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

Free Coupon Discount - 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

English

Preview this Udemy Course - GET COUPON CODE

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

- 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: Non-Linear Supervised Algorithm: Decision Tree and Support Vector Machines

- Decision Tree Regression

- Decision Tree Classification

- Support Vector Machines(SVM) - Classification

- Kernel SVM, Soft Margin, Kernel Trick

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

Chapter: Dimensionality Reduction

- Feature Selection - Forward and Backward

- Feature Extraction - PCA, LDA

Chapter: Ensemble Methods: Bagging and Boosting

- Bagging - Random Forest (Regression and Classification)

- Boosting - Gradient Boosting (Regression and Classification)

100% Off Udemy Coupon . Free Udemy Courses . Online Classes

Share :