Pinned Repositories
Capstone_movilens
Movilens project under Capstone course from Harvard on Data Science (Harvard: PH125.9x)
Capstone_own_project
Repository for capstone own project o Harvard
Coursera_Capstone
IBM data Science Certificate
Coursera_Capstone-1
Contains the coursera capstone project
Data-Science-Books-1
Data-Science-projects-portfolio
Serie of projects for dtaascience curriculum
edX-6.86x-machine-learning
Machine Learning with Python (url: https://courses.edx.org/courses/course-v1:MITx+6.86x+1T2020/course/)
Julia
Julia_scientific_programming
Machine-Learning
HarvardX: PH125.8x Data Science: Machine Learning
bidentity's Repositories
bidentity/Capstone_movilens
Movilens project under Capstone course from Harvard on Data Science (Harvard: PH125.9x)
bidentity/Capstone_own_project
Repository for capstone own project o Harvard
bidentity/Coursera_Capstone
IBM data Science Certificate
bidentity/Coursera_Capstone-1
Contains the coursera capstone project
bidentity/Data-Science-Books-1
bidentity/Data-Science-projects-portfolio
Serie of projects for dtaascience curriculum
bidentity/edX-6.86x-machine-learning
Machine Learning with Python (url: https://courses.edx.org/courses/course-v1:MITx+6.86x+1T2020/course/)
bidentity/Julia
bidentity/Julia_scientific_programming
bidentity/Machine-Learning
HarvardX: PH125.8x Data Science: Machine Learning
bidentity/MITx_6.86x_Machine_Learning_with_Python-From_Linear_Models_to_Deep_Learning_Fall_2020
Welcome to 6.86x Machine Learning with Python–From Linear Models to Deep Learning. Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk. As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control. In this course, you will learn about principles and algorithms for turning training data into effective automated predictions. We will cover: Representation, over-fitting, regularization, generalization, VC dimension; Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning; On-line algorithms, support vector machines, and neural networks/deep learning. You will be able to: Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models Choose suitable models for different applications Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering You will implement and experiment with the algorithms in several Python projects designed for different practical applications. You will expand your statistical knowledge to not only include a list of methods, but also the mathematical principles that link these methods together, equipping you with the tools you need to develop new ones.
bidentity/Money-and-Banking-for-Statistics-and-International-Business-Students
This depository is for Chinese students majored in Statistics and International Business who are enrolled in Money and Banking in 2018.
bidentity/rstudio_data_science_harvard
data science professional certificate
bidentity/UiPath-Advanced-Training-Assignment-1
bidentity/uphold-sdk-python
An SDK for the Uphold API.