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high-dimensional-statistics

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-course

IBM Data Science Professional Certificate

ibm-data-science-professional-certificate

IBM Data Science Professional Certificate - Coursera Specialization by IBM (https://www.coursera.org/professional-certificates/ibm-data-science)

ibm-data-science-professional-certification

This repository contains all the resources and solution to quizzes given and asked in IBM Data Science Professional Certification.

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.

ibm-ds-certificate-notes

ibm-machine-learning-with-python

This repository contains all the coding excerises required to achieve the certificate for the, IBM: ML01010EN Machine Learning with Python: A Practical Introduction, course.

ibm_cybersecurity_notes

Notes for the IBM Cybersecurity Analyst Certification

ibm_data_science_project

IBM Data Science Certification

ibmcertif

ibm datascience certification exam code

ibmdatasciencenotebooks

Archive Of Labs for IBM DataScience Certification

interview-preparation

Awesome list and code for Interview Preparation based on HackerRank, LeetCode, etc. on Python and C++

intro-python

Python pour Statistique et Science des Données -- Syntaxe, Trafic de Données, Graphes, Programmation, Apprentissage

intro-r

R pour Statistique et Science des Données -- Démarrer, syntaxe, graphes, éléments de programmation

io19

jupyter-notebooks

Jupyter Notebooks and miscellaneous

lasagne

Lightweight library to build and train neural networks in Theano

learning-python-for-data-analysis-and-visualization

Learning Python for Data Analysis and Visualization course Exercises in Pyhon 3

leetcode-solutions

🏋️ Python / Modern C++ Solutions of All 2122 LeetCode Problems (Weekly Update)

m220p

Learn the essentials of Python application development with MongoDB.

machine-learning

Awesome list (courses, books, videos etc.) and implementation of Machine Learning Algorithms

machine-learning-1

Machine learning: Practical applications

machine-learning-certificate

ibm_coursera

machine-learning-course-octave

Based on Machine Learning course by Andrew Ng, Stanford University

machine-learning-sales-forcast-by-seasons-

Machine learning (Sales Forcast by Seasons)

machine-learning-stanford-university-coursera

Contains the Course Material and Assignment Solutions for the Machine Learning Course at Stanford University on Coursera.

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).