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

Online Courses Udemy - All-in-One:Machine Learning,DL,NLP,AWS Deply [Hindi][Python], Complete hands-on Machine Learning Course with Data Science, NLP, Deep Learning and Artificial Intelligence

Created by Rishi Bansal | English

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fundamentals-of-machine-learning-hindi

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.


As a Bonus Introduction Natural Language Processing and Deep Learning is included.

Below Topics are covered
Chapter - Introduction to Machine Learning
- Machine Learning?
- Types of Machine Learning

Chapter - Setup Environment
- Installing Anaconda, how to use Spyder and Jupiter Notebook
- Installing Libraries

Chapter - Creating Environment on cloud (AWS)
- Creating EC2, connecting to EC2
- Installing libraries, transferring files to EC2 instance, executing python scripts

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: Deploy Machine Learning Model using Flask
- Understanding the flow
- Serverside and Clientside coding, Setup Flask on AWS, sending request and getting response back from flask server

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)

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