akhilvydyula Goto Github PK

followers: 49.0 following: 225.0 repos: 202.0 gists: 1.0

Name: Akhil Vydyula

Type: User

Company: PwC

Bio: 📚 Instructor & Author 💻 Senior Data Engineer 🏆 Top 0.01% in Machine Learning 🔗 Check my courses on ML: [https://tinyurl.com/muxc3hrd](https://tinyu

Twitter: akhilvydyula

Location: Hyderabad

Blog: https://www.udemy.com/user/akhil-vydyula/

नमस्ते (Namaste)🙏🏻, I'm Akhil Vydyula

Working at Atos

connect with me:

A little more about me...

const Akhil Vydyula = {
    pronouns: "He" | "Him",
    code: ["Python", "C", "C++"],
    askMeAbout: ["NLP", "ML", "DevOps","Computer Vision","photography"],
    technologies: {
        frameworks: {
            native: ["django", "flask"],
            Libraries: ["pytorch","tensorflow","spacy","keras","rasa","opencv","skimage",...]
        },
        devOps: ["AWS", "Docker🐳","chef"],
        databases: ["postgresql"]
    },
    currentFocus: "Machine Learning with GCP",
    funFact: "There are two ways to write error-free programs; only the third one works"
};

⛅ Currently, I am exploring cloud and serverless technology through projects.

🛠 Tech Stack

My Favourite Languages:

My Top Skills:

Web Development

Android Development

⚙️ GitHub Analytics

Akhil Vydyula's Projects

-personalized-cancer-diagnosis

This Repo Contains all the exercise files for Data Science Course of 365 Datascience . The repo is split into the relevant folders & there is one exercise folder which contains all the files of that course. Don't forget to star it :D

advanced-nlp-with-tensorflow-2

Advanced NLP with TensorFlow 2, published by [Packt]

ai

Microsoft AI

ai-for-healthcare-nanodegree

Learn to build, evaluate, and integrate predictive models that have the power to transform patient outcomes. Begin by classifying and segmenting 2D and 3D medical images to augment diagnosis and then move on to modeling patient outcomes with electronic health records to optimize clinical trial testing decisions. Finally, build an algorithm that uses data collected from wearable devices to estimate the wearer’s pulse rate in the presence of motion.

aiml

Course work from AIML by Great Learning

aiml-projects

Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning

akhil-vydyula-portfoliowebsite

this is my personal website

akhilvydyula

amazon-fine-food-review

Machine learning algorithm such as KNN,Naive Bayes,Logistic Regression,SVM,Decision Trees,Random Forest,k means and Truncated SVD on amazon fine food review

amazon-fine-food-reviews

Amazon Fine Food Reviews is classification Sentiment Analysis problem. Classify the positive and negative reviews given by Amazon users. Given some product-based features and related reviews in text data. Featuring data and apply various Machine Learning techniques to classify reviews.

amazon-fine-food-reviews-sentiment-analysis

Amazon_fine_food_review Introduction The Amazon Fine Food Reviews dataset consists of 568,454 food reviews. This dataset consists of a single CSV file, Reviews.csv Data Set Click here to get the dataset. Review.csv - 251MB Dataset statistics Number of reviews 568,454 Number of users 256,059 Number of products 74,258 Users with > 50 reviews 260 Median no. of words per review 56 Timespan Oct 1999 - Oct 2012 Data Fields Explanation Id - Unique row number ProductId - unique identifier for the product UserId - unqiue identifier for the user ProfileName HelpfulnessNumerator - number of users who found the review helpful HelpfulnessDenominator - number of users who indicated whether they found the review helpful Score - rating between 1 and 5 Time - timestamp for the review Summary - brief summary of the review Text - text of the review EDA Objective Analysing the data & plot the required graphs to show that these conclusions are true: a. Positive reviews are very common. b. Positive reviews are shorter. c. Longer reviews are more helpful. d. Despite being more common and shorter, positive reviews are found more helpful. e. Frequent reviewers are more discerning in their ratings, write longer reviews, and write more helpful reviews Note: This notebook is highly inspired from the Exploratory visualization of Amazon fine food reviews by Rob Castellano. Model Building STEP-1: Copy the data in Pandas DataFrame and drop unwanted columns. STEP-2: Text Preprocessing. a. Converting to lower-case. b. Removing HTML Tags. c. Removing Special Characters. d. Removing Stop Words. e. Stemming (Snowball Stemming) STEP-3: Vectorizing out Data Set STEP-4: Building and evaluating the model a. Naive Bayes b. Logistic Regression - with L1 and L2 regularizors c. Linear SVM d. RBF Kernel SVM

amexpert_ml_hackathon_2019_winners_soultions

apparel-recommendation

applications-of-statistical-learning-with-python

Code repository for Applications of Statistical Learning with Python, Published By Packt

applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

artificial-intelligence-and-machine-learning

Algorithm implementations and homework solutions for the Stanford's online courses

artificial-intelligence-deep-learning-machine-learning-tutorials

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.