C CHARAN TEJA's repositories
AI-Frameworks
Science des Données Saison 5: Technologies pour l'apprentissage automatique / statistique de données massives et l'Intelligence Artificielle
Apprentissage
Science des Données Saison 3: Apprentissage Automatique / Statistique pour l'Intelligence Artificielle
awesome-courses
:smirk: :page_facing_up: An awesome list of educational websites, YouTube playlists, channels and books about programming
awesome-courses-1
:books: List of awesome university courses for learning Computer Science!
awesome-datascience
:memo: An awesome Data Science repository to learn and apply for real world problems.
computer-science
:mortar_board: Path to a free self-taught education in Computer Science!
contractions
Fixes contractions such as `you're` to you `are`
cs-video-courses
List of Computer Science courses with video lectures.
Dataset
Download Data
datasist
A Python library for easy data analysis, visualization, exploration and modeling
High-Dimensional-Deep-Learning
Science des données Saison 4 : Apprentissage en grande dimension, Données fonctionnelles, Détection d'anomalies, Introduction au Deep Learning
IBM-AI-Engineering
IBM AI Professional Certificate
IBM-Data-Science
Respository of the practical assigments of the course IBM Data Science from coursera
IBM-Data-Science-Professional-Certification-1
Learning materials, Quizzes & Assignment solutions for the entire IBM data science professional certification. Also included, a few resources that I found helpful.
Interview-Preparation
Awesome list and code for Interview Preparation based on HackerRank, LeetCode, etc. on Python and C++
LeetCode-Solutions
🏋️ Python / Modern C++ Solutions of All 2122 LeetCode Problems (Weekly Update)
Machine-Learning
Awesome list (courses, books, videos etc.) and implementation of Machine Learning Algorithms
Machine-Learning-Tutorials
machine learning and deep learning tutorials, articles and other resources
Machine-Learning-with-Scikit-Learn-Python-3.x
In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems fall into a few categories: supervised learning, in which the data comes with additional attributes that we want to predict (Click here to go to the scikit-learn supervised learning page).This problem can be either: classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data. An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories and for each of the n samples provided, one is to try to label them with the correct category or class. regression: if the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight. unsupervised learning, in which the training data consists of a set of input vectors x without any corresponding target values. The goal in such problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization (Click here to go to the Scikit-Learn unsupervised learning page).
matplotlib-tutorial
Matplotlib tutorial for beginner
mlcourse.ai
Open Machine Learning Course
MLTraining
Machine Learning Training for Data Science and AI ... Bootcamp in progress
nlpaug
Data augmentation for NLP
Online-Courses-Learning
Contains the online course about Data Science, Machine Learning, Programming Language, Operating System, Mechanial Engineering, Mathematics and Robotics provided by Coursera, Udacity, Linkedin Learning, Udemy and edX.
opendatasets
A curated collection of datasets for data analysis & machine learning, downloadable with a single Python command
Practice-Python
Coursera Courses and practice in Python
retail-sales
This project include Exploratory Data Analysis, Time Series Data Analysis, Forecasting, and Data Visualization (Dashboard) for Retail Sales Data
SQL-Data-Analysis-and-Visualization-Projects
SQL data analysis & visualization projects using MySQL, PostgreSQL, SQLite, Tableau, Apache Spark and pySpark.