Faizy (mohd-faizy)

mohd-faizy

Geek Repo

Location:New Delhi

Home Page:https://mohdfaizy.com/

Twitter:@F4izy

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Faizy's repositories

Probabilistic-Deep-Learning-with-TensorFlow

Probabilistic Deep Learning finds its application in autonomous vehicles and medical diagnoses. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real-world datasets.

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06P_Sentiment-Analysis-With-Deep-Learning-Using-BERT

Finetuning BERT in PyTorch for sentiment analysis.

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learn_python

This is a public repository of Jupyter notebooks with introductory tutorials on different aspects of Python programming. Please star us if you think it is useful:

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CAREER-TRACK-Data-Scientist-with-Python

This Repo contains tools that allow us to import, clean, manipulate, and visualize data —Includes Python libraries, like pandas, NumPy, Matplotlib, and many more to work with real-world datasets to learn the statistical and machine learning techniques.

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DataScience-Projects

Projects : DataScience, Artificial intelligence, Machine learning, Deep Learning

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Machine-Learning-Scientist-with-Python

Machine Learning - - Supervised, Unsupervised, and deep learning. Processing data for features, training models, assess performance, and tune parameters for better performance. In the process, you'll get an introduction to natural language processing, image processing, and popular libraries such as Spark and Keras.

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ML-DS-OneNote

Machine-Learning/Data-Science in one Notebook

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01P_Project_Deep_Learning_for_Traffic_Sign_Classification

Traffic Sign Classification Using Deep Learning in Python/Keras

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LearnWebTech

WebTechnologies: HTML, CSS, JavaScript

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11P_Classification-with-Transfer-Learning-in-Keras

Classification with Transfer Learning in Keras

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12P_Fake_News_Detection_with_Machine_Learning

In this project, we will train a Bidirectional Neural Network and LSTM based deep learning model to detect fake news from a given news corpus.

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feature-engineering-hacks

This repository contains a collection of hacks and tips for feature engineering. It is a great resource for anyone who wants to learn how to improve the performance of their machine learning models.

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Learn_Matplotlib

Data visualization

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Licenses-certifications

Licenses & certifications

Natural_Language_Processing_in_Python

This repository contains the code and resources for the "Natural Language Processing in Python". This repository contains the core skills you need to convert unstructured data into valuable insights using NLP.

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NLP_Projects

Welcome to the Natural Language Processing Repository! This repository is a collection of code examples, algorithms, and resources for Natural Language Processing (NLP). NLP is a field that focuses on the interaction between computers and human language, enabling machines to understand, analyze, and generate text.

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Stats-with-Data

This repository is a resource for learning and applying statistics in data science. It contains code examples and explanations for many common statistical concepts, from descriptive statistics through regression and time series analysis.

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10P_Transfer-Learning-for-NLP-with-TensorFlow-Hub

we will use pre-trained NLP text embedding models from TensorFlow Hub, perform transfer learning to fine-tune models on real-world data, build and evaluate multiple models for text classification with TensorFlow, and visualize model performance metrics with Tensorboard.

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DataStructures_Algorithms_Python

Comprehensive collection of Python implementations and explanations for essential data structures and algorithms

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EarthquakePrediction

Deeplearning model to predict earthquake occurence based on realtime data

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Preprocess_ML

This repository hosts Python code that utilizes the Scikit-learn preprocessing API for data preprocessing. The code presents a comprehensive range of tools that handle missing data, scale data, encode categorical variables, and perform other functions.

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TensorFlow-Advanced

Functional API, Advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. Generative deep learning, Style Transfer to Auto Encoding, VAEs, and GANs.

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