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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
(354 reviews)
20,698 students
Created by


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Platform: Udemy
Video: 17h 43m
Language: English
Next start: On Demand

Table of contents


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)

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.


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

This course is 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.
• This will provide a good foundation in understanding concept of Machine Learning.
Senior Developer
A total of 13 years of experience. I started my career as a programmer.  Apart from programming, I have worked on Cloud & Virtualization technology, DevOps and Machine Learning. Also, I have very good knowledge of software design methodologies, information systems architecture, object oriented design, and software design patterns. Teaching is my passion.  I hope you will enjoy my course.
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Platform: Udemy
Video: 17h 43m
Language: English
Next start: On Demand

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