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Sunday, July 26, 2020

Fundamentals of Machine Learning [Hindi][Python] [Free 100% off premium Udemy course coupon code]

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Fundamentals of Machine Learning [Hindi][Python]

Complete hands-on Machine Learning Course with Data Science, NLP, Deep Learning and Artificial Intelligence

What you'll learn?

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

Requirements and What you should know?

  • Prior Understanding of Python is requiried with know how to operate Spyder/Jupiter Notebook for Coding.
  • For Machine Learning Concept no prerequisite. Anyone can do this course.

Who is this course for?

  • This will provide a good foundation in understanding concept of Machine Learning.
  • Anyone who is looking or dont know from where to start Machine Learning, Deep Learning and Natural Language Processing can opt for this course.

What is this course about?

This course is designed to understand basic 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

- Machine Learning?

- Types of Machine Learning


Chapter - Data Preprocessing

- Null Values

- Correlated Feature check

- Data Molding

- Imputing

- Scaling

- Label Encoder

- On-Hot Encoder


Chapter - Supervised Learning: Regression

- Simple Linear Regression

- Minimizing Cost Function - Ordinary Least Square(OLS), Gradient Descent

- Assumptions of Linear Regression, Dummy Variable

- Multiple Linear Regression

- Regression Model Performance - R-Square

- Polynomial Linear Regression


Chapter - Supervised Learning: Classification

- Logistic Regression

- K-Nearest Neighbours

- Naive Bayes

- Saving and Loading ML Models

- Classification Model Performance - Confusion Matrix


Chapter: UnSupervised Learning: Clustering

- Partitionaing Algorithm: K-Means Algorithm, Random Initialization Trap, Elbow Method

- Hierarchical Clustering: Agglomerative, Dendogram

- Density Based Clustering: DBSCAN

- Measuring UnSupervised Clusters Performace - Silhouette Index


Chapter: UnSupervised Learning: Association Rule

- Apriori Algorthm

- Association Rule Mining


Chapter: Decision Tree

- Decision Tree Regression

-Decision Tree Classification


Chapter - Natural Language Processing

Below Text Preprocessing Techniques with python Code

- Tokenization, Stop Words Removal, N-Grams, Stemming, Word Sense Disambiguation

- Count Vectorizer, Tfidf Vectorizer. Hashing Vector

- Case Study - Spam Filter


Chapter - Deep Learning

- Artificial Neural Networks, Hidden Layer, Activation function

- Forward and Backward Propagation

- Implementing Gate in python using perceptron


Chapter: Regularization, Lasso Regression, Ridge Regression

- Overfitting, Underfitting

- Bias, Variance

- Regularization

- L1 & L2 Loss Function

- Lasso and Ridge Regression


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Sunday, July 26, 2020

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