data Science:

  • Proficient in data analysis, data visualization, exploratory data analysis (EDA), data modeling, data engineering, pattern recognition, and predictive modeling techniques.

Deep Learning:

  • Experienced in building and training deep learning models, including artificial neural networks (ANN), convolutional neural networks (CNN), recurrent neural networks (RNN) such as gated recurrent units (GRU) and long short-term memory (LSTM), image processing, transfer learning, and hyperparameter tuning.

Natural Language Processing (NLP):

  • Skilled in NLP techniques such as tokenization, stemming, lemmatization, bag of words, and word2vec.

Libraries and Frameworks:

  • Proficient in using various libraries and frameworks, including TensorFlow, Scikit-learn, NLTK, Keras, PyTorch, Pandas, NumPy, OpenCV, Streamlit, and MediaPipe.

MLOps:

  • Experienced in model monitoring, model tracking, model packaging, containerization, model deployment, version control systems, and continuous integration/continuous deployment (CI/CD) practices.

Generative AI:

  • Knowledgeable in large language models (LLM), Langchain, retrieval-augmented generation (RAG), fine-tuning, Langserve, prompt engineering, and Langsmith.

Cloud Platforms:

  • Proficient in working with cloud platforms such as AWS, Azure, and Google Cloud Platform.

Tools:

  • Skilled in using various tools, including Git, MLflow, Microsoft Excel, Docker, and Power BI.

Databases:

  • Experienced in working with databases such as MongoDB (NoSQL) and MySQL (relational).


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  1. Blog_generator Blog_generator Public

    This is a generative AI project , developed using the local llama 2 model which is a open source model, and i used the streamlit for the user interface.

    Python

  2. potato-blight-prediction potato-blight-prediction Public

    This project aims to predict potato blight outbreaks using deep learning techniques. Potato blight, caused by the Phytophthora infestans pathogen, can significantly impact crop yields if not detect…

    Jupyter Notebook