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All-in-One: Machine Learning, DL, NLP [Hindi][Python]

All-in-One: Machine Learning, DL, NLP [Hindi][Python]

Udemy Course Coupon ED | All-in-One: Machine Learning, DL, NLP [Hindi][Python]
Complete hands-on Machine Learning Course with Data Science, NLP, Deep Learning and Artificial Intelligence
Created by Rishi Bansal

What you'll learn

  • Master in creating Machine Learning Models on Python
  • Visualizing various ML Models wherever possible to develop a better understanding about it.
  • How to Analyse the Data, Clean it and Prepare (Data Preprocessing Techniques) it to feed into Machine Learning Models.
  • Learn the most Basic Mathematics behind Simple Linear Regression and its Best fit line.
  • What is Gradient Descent, how it works Internally with full Mathematical explanation.
  • Make predictions using Simple Linear Regression, Multiple Linear Regression.
  • Make predictions using Logistic Regression, K-Nearest Neighbours and Naive Bayes.
  • Fundamental Concept of Deep Learning and Natural Language Processing. Python Code is include at some place for explanation.

Description
This course is designed to cover maximum Concept of Machine Learning.  Anyone can opt for this course. No prior understanding of Machine Learning is required. 
NOTE: Course is still under Development. You will see new topics will get added regularly.
Now question is why this course?
This Course will not only teach you the basics of Machine learning and Simple Linear Regression. It will also cover in depth mathematical explanation of Cost function and use of Gradient Descent for Simple Linear Regression. Understanding these is must for a solid foundation before entering into Machine Learning World. This foundation will help you to understand all other algorithms and mathematics behind it.

As a Bonus Introduction Natural Language Processing is included.

Below Topics are covered till now.
Chapter - Introduction to Machine Learning
  1. - Machine Learning?
  2. - Types of Machine Learning
Chapter - Data Preprocessing
  1. - Null Values
  2. - Correlated Feature check
  3. - Data Molding
  4. - Imputing
  5. - Scaling
  6. - Label Encoder
  7. - On-Hot Encoder

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