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

All-in-One:Machine Learning,DL,NLP,AWS Deply [Hindi][Python], available at $79.99, has an average rating of 4.05, with 183 lectures, 5 quizzes, based on 384 reviews, and has 20762 subscribers.

You will learn about 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. This course is ideal for individuals who are Anyone who is looking or dont know from where to start Machine Learning, Deep Learning and Natural Language Processing can opt for this course. or This will provide a good foundation in understanding concept of Machine Learning. It is particularly useful for Anyone who is looking or dont know from where to start Machine Learning, Deep Learning and Natural Language Processing can opt for this course. or This will provide a good foundation in understanding concept of Machine Learning.

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

Summary

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

Price: $79.99

Average Rating: 4.05

Number of Lectures: 183

Number of Quizzes: 5

Number of Published Lectures: 178

Number of Published Quizzes: 5

Number of Curriculum Items: 193

Number of Published Curriculum Objects: 188

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

Anyone who is looking or dont know from where to start Machine Learning, Deep Learning and Natural Language Processing can opt for this course.

This will provide a good foundation in understanding concept of Machine Learning.

Target Audiences

Anyone who is looking or dont know from where to start Machine Learning, Deep Learning and Natural Language Processing can opt for this course.

This will provide a good foundation in understanding concept of Machine Learning.

This course is designed to cover maximum concepts of machine learning a-z. 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)

Course Curriculum

Chapter 1: Introduction to Machine Learning

Lecture 1: What is Machine Learning?

Lecture 2: Types of Machine Learning

Lecture 3: Supervised Learning

Chapter 2: Optional: Setup Environment

Lecture 1: Installing Anaconda

Lecture 2: How to Use Spyder Notebook

Lecture 3: How to use Jupiter Notebook

Lecture 4: Installing Library

Chapter 3: Optional: Setup Environment on cloud (AWS)

Lecture 1: Why AWS?

Lecture 2: Creating EC2 instance

Lecture 3: Connect to EC2 instance

Lecture 4: Installing Packages

Lecture 5: Transferring Files to AWS EC2 instance

Chapter 4: Data Preprocessing

Lecture 1: What is Data Preprocessing?

Lecture 2: Checking for Null Values: Concept + Python Code

Lecture 3: Correlated Feature Check: Concept + Python Code

Lecture 4: Data Molding(Encoding): Concept + Python Code

Lecture 5: Data Splitting

Lecture 6: Data Splitting : Python Code

Lecture 7: Impute Missing Values: Concept + Python Code

Lecture 8: Scaling

Lecture 9: Scaling: Python Code

Lecture 10: Label Encoder: Concept + Code

Lecture 11: One-Hot Encoder: Concept + Python Code

Chapter 5: Supervised Learning: Regression

Lecture 1: Simple Linear Regression: Concept

Lecture 2: Minimizing Cost Function

Lecture 3: Ordinary Least Square(OLS)

Lecture 4: Gradient Descent

Lecture 5: Measuring Regression Model Performance: R^2 (R – Square)

Lecture 6: Simple Linear Regression: Python Code -1

Lecture 7: Simple Linear Regression: Python Code -2

Lecture 8: Assumptions of Linear Regression

Lecture 9: Multiple Linear Regression: Concept

Lecture 10: Dummy Variable

Lecture 11: Multiple Linear Regression: Python – 1

Lecture 12: Multiple Linear Regression: Python – 2

Lecture 13: Multiple Linear Regression: Python – 3

Lecture 14: Polynomial Linear Regression: Concept

Lecture 15: Polynomial Linear Regression: Python – 1

Lecture 16: Polynomial Linear Regression: Python – 2

Lecture 17: Polynomial Linear Regression: Python – 3

Lecture 18: Polynomial Linear Regression: Python – 4

Lecture 19: Linear Regressions Comparisons

Lecture 20: Assignment: Predicting Housing Prices (Boston Data Solution): Optional

Chapter 6: Supervised Learning: Classification

Lecture 1: Logistic Regression

Lecture 2: Confusion Matrix: Measuring Performance of Classification Model

Lecture 3: Confusion Matrix: Case Study

Lecture 4: Logistic Regression: Python 1

Lecture 5: Logistic Regression: Python 2

Lecture 6: Logistic Regression: Python 3

Lecture 7: Logistic Regression: Python 4

Lecture 8: K – Nearest Neighbours Algorithm

Lecture 9: K – Nearest Neighbours: Python 1

Lecture 10: K – Nearest Neighbours: Python 2

Lecture 11: Naive Bayes

Lecture 12: Naive Bayes: Python Code

Lecture 13: Pickle File: Saving and Loading ML Models: Python

Lecture 14: Assignment 2: Predicting Wine Quality: Optional

Chapter 7: UnSupervised Learning: Clustering

Lecture 1: K-Means Algorithm

Lecture 2: Random Initialization Trap

Lecture 3: Elbow Method: Choosing optimum no of clusters

Lecture 4: K-Means++ : Python 1

Lecture 5: K-Means++ : Python 2

Lecture 6: K-Means++ : Python 3

Lecture 7: Hierarchical – Agglomerative Algorithm

Lecture 8: Agglomerative – Dendrogram

Lecture 9: Agglomerative – Python 1

Lecture 10: Agglomerative – Python 2

Lecture 11: Density Based Clustering – DBSCAN

Lecture 12: DBSCAN – Python 1

Lecture 13: DBSCAN – Python 2

Lecture 14: Measuring UnSupervised Clusters Performance

Lecture 15: Silhouette Index – Python 1

Chapter 8: UnSupervised Learning: Association Rule

Lecture 1: Apriori Algorithm

Lecture 2: Association Rule Mining

Lecture 3: Apriori Association: Python 1

Lecture 4: Apriori Association – Python 2

Lecture 5: Apriori Association- Python 3

Chapter 9: Deploy Machine Learning Model on AWS Using Flask

Lecture 1: Deploying ML on AWS – Concept

Lecture 2: Saving the ML Model

Lecture 3: Serverside – Python

Lecture 4: Clientside – Python

Lecture 5: Configuring and sending request

Chapter 10: Supervised Learning: Decision Tree and Support Vector Machines

Lecture 1: Decision Tree Regression – Concept 1

Instructors

Rishi Bansal
Senior Developer

Rating Distribution

1 stars: 14 votes

2 stars: 13 votes

3 stars: 65 votes

4 stars: 145 votes

5 stars: 147 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!