Description This Course Cover Topics such as Python Basic Concepts, Python Advance Concepts, Numpy Library, Scipy Library, Pandas Library, Matplotlib Library, Seaborn Library, Plotlypy Library, Introduction to Data Science and steps to start Project in Data Science, Case Studies of Data Science and Machine Learning Algorithms such as Linear, Logistic, SVM, NLP This is best course for any one who wants to start career in data science.

He also launched the IT Department's placement drive on the occasion. Addressing the participants, Vice-Chancellor, Prof. Mehraj-ud-Din Mir, said the "Digitization and internet technologies have already become an integral part of human lives including academics even before the Covid-19 outbreak and the pandemic has further established the fact that internet and associated technologies have a major stake and role in the future." He asked the Departments of IT, Media Studies and Law to join hands and provide solutions to the issues confronted by the masses, through technological intervention. Prof. Mehraj ud Din Mir encouraged the students to take part in training and workshops that will help them to develop employable skills that are in demand. Regarding the placement drive, he said, "The placement cell constituted for the purpose will help our students to launch their careers in the direction they intend to."

This course will cover how you can use NLP to do stuff. The idea is we make short videos that focus on the aspects of NLP that currently work well and are useful. Speech-to-text now works pretty well, so these methods will also be useful for the audio portions of videos.

Hands-on Training with "7 Stages of Machine Learning ... New What you'll learn Description " We will shift from a mobile first to an AI first world." AI will transform every industry similar to electricity over 100 years ago and have a huge impact on how humans live and work in the future. Moving into Data Science is an amazing career choice. There's high demand for Data Scientists across the globe and people working in the field enjoy high salaries and rewarding careers. For instance, average annual salaries are around $125,000 in America and ₹14 lacs in India.

Trey Research Inc. delivers innovative solutions for manufacturers. They specialize in identifying and solving problems for manufacturers that can run the range from automating away mundane but time-intensive processes to delivering cutting edge approaches that provide new opportunities for their manufacturing clients. Trey Research is looking to provide the next generation experience for connected car manufacturers by enabling them to utilize AI to decide when to pro-actively reach out to the customer thru alerts delivered directly to the car's in-dash information and entertainment head unit. For their PoC, they would like to focus on two maintenance related scenarios. In the first scenario, Trey Research recently instituted new regulations defining what parts are compliant or out of compliance.

Anomaly detection can be treated as a statistical task as an outlier analysis. But if we develop a machine learning model, it can be automated and as usual, can save a lot of time. There are so many use cases of anomaly detection. Credit card fraud detection, detection of faulty machines, or hardware systems detection based on their anomalous features, disease detection based on medical records are some good examples. There are many more use cases.

The course is designed specifically for aspiring machine learning and data science professionals and takes individuals with zero industry experience, …

It has long been recognized that academic success is a result of both cognitive and non-cognitive dimensions acting together. Consequently, any intelligent learning platform designed to improve learning outcomes (LOs) must provide actionable inputs to the learner in these dimensions. However, operationalizing such inputs in a production setting that is scalable is not trivial. We develop an Embibe Score Quotient model (ESQ) to predict test scores based on observed academic, behavioral and test-taking features of a student. ESQ can be used to predict the future scoring potential of a student as well as offer personalized learning nudges, both critical to improving LOs. Multiple machine learning models are evaluated for the prediction task. In order to provide meaningful feedback to the learner, individualized Shapley feature attributions for each feature are computed. Prediction intervals are obtained by applying non-parametric quantile regression, in an attempt to quantify the uncertainty in the predictions. We apply the above modelling strategy on a dataset consisting of more than a hundred million learner interactions on the Embibe learning platform. We observe that the Median Absolute Error between the observed and predicted scores is 4.58% across several user segments, and the correlation between predicted and observed responses is 0.93. Game-like what-if scenarios are played out to see the changes in LOs, on counterfactual examples. We briefly discuss how a rational agent can then apply an optimal policy to affect the learning outcomes by treating the above model like an Oracle.

Tutorial Description: This tutorial is for absolute beginners who want themselves to get introduced with Scikit-Learn and begin with coding in Machine Learning.

Python is the most used language in machine learning. Engineers writing machine learning systems often use Jupyter Notebooks and Python together. Jupyter Notebooks is a web application that allows experimentation by creating and sharing documents that contain live code, equations, and more. Machine learning involves trial and error to see which hyperparameters and feature engineering choices work best. It's useful to have a development environment such as Python so that you don't need to compile and package code before running it each time.