This episode discusses in detail how to approach a project, how to start with a project. This will guide you, motivate you to start your own Machine Learning Project without having to worry much about subject knowledge or domain expertise.
The second part covers the sources where you can find the data and start to work with it. The various site can be found in the link below:
UCI 
https://archive.ics.uci.edu/ml/index.php
Kaggle
https://www.kaggle.com/priyanka841/datasets

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How to Approach Project and Sources

This Podcast is created for those who are taking their first step in Machine Learning, those of you who want to brush up the concepts of Machine Learning, learn in a very simple, easy, and interesting manner.

The episodes cover the basic concepts of Machine Learning, getting familiar with models, algorithms, other technical concepts
.
Learn, update your self while you are travel, jog, exercise, or relax.

The VISION is" Education for Empowerment".
Making Machine Learning accessible to all, willing to learn, grow with Technology and Time.

Education

Training

Language Courses

Higher Education

K-12

Educational Technology

Hi
So if anyone wants to collaborate, I will be glad to offer my assistant. 
Reach me out
sharma25prianca@gmail.com
Instagram machinemantra
YouTube Channel: Machine Learning by Priyanka Sharma

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Collaboration

The episode explains what is meant by predictive modeling and predictive analysis in Machine Learning, bringing out the differences between the two.

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Predictive Modeling and Predictive Analysis in Machine Learning

The episode discusses why R and Python are really popular languages in the Data Science community. The similarity and differences between the two languages in an elaborate manner.



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R and Python Comparison

The episode discusses what is feature engineering in Machine Learning, what are various techniques used in Machine Learning like Imputation, Scaling, One hot encoding, and much more.
Website: https://machinemantra.in/
YouTube: https://www.youtube.com/channel/UCh3IeGHegUhWPmxNG0EYebw?view_as=subscriber

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Feature Engineering in Machine Learning

This episode discusses various types of Regression Metrics in Machine Learning. Most common Regression metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), RMSE, R squared, and Adjusted R squared regression metrics. There is further a little discussion upon which Regression Metric to use for evaluating the Regression Problems, pros and cons of the above-mentioned Metrics.
YouTube:https://www.youtube.com/channel/UCh3IeGHegUhWPmxNG0EYebw?view_as=subscriber
Website: https://machinemantra.in/



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Send in a voice message: https://podcasters.spotify.com/pod/show/priyanka-sharma12/message

How to Approach Project and Sources

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