ravipm260372,github

a-guide-to-machine-learning-in-r

A series of articles to get started into the field of Machine Learning with R language

ai4astromasterclass

A Jupyter notebook containing a machine learning tutorial for Pulsar classification. The notebook was written to support a masterclass delivered to students at Edge Hill University on November 14th 2019.

analytics-edge-mitx-

In the last decade, the amount of data available to organizations has reached unprecedented levels. Data is transforming business, social interactions, and the future of our society. This course is about how to use data and analytics to give an edge to your career and your life. Real world examples of how analytics have been used to significantly improve a business or industry is being addressed. These examples include Moneyball, eHarmony, the Framingham Heart Study, Twitter, IBM Watson, and Netflix. Following analytics methods has been used: linear regression, logistic regression, trees, text analytics, clustering, visualization, and optimization. Statistical software R and a spreadsheet software to build models and work with data is used. The contents of this course are essentially the same as those of the corresponding MIT class (The Analytics Edge). It is a challenging class, but it enabled to apply analytics to real-world applications.

applied-data-science-with-python-specialization

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order.

basic-mathematics-for-machine-learning

The motive behind Creating this repo is to feel the fear of mathematics and do what ever you want to do in Machine Learning , Deep Learning and other fields of AI