Machine Learning

The way that R-squared shouldn’t be utilized for choosing if you have a satisfactory model is illogical and is once in a while clarified unmistakably. This exhibit diagrams how R-squared integrity of-fit functions in relapse investigation and relationships while demonstrating why it’s anything but a proportion of measurable sufficiency, so ought not to propose anything about future prescient execution. The R-squared Goodness-of-Fit measure is one of the most broadly accessible insights going with the yield of relapse investigation in factual programming. Maybe incompletely because of its far-reaching accessibility, it is additionally one of the frequently misjudged ones. Initial, a concise update on R-squared (R2). In a relapse with a solitary free factor, R2 is determined as the proportion between the variety clarified by the model and the all-out watched variety. It is regularly called the coefficient of assurance and can be deciphered as the extent of variety clarified by the presen...

Check out these 5 cool Python libraries that the author has come across during an NLP project, and which have made their life easier. Python is a best friend for the majority of the Data Scientists. Libraries make their life simpler. I have come across five cool Python libraries while working on my NLP project. This helped me a lot and I would like to share the same in this article. 1. Numerizer Amazing library to convert text numerics into int and float. Useful library for NLP projects. For more details, please check PyPI and this Github repo . Installation !pip install numerizer Example #importing numerize library from numerizer import numerize#examplesprint(numerize(‘Eight fifty million’)) print(numerize(‘one two three’)) print(numerize(‘Fifteen hundred’)) print(numerize(‘Three hundred and Forty five’)) print(numerize(‘Six and one quarter’)) print(numerize(‘Jack is having fifty million’)) print(numerize(‘Three hundred billion’)) Output 2. Missingo It is widespr...

Developing machine learning predictive models from time-series data is an important skill in Data Science. While the time element in the data provides valuable information for your model, it can also lead you down a path that could fool you into something that isn’t real. Follow this example to learn how to spot trouble in time series data before it’s too late. Time series forecasting is an important area of machine learning. It is important because there are so many prediction problems that involve a time component. However, while the time component adds additional information, it also makes time series problems more difficult to handle compared to many other prediction tasks. Time series data, as the name indicates, differ from other types of data in the sense that the temporal aspect is important. On a positive note, this gives us additional information that can be used when building our machine learning model — that not only the input features contain useful information, but ...

Introduction What profession did Harvard call the Sexiest Job of the 21st Century ? That’s right… the data scientist . Ah yes, the ever-mysterious data scientist. So what exactly is the data scientist’s secret sauce, and what does this “sexy” person do at work every day? This article is intended to help define the data scientist role, including typical skills, qualifications, education, experience, and responsibilities. This definition is somewhat loose since there isn’t a standardized definition of the data scientist role, and given that the ideal experience and skill set are relatively rare to find in one individual. This definition can be further confused by the fact that there are other roles sometimes thought of as the same, but are often quite different. Some of these include data analyst , data engineer , and so on. More on that later. A data scientist’s level of experience and knowledge in each often varies along a scale ranging from beginner, to proficient , and to...

60 Types of Data Visualization and their Usage To know the visualization you can see at   https://lnkd.in/fzHe8QC  . To know how to Implement Data Visualization in Business? https://lnkd.in/fYUCzgC For practical but less technical resource you can see links below  Know Data Science  Datanest on LinkedIn: "Want to know more on the real-world examples on data science implementation? You… March 26, 2019: Datanest posted on LinkedIn lnkd.in 2. Understand How to answer Why  Datanest on LinkedIn: "To gain value from analytics, we not only need to answer what, but also why… April 3, 2019: Datanest posted on LinkedIn lnkd.in 3. Know Machine Learning Key Terminology https://lnkd.in/fCihY9W   4. Understand Machine Learning Implementation  Datanest on LinkedIn: "Confuse how to convert machine learning to business solution. This is our 7… May 2, 2019: Datanest posted on LinkedIn lnkd.in 5. Machine Learning Applications on Marketing  ...

The Main Courses out of which each person has to do so, to get into Machine Learning are: 1. Machine Learning Certification by Stanford University (Coursera) 2.  Deep Learning Specialization by deeplearning.ai (Coursera) 3. Machine Learning DataScience Certification from Harvard University (edX) 4. Machine Learning DataScience Certification from IBM (Coursera) 5. Machine Learning with Tensorflow on Google Cloud Platform 6. Machine Learning Certification by University of Washington (Coursera) 7. Machine Learning Training A-Z: Hands-On Python and R for Data Science (Udemy) 8. Mathematics of Machine Learning  (Coursera) 9. Data Science Specialization - John Hopkins University  (Coursera) 10. Python for Data Science and Machine Learning (Udemy) 11. Deep Learning Training A-Z: Hands-On Python and R for Data Science (Udemy) 12. Introduction to Machine Learning (IIT - KGP) by Prof Sudeshna Sarkar (NPTEL) 13. Introduction to Machine Learning (IIT - M) by Prof. Balaraman Ravindr...