# Fundamentals of Machine Learning [Hindi][Python]

Online Courses Udemy - Machine Learning, Fundamentals of Machine Learning [Hindi][Python]

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

Created by Rishi Bansal | English

Machine Learning and AI: Support Vector Machines in Python

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Machine Learning A-Z™: Hands-On Python & R In Data Science

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Data Science and Machine Learning Bootcamp with R

Machine Learning Practical: 6 Real-World Applications

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 to Deep Learning and 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

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 - Natural Language Processing: Various Text Preprocessing Techniques with python Code

Chapter - Deep Learning: Artificial Neural Networks, Implementing Gate in python using perceptron

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

Created by Rishi Bansal | English

**Students also bought**Machine Learning and AI: Support Vector Machines in Python

Data Science: Supervised Machine Learning in Python

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning, Data Science and Deep Learning with Python

Data Science and Machine Learning Bootcamp with R

Machine Learning Practical: 6 Real-World Applications

**Preview this course GET COUPON CODE**

**Description**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 to Deep Learning and 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

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 - Natural Language Processing: Various Text Preprocessing Techniques with python Code

Chapter - Deep Learning: Artificial Neural Networks, Implementing Gate in python using perceptron

Free Coupon Discount Udemy Courses