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2021-01-22 13:34:16
30-Day Money-Back Guarantee

This course includes:

  • 17.5 hours on-demand video
  • 1 article
  • 59 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Assignments
IT & Software Other IT & Software Machine Learning

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
Rating: 4.5 out of 54.5 (296 ratings)
20,584 students
Created by Rishi Bansal
Last updated 9/2020
English
30-Day Money-Back Guarantee

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 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)



Who 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.

Course content

19 sections • 178 lectures • 17h 43m total length

  • Preview09:38
  • Preview06:46
  • Supervised Learning
    05:31
  • Quiz 1
    2 questions

  • Installing Anaconda
    05:18
  • How to Use Spyder Notebook
    05:59
  • How to use Jupiter Notebook
    07:37
  • Installing Library
    03:03

  • Why AWS?
    03:53
  • Creating EC2 instance
    08:54
  • Connect to EC2 instance
    05:13
  • Installing Packages
    02:26
  • Transferring Files to AWS EC2 instance
    05:27

  • What is Data Preprocessing?
    07:03
  • Checking for Null Values: Concept + Python Code
    07:45
  • Correlated Feature Check: Concept + Python Code
    09:28
  • Data Molding(Encoding): Concept + Python Code
    03:23
  • Data Splitting
    05:30
  • Data Splitting : Python Code
    09:46
  • Impute Missing Values: Concept + Python Code
    05:34
  • Scaling
    05:56
  • Scaling: Python Code
    06:15
  • Label Encoder: Concept + Code
    04:52
  • One-Hot Encoder: Concept + Python Code
    04:51
  • Data Preprocessing
    3 questions

  • Simple Linear Regression: Concept
    12:15
  • Minimizing Cost Function
    06:30
  • Ordinary Least Square(OLS)
    03:16
  • Gradient Descent
    20:17
  • Measuring Regression Model Performance: R^2 (R - Square)
    07:03
  • Simple Linear Regression: Python Code -1
    08:50
  • Simple Linear Regression: Python Code -2
    08:09
  • Assumptions of Linear Regression
    07:22
  • Multiple Linear Regression: Concept
    04:35
  • Dummy Variable
    06:27
  • Multiple Linear Regression: Python - 1
    11:16
  • Multiple Linear Regression: Python - 2
    07:58
  • Multiple Linear Regression: Python - 3
    10:05
  • Polynomial Linear Regression: Concept
    03:29
  • Polynomial Linear Regression: Python - 1
    06:41
  • Polynomial Linear Regression: Python - 2
    06:11
  • Polynomial Linear Regression: Python - 3
    08:07
  • Polynomial Linear Regression: Python - 4
    05:02
  • Linear Regressions Comparisons
    02:25
  • Simple Linear Regression: Quiz
    4 questions
  • Boston Housing Price Prediction
    1 question
  • Assignment: Predicting Housing Prices (Boston Data Solution): Optional
    09:54

  • Logistic Regression
    06:03
  • Confusion Matrix: Measuring Performance of Classification Model
    05:17
  • Confusion Matrix: Case Study
    05:50
  • Logistic Regression: Python 1
    07:32
  • Logistic Regression: Python 2
    09:36
  • Logistic Regression: Python 3
    07:53
  • Logistic Regression: Python 4
    06:14
  • K - Nearest Neighbours Algorithm
    04:44
  • K - Nearest Neighbours: Python 1
    07:27
  • K - Nearest Neighbours: Python 2
    06:15
  • Naive Bayes
    03:24
  • Naive Bayes: Python Code
    08:57
  • Pickle File: Saving and Loading ML Models: Python
    03:48
  • Wine Quality Prediction
    1 question
  • Assignment 2: Predicting Wine Quality: Optional
    08:30
  • Classify iris plants into three species
    1 question

  • K-Means Algorithm
    07:06
  • Random Initialization Trap
    03:49
  • Elbow Method: Choosing optimum no of clusters
    08:59
  • K-Means++ : Python 1
    09:39
  • K-Means++ : Python 2
    05:23
  • K-Means++ : Python 3
    09:13
  • Hierarchical - Agglomerative Algorithm
    02:47
  • Agglomerative - Dendrogram
    06:08
  • Agglomerative - Python 1
    06:15
  • Agglomerative - Python 2
    03:59
  • Density Based Clustering - DBSCAN
    03:26
  • DBSCAN - Python 1
    05:29
  • DBSCAN - Python 2
    05:12
  • Measuring UnSupervised Clusters Performance
    04:45
  • Silhouette Index - Python 1
    04:09
  • Find optimal no. of brands of car
    1 question

  • Apriori Algorithm
    07:16
  • Association Rule Mining
    11:03
  • Apriori Association: Python 1
    09:28
  • Apriori Association - Python 2
    06:37
  • Apriori Association- Python 3
    07:52

  • Deploying ML on AWS - Concept
    04:12
  • Saving the ML Model
    03:29
  • Serverside - Python
    08:04
  • Clientside - Python
    05:27
  • Configuring and sending request
    06:04

  • Decision Tree Regression - Concept 1
    04:05
  • Decision Tree Regression - Concept 2
    10:34
  • Decision Tree Regression - Python 1
    06:54
  • Decision Tree Regression - Python 2
    06:04
  • Decision Tree Classification - Concept 1
    08:27
  • Decision Tree Classification - Concept 2
    02:36
  • Decision Tree Classification - Python 1
    07:11
  • Decision Tree Classification - Python 2
    02:47
  • Support Vector Machines - Concept
    10:14
  • Support Vector Machines - Python 1
    07:34
  • Support Vector Machines - Python 2
    04:02
  • Kernel SVM
    05:35
  • Kernel SVM - Python 1
    04:35
  • Kernel SVM - Python 2
    02:34
  • Support Vector Regression - Concept
    02:36
  • Support Vector Regression - Python 1
    07:58
  • Support Vector Regression - Python 2
    06:33

Instructor

Rishi Bansal
Senior Developer
Rishi Bansal
  • 4.1 Instructor Rating
  • 493 Reviews
  • 31,782 Students
  • 4 Courses

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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