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

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

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*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 *

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.
- Deploy your own model on AWS using Flask so that anyone can access it and get the prediction.
- 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.
- Regularisation and idea behind it. See it in action using Lasso and Ridge Regression.

Requirements

- For Machine Learning Concept no prerequisite. Anyone can do this course.
- Prior Understanding of Python is required.

Description

This course is designed to cover maximum concepts of machine learning. Anyone can opt for this course. No prior understanding of machine learning is required.

Bonus introductions include natural language processing and deep learning.

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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